Culture Based Computing For Adult Learners (C-CAL) 
 
by 
 
Wanda Eugene 
 
 
 
 
A dissertation submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Doctor of Philosophy 
 
Auburn, Alabama 
August 06th, 2011 
 
 
 
 
Copyright 2011 by Wanda Eugene 
 
 
Approved by 
 
Juan E. Gilbert, Chair, Professor of Computer Science and Software Engineering 
Richard Chapman, Associate Professor of Computer Science and Software Engineering 
N.Hari Narayanan, Chair, Professor of Computer Science and Software Engineering  
ii 
Abstract 
 
 
This dissertation presents the design, implementation, and evaluation of the novel 
Cultural based Computing for Adult Learners (C-CAL) system, a self-directed online learning 
application utilizing culture to support adult learners in grasping computing concepts. C-CAL 
contains four major components ? Culture Inquiry Form, Cultural Data Mining, Links for 
Learning, and Learning Modules ? seeking to enhance the learning experience of non-traditional 
adult learners.  
Through a series of experiments, the C-CAL system has been found to be a viable 
alternative to introducing computing concepts to adult learners. As a result, using the general 
technology acceptance model (TAM), there was no statistical difference between the C-CAL 
system and traditional, non-culturally engaging methods currently used in regards to perceived 
ease of use and perceived usefulness. Thus indicating the C-CAL system performed just as well 
as traditional methods. However, C-CAL was consistently rated higher than the control on along 
all measures. Thus indicating the C-CAL system is a feasible system to utilize, whose benefits 
can manifest itself in the array of adult learners returning to education. This dissertation work 
demonstrates a deepening effort in applying culture norms and understanding to the design of 
computing technology, which will allow for more empowered uses of technology for learning 
and innovations among adult learners. 
iii 
Acknowledgements 
 
The author thanks the following: 
 
 
The Almighty Lord for providing patience, wisdom, knowledge, and for placing such 
amazing people in my path. 
I want to thank everyone that has supported and encouraged me during this process. I 
specifically would like to thank my thesis advisor Juan Gilbert for his patience in helping me to 
define the scope of this project, his mentorship and guidance.  I want to also thank my 
dissertation committee Richard Chapman, and Hari Narayanan for their support, insight, and 
expertise.  I want to also give thanks to my fellow Human Centered Computing Lab members 
for their words of encouragement.  
I would be remiss if I did not thank my extended family and friends the Bryants, the 
Mitchells, Shaundra Daily, Anderson Prewitt, Joaquin Gentry, Jean Accius, Petra Robinson, 
Christin Hamilton, Shanee Dawkins and the Mosaic Family Church for their encouragement 
and acts of kindness provided through out this process. My research pods, Brigid Barron and 
the LIFE Center team, and faculty supporters that have mentored me along the way for their 
direction, comments, and suggestions are very much appreciated. 
I would also like to thank my family, my parents, Madeline Eugene, and, Alicieus 
Eugene for their loving support, my siblings Viness and Amos for carrying me through my 
storms and the rest of my sisters, brothers, cousins, aunts and uncles for their continued support 
and belief in me and my ability to achieve this milestone.  I dedicate this work to the memory 
iv 
of my aunt Jeannette Joseph and my grandfather August Michel, who lived as examples that 
taught me how to deal with life?s challenges. Most of all, I want to give special thanks to God, 
for making this all possible. 
v 
Table of Contents
 
 
Abstract........................................................................................................................................ii 
Acknowledgements.....................................................................................................................iii 
List of Figures..............................................................................................................................x 
List of Tables.............................................................................................................................xii 
Introduction..................................................................................................................................1 
1.1 Motivation...........................................................................................................................1 
1.2 Development of the Culture Construction..........................................................................3 
1.3 Research Challenges...........................................................................................................5 
1.4 Research Question..............................................................................................................6 
1.5 Contributions of the Dissertation........................................................................................6 
1.6 Organization of Dissertation...............................................................................................7 
Literature Review: Design Considerations..................................................................................9 
2.1 Learning Consideration.......................................................................................................9 
2.1.1 Reaching Adult Learners..........................................................................................9 
2.1.2 Role of Computing..................................................................................................11 
2.2 Scaffolding Learning........................................................................................................16 
2.2.1 Culture and Learning..............................................................................................17 
2.3 Technology Design Consideration....................................................................................20 
vi 
2.3.1 e-Learning...............................................................................................................20 
2.3.2 Culturally-Relevant Design....................................................................................22 
System Design...........................................................................................................................24 
3.1 Component I: Culture Inquiry Form.................................................................................26 
3.1.1 Creating a Protocol.................................................................................................26 
3.1.2 Testing Protocol......................................................................................................28 
3.1.2.1   Settings..........................................................................................................28 
3.1.2.2   Procedure......................................................................................................28 
3.1.2.3   Participants....................................................................................................29 
3.1.2.4   Analysis.........................................................................................................30 
3.1.2.5   Results...........................................................................................................30 
3.1.3 Translating Protocol Online....................................................................................33 
3.1.3.1   Add Hobbies..................................................................................................34 
3.2 Component II: Culture Data Mining.................................................................................38 
3.3 Component III: Culture Data Mining...............................................................................42 
3.4 Creating Culture & Computing Dyads.............................................................................44 
3.5 Conclusion........................................................................................................................45 
System Implementation.............................................................................................................47 
4.1 Component IV: Learning Modules...................................................................................49 
4.1.2 Design and Development........................................................................................51 
vii 
4.2 Implementation and Evaluation........................................................................................58 
4.3 Conclusion........................................................................................................................59 
Experimental Design..................................................................................................................61 
5.2 Mining for culture (Component 2):...................................................................................64 
5.2.1 Hypothesis...............................................................................................................64 
5.2.2 Method....................................................................................................................64 
5.2.3 Procedure and Participants......................................................................................65 
5.2.4 Measure...................................................................................................................65 
5.2.5 Results and Analysis...............................................................................................65 
5.2.6 Conclusion..............................................................................................................72 
5.3 Culture Dyads (Component 3):.........................................................................................73 
5.3.1 Hypothesis...............................................................................................................73 
5.3.2 Method....................................................................................................................73 
5.3.3 Procedure and Participants......................................................................................73 
5.3.4 Measure...................................................................................................................75 
5.3.5 Results and Analysis...............................................................................................75 
5.3.6 Conclusion..............................................................................................................76 
5.4 Creating culture based learning modules (Component 4):...............................................77 
5.4.1 Hypothesis...............................................................................................................77 
5.4.2 Method....................................................................................................................77 
5.4.3 Procedure and Participants......................................................................................77 
viii 
5.4.4 Measure...................................................................................................................78 
5.4.5 Results and Analysis...............................................................................................78 
5.5 Enhancing the learning experience of adult learners when being introduced to computing  
concepts.............................................................................................................................80 
 
5.5.1 Data Collection.......................................................................................................80 
5.5.2 Method....................................................................................................................81 
5.5.3 Control Treatment...................................................................................................82 
5.5.4 System design to support system usability for adult learners.................................83 
5.5.4.1   Hypothesis.....................................................................................................83 
5.5.4.2   Method..........................................................................................................83 
5.5.4.3   Procedure & Participants..............................................................................84 
5.5.4.4   Measure.........................................................................................................85 
5.5.4.5   Results & Analysis........................................................................................85 
5.5.4.5.1     Perceived Usefulness.........................................................................85 
5.5.4.5.2     Perceived Ease of Use........................................................................92 
5.5.5  Demonstrating cognitive understanding.................................................................98 
5.5.5.1   Hypothesis.....................................................................................................98 
5.5.5.3   Procedure & Participants..............................................................................99 
5.5.5.4   Measure.........................................................................................................99 
5.5.5.5   Results & Analysis......................................................................................100 
Conclusion...............................................................................................................................109 
ix 
6.1 Summary.........................................................................................................................109 
6.2 Conclusion......................................................................................................................111 
6.3 Contributions..................................................................................................................112 
6.4 Future Research..............................................................................................................113 
6.5 Final Thought..................................................................................................................115 
References................................................................................................................................117 
 
x 
 List of Figures
 
 
Figure 1: System Architecture......................................................................................................26?
Figure 2: Sample Culture Inquiry Form........................................................................................34?
Figure 3: Add Your Hobby...........................................................................................................37?
Figure 4: Describe Your Hobby....................................................................................................38?
Figure 5: Defining Hobby.............................................................................................................48?
Figure 6: Hobbies in Context........................................................................................................49?
Figure 7: Introduction Page..........................................................................................................52?
Figure 8: Page 1, Control..............................................................................................................53?
Figure 9: Culture Based Example.................................................................................................54?
Figure 10: Concept Defined: Control...........................................................................................55?
Figure 11: Concept Defined-Experimental...................................................................................56?
Figure 12: More on Concept in Culture........................................................................................57?
Figure 13:Your Turn.....................................................................................................................58?
Figure 14: Learning Module Flowchart........................................................................................59?
Figure 15: Learning Module Sample Page 1................................................................................79?
Figure 16: Learning Module Sample Page 2................................................................................80?
Figure 17: PU K-S-Test Plot.........................................................................................................92?
Figure 18: PEOU K-S-Test Plot...................................................................................................98?
Figure 19: Objects K-S-Test Plot................................................................................................107?
xi 
Figure 20: Functions K-S-Test Plot............................................................................................108?
 
xii 
List of Tables
 
 
Table 1: Lee's Culture Data Set Example.....................................................................................18?
Table 2: Eglash African Fractals Example...................................................................................19?
Table 3: Focus Group Culture Examples......................................................................................32?
Table 4: Validation Study Demographic......................................................................................66?
Table 5: Hobby Bucket.................................................................................................................67?
Table 6: AQ Summary..................................................................................................................69?
Table 7:  Dominant Attribute........................................................................................................70?
Table 8: Cluster 5 Summary.........................................................................................................70?
Table 9: Cluster 8 Summary.........................................................................................................71?
Table 10: Cluster 1 Summary.......................................................................................................72?
Table 11: Sample of Correlated Culture and Concept..................................................................75?
Table 12: Full Study Demographics.............................................................................................81?
Table 13: Perceived Usefulness Experimental Results.................................................................88?
Table 14: Perceived Usefulness Control Results..........................................................................89?
Table 15: PU Analysis..................................................................................................................90?
Table 16: PU K-S-Test Results.....................................................................................................91?
Table 17: Perceived Ease of Use Experimental Results...............................................................94?
Table 18: Perceived Ease of Use Control Results........................................................................95?
Table 19: PEOU Analysis.............................................................................................................96?
 
xiii 
Table 20: PEOU K-S-Test Results...............................................................................................97?
Table 21: Results for "Your Turn" for the Functions Concept -Control Group.........................101?
Table 22: Results for "Your Turn" for the Functions Concept- Experiemental Group..............102?
Table 23: Results for "Your Turn" for the Objects Concept- Contorol Group...........................103?
Table 24:Results for "Your Turn" for the Objects Concept -Experimental Group....................104?
Table 25: Object Concept Analysis............................................................................................106?
Table 26: Functions Concept Analysis.......................................................................................106?
Table 27: Results for "Your Turn" K-S-Test Analysis...............................................................107?
 
 
 
1 
Chapter 1 
 
Introduction 
 
 
?The engine of our national economy, upon which our safety and security, our wellbeing, our 
quality of life, and our global competitiveness, indeed, our national preeminence depends, is 
powered by the technological and scientific discoveries and innovations? (Jackson, 2004).    
 
1.1 Motivation 
 
There is a rapid societal shift from a need for laborers to one for knowledge workers and 
a wide-ranging effort to strengthen scientific competitiveness (Gilbert & Eugene, 2007). There is 
now a need for digital fluency among those who were once common laborers.  Digital fluency, as 
defined by the National Research Council, is the ability to reformulate knowledge, to express 
oneself creatively; and appropriately, to synthesize new knowledge, and to produce and generate 
information rather than just comprehend it (NRC, 1999). 
Without supporting common laborers, ages 18-50, who are not usually targeted for 
computational learning resources outside of academic or job space, in developing digital fluency, 
the competitive edge will be lost and a rather large segment of our population will continue to be 
marginalized. The question becomes, ?How can our society be supported in gaining digital 
fluency?? A first step towards answering this question came after the term ?digital divide,? was 
coined during the mid 1990s. During this time, access to computational resources was thought to 
be the solution. There was little success, however, in integrating technology into the lives of 
 
2 
learners (Wolverton, 2010). Since then, education, rather than access alone, has become the push 
for supporting participation in the information society. 
As with all other educational concepts, there are numerous challenges that can hinder 
efforts to educate adult learners about computing. The obstacle most relevant to the current body 
of work is that of culture. Hughes postulated that all inventions are fashioned by individuals with 
a very specific educational and cultural background such that each part of an invention?s 
complex story involves processes that are highly contingent and highly intertwined with social, 
economic, and political relationships (Hughes, 1993). In other words, technology systems are 
imbued with the culture of the inventors. This idea can be extended to computing education. 
Computing education is socially produced, and social production is culturally informed. Thus, 
the culture of the producers of these educational practices shapes the ways in which they are 
realized (Castells, 2001). As a result, efforts towards computing education can be hindered for 
learners with limited or no participation in the producers? culture that is inherently built upon in 
the computing education practices.  
Conceptual understanding of computing differs between cultural groups with respect to 
logic, perceptions of time and space, society, values, problem-solving methods, or even 
determining which problems are considered legitimate (Tedre et al., 2003). Therefore, some 
learners must first come to understand this new culture prior to engaging in the learning 
experience, some will adapt regardless of the situation, some learners will adapt with difficulty, 
and some learners will not adapt at all. It is imperative that the cultural barriers to learning are 
addressed in order to support the development of digital fluency in adult learners. 
 
 
 
3 
1.2 Development of the Culture Construction 
Culture has been presented as both a challenge to learning, and although culture has made 
its way to the forefront of conversation, constantly being acknowledged as important, it still 
manages to elude specificity.  In every context of its use, culture takes on a different meaning or 
representation, fulfilling a different purpose or function. As culture influences action by shaping 
a repertoire or "toolkit" of habits, skills, and styles from which people construct ?strategies of 
action? (Swidler, 1986), it creates culture identities, which are understood within and between 
the culture participants. These toolkits depict the wealth of knowledge shared and understood 
within the culture. They also provide an alternative lens of understanding and interpreting data 
not already associated with one?s mental schema. 
Unfortunately, current methods of culture identification are limited and do not encompass 
this kind of multifaceted view of the learner. In previous research (Jensen, 1969; Betancourt & 
Lopez, 1993; Nasir & Hand, 2006), a participant?s culture was often defined by his gender and/or 
ethnic culture of participation, or the practices he partakes, in hopes that one would characterize 
the other. For example, an African American male that participates in the music culture of hip-
hop, would then be identified by one of the above cultures (being African American or hip-hop) 
regardless of his depth of participation or identification with those cultures. 
Though often used interchangeably, despite their vast differences, culture is not race but 
is informed by racial and ethnic categories (Nasir and Hand, 2006).  Race is pervasive and 
unchangeable yet culture is produced in cultural settings between people created in moments of 
culture activity in the context of institutions. The merging of the two constructs, the perspective 
of race as culture, which asserts that the characteristics and adaption of all people from a racial 
group are viewed as being homogenous (Nasir and Hand, 2006) is therefore unfounded.  
 
4 
The term culture will be used as one of the fundamental underpinnings of this body of 
work, and the foundation for the system design efforts. The use of culture throughout this 
research is theoretically based in sociocultural theory. As described by Nasir & Hand (2006), 
sociocultural perspective on culture as produced and reproduced in moments as people do in life, 
examine the roles of social and cultural processes as mediators of human activity and thought. 
The authors also present the four core aspects of sociocultural theory that form the foundation of 
the definition of and use of culture: 
1. Development at multiple levels of analysis 
2. Cultural practices as a unit of analysis  
3. Learning as a shift in social relations  
4. The meditation of artifacts and tools  
 In understanding this perspective of sociocultural theory, culture is grounded as being a 
system of meaning carried across generations, and constantly recreated in local context as one 
participates and reconstructs cultural practices. This perspective of the culture construct 
encompasses a dynamic view of culture as a holistic body that is constantly changing and one 
within which its participants interact. Building upon this dynamic characterization of culture, in 
this research culture is defined as ?who you are? and ?what you do.? Who you are? are things 
about about a person that are not easily changed, i.e. gender, ethnicity, age, height, weight, 
etc.?What you do? are things a person regularly practice and/or have meaning in their life, i.e. 
religion, political affiliation, music, sports, hobbies, etc.  
 
 
 
 
5 
1.3 Research Challenges 
 
Adult learners have been chosen since audiences aged 18-50 are normally not targeted for 
computational learning resources unless they have received higher education or some other job 
training (Seals et al., 2008). Adults in this age group that are not privy to higher education, or 
employment where such training is available can quickly become disenfranchised since they lack 
the computing knowledge that drives our society. There are certain challenges, however, to the 
development of a system to support these adults in understanding computing concepts such as 
barriers to learning with computational artifacts. 
Because computational artifacts are culturally mediated, they are inherently designed 
with the cultural prospective and understanding of their designers and builders (Eugene & 
Gilbert, 2008). This can prove problematic if the learner is not a participant in the designer?s 
culture. Before the learner can engage in learning, s/he must first come to understand the culture 
that the artifact is situated in, and then begin the learning process. All learners participate in a 
range of cultures and culture practices, derived from their life experiences in communities, 
industries, hobbies etc. Every artifact or practice that draws from a culture has rules of 
engagement. Delpit (2005) explains, that learner decides when and how to draw from their 
knowledge and experiences from within these various cultures but are lost without that choice. 
This creates an unnecessary barrier, making it difficult for new learners to engage.  A learner?s 
inability to connect to the culture that the artifact is situated in can create disjoint learning, 
making it difficult for a learner to connect this learning experience to their current mental model. 
This in turn produces a rote learner or a learner that learns without understanding of the 
reasoning or the relationships involved (Mayer, 1995), versus an adaptive learner or a learner 
with flexible knowledge that allows them to invent ways to solve familiar problems and 
 
6 
innovative skills to identify new problems (Brophy et al., 2004), because the learner does not 
embody a full understanding of the learned material. Though problematic with all learners, this 
can be detrimental to adult learners that often affiliate new learning as a challenge for the young 
to engage in, thus are apprehensive. Concern for the challenge that awaits, matched with barriers 
of learning computational artifacts that stems from an unfamiliar culture background these 
learners can find themselves at a disadvantage from the very beginning. 
1.4 Research Question 
 
In order to understand how culture participation can be captured, collected, and used to 
generate instruction through a computer-based system, the following research question will guide 
this research: 
Is culture based learning a feasible option to introduce adults to computing and does culture 
based learning enhance the learning experience for adult learners when being introduced to 
computing concepts? 
The exploration of this question gives rise to two sub-questions: 
1. Can culture of participation be identified and captured? 
2. Can culture data mining be used to create culture based learning modules? 
 
1.5 Contributions of the Dissertation 
 
The Cultural based Computing for Adult Learners (C-CAL) system in all its components 
fills a void in making the following contributions to the field of Human Centered Computing and 
Computer Science Education: a practical tool for identifying culture of participation for a group, 
modeling a process for the use of culture as a design construct and introduces a new system for 
increasing digital fluency for among Adult learners. 
 
7 
 
1.6 Organization of Dissertation 
 
Chapter 2 gives an overview of the areas of research that pertain to the development of C-
CAL. The research presented here is addressed in three parts: Learning Consideration, 
Scaffolding Learning and Technology Design Considerations. In learning consideration, a brief 
history of the plight of adult learners is discussed followed by a snapshot of the current issues 
with adult learners and computing.  Next, the theoretical basis established that supports culture 
and learning in Scaffolding Learning.  Finally, chapter 2 presents Technology Design 
considerations such as culturally relevant design, e-Learning cultural data mining and user 
interface design.  
Chapter 3 will discuss the system design in terms of system architecture. Here a thorough 
picture of the steps taken to design the C-CAL system. C-CAL is composed of four components 
all of which were designed and developed for this system. The first component is the Culture 
Inquiry Form, followed by the Culture Data Mining and then the Culture Dyads and finally the 
Learning Modules. The detailed design of the first three of the components will be discussed in 
chapter 3, including the design decisions made, the various factors used to influence those 
decisions, and final design that resulted.  
Chapter 4 will continue from chapter 3 with the implementation of the design strategies 
of chapter 3. The implementation of the C-CAL system is the materialization of the fourth and 
final component, the Learning Modules. Chapter 4 will demonstrate the transition of moving 
from the design realm to creating a user-friendly learning application. 
Chapter 5 presents the experiments, results, and analysis conducted on the C-CAL system 
in part and as a whole. Throughout the design and development of the C-CAL system the 
 
8 
components were vigorously analyzed and or tested. At the end, an experiment was conducted on 
the C-CAL system in its entirety.  Finally, Chapter 6 discusses the conclusions of the study, 
including limitations; summarizes main contributions that this work made; and points to some 
areas for future work. 
 
 
9 
Chapter 2 
 
Literature Review: Design Considerations
   
 
This chapter details the literature that informs an understanding of design considerations 
for Culture-based Computing for Adult Learners (C-CAL), a computer-based learner assistant, 
utilizing adult learners? culture-based knowledge to teach them computing concepts. These 
considerations are discussed two domains: learning and technology. Learning considerations 
include those related to reaching adult learners, teaching computing, and scaffolding learning 
through culture. Culturally relevant design and user interface design are considerations that 
inform the technological design of C-CAL. 
 
2.1 Learning Consideration 
 
2.1.1 Reaching Adult Learners 
 
One of the greatest challenges of education is the quest to understand if the learner is 
gaining insight into their own interpretation and framing the learning experience in the context of 
their learning. Beginning in the 1920s, the exploration of adults and learning was rooted in 
behavioral psychology and focused on the question can adults learn? By the 1950s, there was a 
shift away from this more behavioral view, to a more cognitive view of learning. At this time, 
however, adults were not differentiated from children.  Researchers in this time period found that 
 
10 
gaps in adult knowledge were actually functions of non-cognitive factors such as level of 
education training, health and speed of response (Merriam, 2001).  
These discoveries, helped researchers put to the rest the question of whether adults could 
learn and led to the development of an understanding of how adult learning differs from the way 
children learn. By the late 1960?s two theories of adult learners began to gain popularity, that still 
serve as the foundation to adult education today: 1) andragogy, defined as the art and science of 
helping adults learn, and 2) the theory of self-directed learning, centered on learners becoming 
increasingly self-directed as they mature.  Merriam points out that by the 1970?s and 1980?s both 
theories had gained criticism for not considering the social impediments of adult learners and 
ignoring social-historical context. This social-historical context is addressed by Guy (1999) who 
asserts that every aspect of adult life shaped by culture and education has served as a vehicle for 
defining the culture values that people hold or that they view as central to being successful in 
their society. As a result, ?the nature of the fit between learners? cultural backgrounds [emphasis 
added] and their educational experience is of central concern because of cultures? importance in 
establishing criteria for success or failure? (Guy, 1999, p. 13).   
In order to better understand the social impediments and social-historical context of adult 
learners, over time, distinctions are made among the different categories of adult learners, such 
as age and motivation, all of which factors into their learning needs.  
There are three generations of adult participations, the older age group of 45-50 years or 
more, the main and middle group from 25-30 up to 45 to 50 years, and the young adults from 18 
to 25-30 years (Illeris, 2003).  The older generation?s attitudes towards education are depicted by 
their identity as ?wage earner consciousness.?  The middle generation is also a carrier of the 
wage earner identity, but developed with it the material and collective value orientation toward 
 
11 
more immaterial and individualistic attitudes. The young generation?s central identity is 
influenced by the culture liberation.  These distinctions can provide insight regarding the 
possibility of defense strategies to learning that challenges their identity and personal way of 
thinking, reacting, and behaving.  
 In current research, the concept of the identity of adult learners within education has 
expanded to encompass non-traditional students, students other than young adults matriculating 
from high school into college, within informal learning environments, learning outside of the 
classroom. The term adult learners has evolved from adults that participated in learning as a 
voluntary activity, to adults that are there directly through necessity or persuasion either by 
employers/authorities, or indirectly, to avoid social and economic marginalization. All of which 
do not capture the various types of adult learners or identify a centralized motivating factor. 
Motivations are rarely straightforwardly, positive or negative, but seem to be a mixture of social, 
personal and/or technical elements with a focus on the concrete skills that the adults expect to 
gain (Illeris, 2003). Furthermore, researchers still agree that adult learners differ in motivation, 
limitations, and method of engaging in the learning process.  If this is true, how are these adult 
learners reached?  
2.1.2 Role of Computing 
 
With the massive return of adults to education, a buzz around the role of computing 
education has also begun to arise.  Many of these returning adult learners, are finding their 
newfound educational or career interests fully entrenched with some form of computing 
education.  In the last two decades, society has made an enormous investment in technology-
based infrastructures for continued education, which has resulted in technology-based education 
and technology-assisted education becoming high priority for almost all forms of education 
 
12 
(Nasseh, 1999).  In addition, most jobs today require employees to have the capacity to access, 
organize, and evaluate information using technology, ?whether the person works on a 
construction site, drives an 18-wheeler, or processes payroll at the local convenience store, 
technological skills can mean the difference between having a job and seeking one, especially for 
older workers? (Cordes, 2009, pg 1). This need has adult learners storming learning centers 
around the world to improve their skills and knowledge bases (Nasseh, 1999; AE Listserve, 
2008, Massy, 2005).   
Meeting the needs of the influx of adult learners requires distributed learning 
environments, and the ability to support new technologies and devices (Cordes, 2009). Although 
these adult learners are returning to education, many will not be in classrooms and are armed 
only with the limited technical knowledge they obtained from workplace or personal life. There 
will most likely be an inverse relationship between their age and socioeconomic status versus 
their technological experience and resources.  Thus, the older adults are, the less technological 
experience they are likely to possess. In addition, if these adult learners are of a lower 
socioeconomic status, there will be higher disparities in terms of available resources. Cordes 
(2009) asserts, factors such as formal computer training, practical experience, and the confidence 
gained from extensive use over time, that is often seen in younger learners that have grown up 
digitally, will play a critical role in their effectiveness in performing the needed learning tasks.   
These factors reflect much of the call to action that has been a stir in the computing 
education community for the last several years. All of which begs the question, how do we meet 
their needs?  In 1999, the National Research Council organized by the National Academy of 
Sciences produced ?Being Fluent with Information Technology? to set the standard for what 
everyone should know about information technology in order to use it effectively now and in the 
 
13 
future (National Research Council, 1999). In the report, they explained that computer literacy is 
too modest of a goal in the presence of rapid change because it lacks the necessary ?staying 
power.?  The committee called for learners to have fluency with information technology 
(FITness), which entails the ability to reformulate knowledge, to express oneself creatively; and 
appropriately, to synthesize new knowledge, and to produce and generate information rather than 
just comprehend it.  Further, fluency was defined as having three interrelated dimensions?
intellectual capabilities, conceptual knowledge, and an appropriate skill set. Thus, the committee 
established the need to have ?an adequate level of ?FITness? that provides an individual with the 
foundational knowledge and understanding that enables him or her to advance along a 
continuum, becoming more and more adept at applying information technology for a range of 
purposes and having a deeper understanding of technological opportunities for doing 
so?(National Research Council, 1999, pg 14). 
Over the years ?FITness? has had a range of interpretations of what its practical 
components should contain.  A long-standing question that has evolved from this, is what exactly 
is this need to know knowledge?  For example, is the goal to provide all adult learners an 
introduction to computer science education (often entailing some form of programming); or is 
the goal to effectively provide knowledge equivalent to that obtained through an information 
system-training program (focusing on the processes and management of information)? Efforts 
made to address the ?need? question often align themselves, knowingly or unknowingly, to one 
or the other side of this debate.  
One growing concept, energized by Jeanette Wing in 2006, is that of computational 
thinking, a fundamental skill she claims is as rudimentary as reading, writing and arithmetic 
(Wing, 2006).  Computational thinking involves solving problems, designing systems, and 
 
14 
understanding human behavior by drawing on the concepts fundamental to computer science. It 
encompasses six main characteristics: 1) Conceptualizing (thinking at multiple levels of 
abstraction); 2) a fundamental skill as oppose to a rote skill; 3) a model of human thinking; 4) a 
way of complementing and combining mathematical and engineering thinking; 5) based on ideas 
not artifacts and 6) it is for everyone everywhere.  
Further, Guzdial asserts the need for the computing profession to explicitly teach an 
expanded view of the ways of thinking about computing and making computational thinking 
easily accessible to a broader audience (Guzdial, 2008).  He discusses Miller and Pane?s study of 
how people specify processes in natural language that suggests that object-oriented thinking, in 
regards to a novice task description, is not natural for students.  Thus, there is a need to teach 
students computing in a way that makes sense to them (Guzdial, 2008).   
Though often seen as a ?noble goal,? computational thinking has been met with some 
debate as to the role of programming languages in this process.  One perspective on this debate, 
is that programming is to computer science what proof of construction is to mathematics, and 
what literary analysis is to English (Lu and Fletcher 2009). In other words, the introductory level 
should focus on vocabularies and symbols that can be used to annotate and describe computation 
and abstraction, suggest information and execution, and provide notation around which mental 
models of processes can be built. In another view, there is a call to move away from object- 
oriented languages all together with beginners, and to a more simplified languages until 
proficiency in design has been reached (Davies 2008).  Yet and still other debates have focused 
on the optimal choice for the more common languages in use today (McIver, 2002).  
A more recent argument gaining support focuses on ?how? computing is introduced. In 
understanding that the mental and material worlds are mainly connected through our acting, 
 
15 
perceiving, thinking and feeling; that is, through our activities, there must be a shift in focus on 
how to build that bridge between the mental and the material (Siefkes, 1997).  Thus, the debates 
on whether to focus on computing technology instructions, on computer science, on information 
science or even what programming language is optimal, becomes secondary to connecting with 
the target audience.  Siefkes suggests a shift of focus to how to better connect one?s mental 
model to the new material. 
Mechanical metaphors of thinking and learning such as:  
?Our mind is a container where knowledge is stored in various compartments; 
theories and curricula are buildings with foundations and supporting pillars; 
learning is putting ?stuff? into right places; teaching thus is making this stuff 
handy.? (Siekes, pg40, 1997) 
no longer suffice, according to Siekes (1997). In an effort to engage adult learners in digital 
learning with a goal of obtaining FITness, their learning is distinguished from children by 
acknowledging that a cognitive change is requested in how they view their world.  Siekes 
explains that if change is to last, it must affect and be connected to the people involved and 
spread to wider domains. He continues by establishing that our environments represent our 
activities, and that these activities are supported and dictated by our knowledge and valuation, 
which are formed by them simultaneously.  Thus, Siekes concludes, knowledge and valuations 
are linked by perception, action and communication that are through our mental, physical and 
social activities.  If matters such as logical thinking and problem solving are emphasized, and 
then resolve to connect the creative and innovative nature of computing to learners, FITness can 
be assured regardless of language or technology discipline used (Levy, 1995; Hu, 2003).    
 
 
16 
2.2 Scaffolding Learning 
 
This section expands upon the idea that there has to be alignment between a learner?s 
cultural background and his educational experience. Learning is a cultural activity. In order for it 
to be meaningful, it must be presented from a culture that is known to the learner. Evidence for 
this necessity can be drawn from Situated Learning Theory. A brief background is presented here 
to provide a basis for extrapolating an understanding of the impact of culture on learning. 
Situated Learning Theory examines knowledge acquisition through active participation in the 
social, environmental, and cultural merits of a situation (Lave and Wenger, 1990). Through 
legitimate peripheral participation, learners are afforded knowledge in the communities of 
practice that they participate in, which serve as tools to shape their understanding of the world 
around them (Driscoll, 2000). When a learner?s instructional knowledge is divorced from her 
communal configuration developed from within her community of practice, her ability to 
associate her instruction with her already learned knowledge is hindered. 
In the social context of an environment, learning can be manifested through the basic 
interaction of others, shared information, or even through abstractions from acuity.  Lave and 
Wenger (1990) expressed that learning is not only a result of organized education present in 
rigorous formats, but also of opportunities to be employed through practice.   
Situated learning is defined as a process of engagement in a community of practice (Smith, 
2003); a context of learning in an everyday practice that is stretched over, not divided among-
mind, body, activity, and culturally organized settings (McLellan, 1996); or simply a derivative 
of active cultural participation.  As a result, it is wondered can it serve as a solution to alleviate 
the disparities (Johnson & Kristsonis, 2006) in educating adult learners stemming from diverse 
backgrounds enriched with a lifetime of culture experiences?  If identifying the sociocultural 
 
17 
setting as the community of people and asserting that knowledge is a lived practice (Driscoll, 
1999), then the learning of its participants is impacted and formulated about the activities of this 
community of practice (Lave and Wenger, 1990). Learning is then a tool resulting within a 
community of practice by which it loses some of its validity if separated from its culture.   
2.2.1 Culture and Learning 
 
Situated learning must involve activity, concept and culture; comprehension of one will 
not abide with the other two, because they are interdependent (McLellan, 1996).  Therefore, if 
knowledge can be seen as a dynamic toolkit cultivated through experience (Orellana & Bowman, 
2003), tools and cultural skills in these toolkits can be identified by means of certain 
characteristics.  An understanding of these sets of tools is obtained through first accepting the 
belief system of the culture to which the tool belongs, and then through use (McLellan, 1996). In 
the same fashion, others may not necessarily understand the community-learning tool of 
members of a culture without the acceptance of the functioning of that tool with respect to the 
culture.  
There are several examples in research, and other disciplines and domains of various 
efforts of using culture attributes, practices and experiences to facilitate learning and to connect 
to the mental model of the target audience. Numerous tools and agents have been developed in 
response to the understanding of the importance of culturally relevant learning. For example, Say 
Say oh Playmate is a program that builds on students prior knowledge, lyrics from popular 
songs, and incorporated music to motivate reading and to support beginning literacy skills 
(Pinkard, 2001).  Also African-American Distributed Multiple Learning Styles System 
(AADMLSS) City Stroll is an interactive game-like environment that uses culturally relevant 
cues, gestures, sounds lyrics and animation to identify with the culture of middle school and high 
 
18 
school African-American students from the inner city to teach them algebra. (Gilbert et al., 
2008).  MindRap is another tool that harnesses the power of culture and the students? creativity 
to energize the learning process and encourage an interest in math and science by combining 
interactive teaching applications with hip-hop music and culture (Gilbert et al., 2008).  Another 
example, Lee uses culture modeling to teach literature. Culture modeling in essence provides 
instructional organization that makes academic concepts, strategies, and habits explicit and 
provides ways of engaging in the work of the disciplines familiar and that provides supports for 
instances where the learner is unsure (Lee, 2007).  She uses the culture of everyday practices as a 
lens for understanding the role of perception in influencing actions.  Within culture modeling, 
culture data sets are used, which are familiar examples that new learning can be anchored and 
used to provide problems whose solutions mirror the demands of the academic task that the 
learner is to discover (Lee, 2007).  Similarly, Eglash investigates fractal geometry as in 
geometric patterns, calculations and theories, as facets expressed in various African cultures 
(Eglash, 1999). Making connections across relevant schemata or clusters of schematic networks 
helps to create connections between the known and the unknown. 
Table 1 shows how Lee engages inner city students in conversations about symbolism by 
discussing well-known rap lyrics and drawing parallels to similar ideas in literature.   
Table 1: Lee's Culture Data Set Example 
Culture data sets Literature 
Rap lyrics ?The Mask? by the Fuges 196 
Students analyze the meaning of the Mask and terms 
in the Mask like ?why Golden Child was not symbol 
but figurative language such as saying ?you star 
bright is a another way of saying you light-skined?? 
Symbolism 
Rap video ?I use to Love Her? by Comon Sense 
1994 
Symbolism 
Short story ?Everyday Use? by Alice Walker 194 Symbolism 
Novel ?Beloved? by Toni Morison 1987 Symbolism 
 
19 
 
Similarly, Table 2 below shows the connection made by Eglash in identifying 
illustrations of fractal geometry in indigenous African architecture designs, decorative arts, 
ceremonies and customs. Much like the models created by Eglash and Lee, this research 
demonstrates a similar model.  
 
Table 2: Eglash African Fractals Example 
Culture Fractals 
Kinship and descent Recursion 
Divination Binary codes=> numeric systems 
African windscren (the Sahel have strong wind and 
dust. The shortest rows kep the dust out the best 
because they are the tightest weaved, but also require 
more material and efort. They know that wind blows 
stronger when you go up from the ground, so they 
make the windscren to match ) 
Scaling 
African windscren: Maximum in function (keping 
dust out) for a minimum of cost (efort and materials) 
Cost benefit analysis 
 
Finally, all knowledge is not obtained via situated cognition. Knowledge can be acquired 
through formal and informal ways. The interaction of the types of knowledge is beneficial when 
the strengths of both forms of knowledge are used (Eales, 1997). Given that full understanding of 
the attained knowledge has occurred, then knowledge learned through formal means can be 
applied in situations dealing with informal contexts. Situated learning does play a vital role in the 
development of learners. Because the nature of learners is to participate in various communities 
of practice, it is natural that the learner?s development in one community of practice can come as 
a result of her participation in another.  
Regardless of how the knowledge is acquired, it must prove to be meaningful to the 
learner. Ausubel?s Meaningful Learning Theory depicts meaningful learning as a process of 
 
20 
relating potentially meaningful information to what the learner already knows in a non-arbitrary 
and substantive way (Driscoll, 1999). In other words, if a learner has experiences that are unique 
to her/his identity, then meaningful learning occurs when s/he is able to relate new potentially 
meaningful information.  Situating a learning experience within a culture where a learner is an 
active participant is an example of how to make it meaningful. 
As a caveat to the dialogue of culture and learning, it is presented with a caution on 
knowledge transfer. Saxe?s (1988) study on the math of street vendor children, displayed the 
perplexing scenario of their cultural knowledge of mathematics not manifesting itself in tasks 
that required their understanding of the conventional rules for composing digits into multi-digit 
values, even though they used appropriate linguistic descriptors in their everyday activities. 
Gasson (1999) explains this phenomenon in that individual knowledge is therefore based in the 
context of action and transferable between tasks only when concepts may be generalized and 
made apparent from experience gained through previous learning activities.  She reveals, the 
transfer of knowledge therefore becomes possible only when sociocultural practices can be 
translated to a new context of social action. 
 
2.3 Technology Design Consideration 
 
2.3.1 e-Learning 
 
In understanding that the majority of adult learners that will be engaging in the learning 
of computing technology via computer technology, care must be taken in how the experience is 
structured. In some sense, for many of these learners it will be like submersing oneself in a 
foreign country with limited, to no knowledge of their language and expecting to not only 
survive, but to thrive. Though, as difficult as the challenge may sound, it is possible, and less 
 
21 
painful, with the right tools.  Thus, C-CAL will be designed as an e-Learning tool.  For the 
purpose of this study, the term e-Learning is an umbrella term for online learning, distance 
learning, web-based training, computer based-learning, etc., in which learning and training are 
facilitated through both computer and communication technology. One of the greatest benefits of 
e-Learning is its ability to overcome the various boundary conditions such as space and time, in 
which knowledge can be imparted to a learner.  e-Learning techniques can be divided into four 
categories:  
? Knowledge databases provide index explanation and guidance for software questions, 
along with step-by-step instructions for performing a specific task as often seen in 
popular help menus.  
? Online support such as forums, chat rooms, online bulletin boards, email, or live instant 
messaging support.  
? Asynchronous training, self pace learning that is CD-ROM-based, Network-based, 
Intranet/Internet-based, allowing learners to participate according to their schedule and be 
geographically separated from the instructor. 
? Synchronous training happens in real-time using various e-Learning technologies and 
occurs much like a traditional classroom session (Obringer, 2006).  
E-Learning involves the use of a number of technological tools that can be applied in 
various contexts; it is not a distinctive education system in itself (Nichols 2003). Though many 
are the opportunities that e-Learning affords, the fact still remains that e-Learning doesn?t change 
anything about how human beings learn.  e-Learning is one of the youngest learning tools as far 
as research is concerned. The bulk of literature in e-Learning is practice-based and is typically 
presented in a descriptive format (Nichols, 2003). Thus, much of e-Learning has been conducted 
 
22 
on a trial and error bases. The rapid growth of e-Learning technologies and the desire to see these 
technologies integrated in the learning process has caused a lapse in verifying its validity. The 
true educational potential of these tools may never be realized until developers see links between 
established theoretical prospective on learning and useful applied techniques in e-Learning 
course design, as well as find ways to improve the dissemination of research for more specified 
guidance  (Bannan-Ritland, Bragg & Collins, 2004).  For the true value of e-Learning to be 
reached, those presenting it must look past the novelty of the technology delivery formats and 
apply what they know about teaching and learning to the creation of these environments basing 
their decisions on sound pedagogical constructs relevant to their own domains (Bannan-Ritland, 
Bragg & Collins, 2004).  As a result, C-CAL will be a learning tool that utilizes the e-Learning 
platform as a vehicle to reach a broader audience, yet rooted in learning theories such as Situated 
Learning and Meaningful Learning Theory. 
 
2.3.2 Culturally-Relevant Design 
 
Understanding the needs of adult learners, and the challenges of getting these learners to 
the level of fluency in information technology as described by the National Academies of 
Science, can culture be used as an ethnic-social construct to bridge the world of adult learners 
and the learning of computing technology? 
In order embark on such an effort it would be wise to heed to the lessons learned how to 
present culture based learning. Classroom based teaching, is one area where culturally relevant 
learning has been explored extensively. Teaching in a culturally relevant manner requires that 
educators of adults examine the learning environment for communicative processes, instructional 
practices, classroom customs and expectations, learning evaluation criteria, and instructional 
content that are potentially culturally incompatible. Guy (1999) explores a model for teaching in 
 
23 
a culturally relevant way that is compatible with the learners. The model explores four elements 
of culture, (1) the instructor?s cultural identity, (2) the learner?s cultural identity, (3) the 
curriculum, and (4) instructional methods and processes. This model can become difficult to 
implement in practice because of the daunting task of customizing the learning experience for 
each student. The four elements of culture become the bases of forming design consideration for 
culturally relevant design. 
 
 
 
 
 
 
 
 
 
 
 
 
24 
Chapter 3 
 
System Design 
 
 
Marie is a 28-year-old woman of an ethnic minority and living in the southeast region of 
the United States. She graduated from high school about 11 years ago and has been working a 
series of odd jobs as she pursues her acting career. She has been acting for about 14 years, 
appearing in at least two productions a year. Marie also loves to cook, coming up with a range 
of recipes and is trained in martial arts. Marie wants to find a way to improve her earning 
capacity until she gets her big break. Marie has rich cultural knowledge that can be used to 
enhance her learning experience. Marie sits down to C-CAL, and in the Culture Inquiry Form, 
enters the cultures in which she participates. Once her Culture Inquiry Form is submitted, it is 
clustered to determine the dominant culture with respect to others in the database. Once the 
dominant culture in which she participates is determined, in this case theatre, it is then manually 
linked to a computing concept; variables in this case.  
Marie is presented with the idea that a performer can be considered a variable. In linking 
variables to the culture attributes of theatre, we will focus on the common trait of a symbolic 
representation. In theatre, symbolic representation can be likened to that of a performer. For 
example, in the production of Shakespeare?s Romeo and Juliet the role of the female protagonist 
character Juliet, has been played by a variety of performers all representing the same persona. 
However, as long as the rules were followed for example the experiences of the character, it is 
understood what symbolic representation the performer is portraying.  When performing, every 
 
25 
thought, action and reaction is performed in character to denote or convey the character. Hence, 
the actress becomes a symbolic representation of a persona or character. Much like a variable 
labeled "int i," it doesn't matter which integer is placed in the variable i (high/low or 
positive/negative), as long as it is reflective of the symbol integer. Therefore, regardless what 
quantity or expression is given, the variable must stay in character. 
This scenario describes the intended use of Culture based Computing for Adult Learners 
(C-CAL); supporting adult learners in understanding computing concepts through culture-based 
instruction. This chapter will describe the components of the (C-CAL) system that 
operationalized the design considerations discussed in the previous chapter. C-CAL contains four 
major components ? Culture Inquiry Form, Cultural Data Mining, Culture Dyads, and Learning 
Modules (Figure 1) ? each of which was designed, implemented, and evaluated as a part of this 
research. The C-CAL system was designed using an iterative design methodology. For each 
component an initial design was composed, then presented to several test users. Based upon the 
feedback received from the test users the design was refined to account for any shortcomings in 
the design. The process was repeated until user issues have been addressed at an acceptable level 
(Nielsen, 1993). The sections below discuss the design rationale for these components as well as 
research conducted to inform their implementation. This will serve as the system design road 
map. A discussion of the implementation of the entire C-CAL system will be presented in 
Chapter 4. 
 
 
26 
 
Figure 1: System Architecture 
 
3.1 Component I: Culture Inquiry Form 
 
The goal of Component I of the system was to answer the question, can one?s culture of 
participation be identified and captured? The Culture Inquiry Form (CIF) allows the learners to 
self identify with the culture(s) in which they participate. The CIF collects culture participation 
information based on ?who you are? and ?what you do? (Gilbert and Eugene, 2009). In order to 
formulate a design centered about the target audience, the design of the first component required 
input from the target audience regarding their culture of participation.  Cultures of participation 
are best understood and explained by their participants. Thus the design, set out to determine the 
adaptive responses and/or activities that reflect a person?s knowledge base as a participant of that 
cultural practice. The researcher hypothesized that by asking adults about their shared 
experiences regarding their participation within a community of practice they will reveal both 
cultural attributes that will provide insight into how they identify themselves as participants of 
these cultures. The design of Component I consisted of: 1) creation of the Culture Participation 
Focus Group Protocol; 2) an initial test the protocol; and 3) translation of the protocol into an 
online space.  
 
3.1.1 Creating a Protocol 
 
27 
 
The initial focus group protocol, the Culture Participation Focus Group Protocol 
(CPFGP), was designed (see Appendix A) by adapting the Family Math Protocol created by the 
Family Math Project (Martin et al., 2009). The Family Math Project and team are affiliates of 
Stanford University School of Education and the Learning Informal and Formal Environments 
(LIFE) Center.  The project?s goals are to identify contexts, activities, and resources involved in 
learning and using mathematics in families where they seek out and design for points of synergy 
between math in the home, and math and school. Using the protocol, the Family Math team 
conducts two-hour interviews in the family homes where they investigate the family as a cultural 
context for learning and doing math. The questions are focused on everyday activities and 
designed to solicit narratives. Although the Family Math Protocol?s primary purpose related to 
demonstrations of math in everyday activities, its ability to capture a groups? depiction of their 
everyday activities and solicit narratives provides an avenue for understanding a group?s culture 
of participation.  
The first portion of the CPFGP covers context and activities, where questions centered on 
generating discussion on the various activities, people experience in different places and times. 
The questions in this portion of the protocol stemmed from topics such as home design and 
improvement; hobbies and collections; favorite or leisure activities; cooking, shopping, work and 
travel.  Next, the CPFGP investigates problem-solving strategies, and a deeper exploration of 
preferred activities is conducted by investigating how various activities are done.  In keeping in 
the customs of the Family Math Protocol, the CPFGP also seeks computing in a minute stories, 
where everyday experiences imitate experiences encountered in computing are shared. The 
protocol concludes with a final question of, ?what is computing to you,? in an effort to provide a 
 
28 
chance to explore any uncovered arenas. The CPFGP was used to conduct the focus groups 
discussed below. 
 
3.1.2 Testing Protocol 
 
The focus group method was selected because of some of its immediate benefits, such as 
the group effect (Lindlof & Taylor, 2002). It provides an interactive group setting, where 
participants are free to talk to one another. As participants listen to others? verbalized 
experiences it stimulates memories, ideas, and experiences of their own.  As such, group 
members begin to communicate in a common language to describe similar experience, allowing 
the capture of a common shared ontology. The focus group provided a means of understanding 
the cultures of participation of the targeted audience and their ontology for characterizing their 
participation in these cultures.  
 
3.1.2.1 Settings   
The focus groups were conducted in collaboration with the Information Management and 
System Engineering (IMSE) Program in Detroit, Michigan. The IMSE program, under Wayne 
State University?s National Science Foundation Broadening Participation in Computing Project, 
is a collaboration of Wayne State, Focus Hope, and several industry partners to support 
disadvantage students at critical junctures from a GED through the completion of a post 
secondary degree (Brockmeyer, 2007).  
 
3.1.2.2 Procedure 
In preparation for the focus groups, there was some concern regarding the length of the 
protocol and the time allotment for the focus groups. The flow of a focus group or interview 
 
29 
depends heavily upon the engagement of the group. A highly engaged group can produce 
extensive discussion in which rich data can be obtained, however this can limit how many 
protocol questions can be covered in a session. As a result, in order to diminish the length of the 
protocol and provide adequate time for the researcher to dig deeper into questions, ten of the 
focus group questions and demographic questions were sent out to participants a week prior to 
the study in the form of a survey (see Appendix B.). The first three questions consisted of 
demographic questions such as gender, ethnicity and age. The remaining questions were related 
to industry experience and technology usage. The questionnaire provided a foundation to tailor 
the focus group discussions.   
As prescribed by the protocol, on the first day the instructional sheets are provided then 
each focus group began with a round of introductions and explanation of the study to the 
participants to establish rapport. The audio recorder was started as participants then engage in a 
brief icebreaker, in which they talk about themselves, successes, and challenges. In the beginning 
of the focus group, each question was presented to participants in a round robin fashion. As 
participant?s comfort level began to rise, as a new question was introduced, participants began to 
chime in at will. The researcher made note of each speaker to ensure that every participant had a 
chance to respond to a question before moving on to the next question. On the last day, the 
participants were thanked for their time, and asked if they have any questions before completing 
the focus groups.  
 
3.1.2.3 Participants 
Participants of Focus: HOPE cohort 3, which was the current group of students in the 
program, was invited to participate in the focus groups. Eleven participants were divided into 
two groups (n = 4 in Group 1 and n = 7 in Group 2). Focus groups were conducted over two 
 
30 
days, at the Focus: HOPE facility in Detroit, MI. Each session, with each group, lasted 1-1.5 
hours. The study took place during the participants? math class time on day one and computing 
class time on day two, thus the groups where formed based upon the participants math class 
affiliations and time of arrival on the day of the study. Each participant was compensated with a 
$25 credit to their IMSE co-payment for each day they participated. 
According to the pre-survey, the focus group consisted of 81% African American?s, 55% 
male, 44.4% female, and average age ranged from 45 to 54. More than half the participants 
considered themselves as novice computer users, yet admitted to using a computer daily. In the 
pre-survey, participants mentioned several industries in which they have been previously 
employed. This allowed the researcher to conduct a cursory overview of the participants? past 
industries of employment prior the day of the focus groups to support additional probing during 
the course of the focus group.   
 
3.1.2.4 Analysis 
Data gathered from the focus groups were manually analyzed by the researcher in search 
for common themes that emerged from the respondents. Data analysis codes were not developed 
prior to data analysis to prevent bias. Thus initial analyses were conducted more in terms of a 
review of which questions produced more dialogue among the group. And then followed by 
determining which questions caused participants to provided a more deep reflection or in-depth 
explanation of cultural activities.  
 
3.1.2.5 Results 
Table 3: Focus Group Culture Examples depicts a sample of the participants? responses 
shared in the focus groups as it relates to their sections for the categories of hobbies and jobs. In 
 
31 
the participants? responses, there were several overlaps in hobbies, jobs, and traditions.  For 
example, though cooking is listed in Table 3 as an example of a hobby, several other participants 
discussed it in reference to family traditions or industries in which they have been previously 
employed. Their participation in cooking ranged from a daily activity, to one that is done only 
with family on given holidays. Participants also discussed their process and preparation patterns 
for engaging in their hobby. For example, one participant, reinforced by other participants, 
discussed the concept and strategy to playing the game of Bones also known as Dominoes; while 
another spoke at length about preparation for a seasonal fishing excursion. 
The majority of participants possessed an extensive job history ranging across several 
industries. On average, each participant worked a minimum of three jobs over a three-year 
period. Participants discussed several of their past work experiences, their positions and some of 
the training or knowledge they considered essential to the job.  One participant, who supervised a 
team of teens conducting road clean-up on the freeway, focused on the safety precautions that he 
enforced. Another stressed the importance of angles in power washing semi-trucks. Yet another 
depicted the process of delivering trash cans and recycle bins and the various factors that come 
into play.  
In addition, one of the most valuable discoveries of the focus groups was the participant?s 
ontology. This provided the researcher with a roadmap of how to capture culture in future 
development, of the C-CAL system. Also gauged was which questions on the protocol 
participants had little or no response to. For example, questions about home design and 
improvement, and communications structures and patterns didn?t provide much insight within 
the focus groups. Thus, these questions were not included in the design of the final Culture 
Inquiry Form. 
 
32 
 
 
Table 3: Focus Group Culture Examples 
Coking Fishing Dominoes/Bones 
Hobies 
Frequency: ranges from 
once/twice a wek-
daily; family tradition 
Proces 
 Culture the fod; 
creating new recipes?; 
 Coks for family size 
 How 2 cok for large 
groups 
 Preparation entails 
having resources space, 
time. 
 Soe meals require 
advance preparation to 
be done days in 
advance 
 Preparation Example: 
 go to store, get 
ingredients, organizing 
ingredients; Layout al 
other neded suplies 
 timeframe where you 
would not be disturbed 
 put everything 
together 
How to prepare: 
 Know the season to catch 
the best fish 
 Devise a plan (Plan when 
you go, who going with, 
where you going), get there 
early to get the best spots, 
pack some lunch and a 
coler?uch like planing 
an event 
How to prepare: 
 Play for 5 pts or 10 
pts 
 develop a game 
strategy, for 
example: watch 
what hasn't ben 
played 
 Goal is the person 
behind you doesn?t 
score 
 Be prepared to 
sacrifice your hand 
to kep block 
oponent 
Road Clean-up Power Wash Trash Colector 
Jobs 
Duration: 3 yrs 
Position: supervise 
tens 
Training Required:  
 Adapt to diferent 
Personalities build god 
relationship with 
workers 
 Safety Precautions: 
must walk precisely 150 
ahead of the vehicle 
and 150 fet back 
towards the vehicle; 
beware of your 
suroundings; beware 
of oncoming trafic 
Duration: 10 yrs 
Position: Power-wash semi-
trucks 
Training Required: 
o Learn how work the 
machines 
o Technique to washing the 
trucks to the satisfaction of 
the owner (angles, and 
spots: example= loking at 
under the tire wel/rim 
normaly not an area 
cleaned in normal car 
washes?so technique 
entails geting al the spots) 
Duration: 5 yrs 
 Deliver 
Trash/recycle cans 
to residential areas 
 Proces: 2-3 people 
runing and 
droping down the 
cans then another 
came by and 
scaned (the can?s 
barcode) 
 safety precautions: 
Beware of trafic 
 Weather efect on 
work conditions 
 
 
33 
 
3.1.3 Translating Protocol Online 
 
The CPFGP and the lessons learned from its use in the focus groups, served as the bases 
for the creation of the online version of Culture Inquiry Form. The first part, or the ?who you 
are? portion, includes the demographics section of the instrument, which was designed to 
correlate the data collecting techniques of the U.S. Census Bureau and the Department of Labor, 
giving a consistent means of measurement. The US Census Bureau demographic categories serve 
as a model for this study, entailing questions such as age, ethnicity, and gender (Figure 2). The 
second part of the CIF allowed for the capture of the ?what you do? part of the learners? culture 
of participation. The design of the second part of CIF stems from the Culture Participation Focus 
Group Protocol (CPFGP) and the collected data. Thus, the second part of the CIF contains 
selections pertaining to hobbies, employment, and traditions. All of the questions in CIF were 
designed as radio buttons or check boxes except for hobbies and traditions. The hobbies question 
presented the participant with a drop-down list of a several hobbies. These hobbies were 
gathered from the focus group study.  If the learner is unable to identify with the listed hobbies 
another avenue is provided for learners to enter in the cultural hobbies in which they participate. 
Participant?s entries are then added to the list, such that later participants will see it when the CIF 
is loaded again. This will be discussed further below. Traditions were also designed as a text box 
so the participants? could enter their traditions and describe them accordingly.  A sample portion 
of the CIF is displayed in Figure 2 and the CIF in its entirety can be found in Appendix C. Also 
included were questions pertaining to computer usage and perceived level of computer 
experience. 
 
 
34 
 
 
Figure 2: Sample Culture Inquiry Form 
3.1.3.1 Add Hobbies 
Because the hobbies originally presented in the drop-down list is not a complete list, and 
mainly meant to help participants reflect on what some of their hobbies could be, an add hobby 
field is also included. The hobbies field was thought to be a key field because it encompassed an 
array of cultural practices. In the focus groups conducted, participants provided vivid 
explanations of their hobbies, how they themselves participated in these hobbies, and how they 
interacted and identified with others that had shared experiences in these activities.  For example, 
two participants talked extensively about their hobby of cooking. One person explained a 
detailed process of preparation, recipes, ingredients, supplies and layout of the kitchen. Another 
participant discussed cooking in regard to it being a shared family holiday activity thus focusing 
more on the people that were was involved.  Several of the hobbies mentioned in the focus group 
 
35 
were basic and general enough where other adult learners, including those in the focus group, 
could identify such as baking, golfing, watching TV, etc. The researcher decided, then, to make 
use of this rich information. Thus, the hobby field was designed as a drop-down menu and 
hobbies shared during the course of the focus group by the participants were then added as the 
initial options to choose from a drop down menu.   
During the focus group, it was discovered that some participants needed seeds, or ideas, 
to help them brainstorm what their hobbies might entail.  The majority of participants initially 
responded as having no hobbies, yet when an example was given or other participants began 
describing their hobbies, it would spark a memory or an idea for the other participants of 
activities they frequently engaged in but seldom refer to as their hobbies.  Assuming the same 
would be true for other participants who would later complete the CIF in a virtual space, some of 
the more common responses received in the focus group were placed in the drop down menu to 
serve a similar purpose of sparking ideas and memories as the examples did for the focus group 
participants.   
The next goal was to devise a way to capture the same rich information about knowledge 
and participation of their hobbies, in a systematic way.  It is key to capture a participant?s 
ontology of their hobby more than an understanding of what they identify or label that hobby to 
be.  As previously discussed, participants in a culture have a shared ontology. The ontology, or 
language, of a culture is one factor that helps us distinguish between our various cultures of 
participation. It is also where a better understanding, of what that culture entails is gained. For 
example, if I said that one of my cultures of participation was Kabaddi, it would be useless 
information to another person, system, etc. However, if I then described it as an activity having 
the objective of to tag as many opponents as possible before returning to the home half of the 
 
36 
field, you can begin to better identify ways that this person?s ontology and description of their 
culture of participation can be likened to a task, process, or skill set to which they can be 
connected.   To do so, the researcher tapped into the Common Sense Computing Initiative 
(http://xnet.media.mit.edu/, 1999). 
The Open Mind Common Sense project, a product of the Common Sense Computing 
Initiative, has developed the machine-interpretable semantic network, ConceptNet 3.  
ConceptNet 3 consists of sets of semantic relations (e.g. EffectOf, DesireOf, CapableOf), known 
as binary predicates, which serve as the edges within a semantic network between compound 
concepts (e.g. ?buy food?, ?drive car?) that are the nodes (Liu & Singh, 2004; Havasi et al, 2007).  
According to Speer (2007), predicates are assertions depicted in their three parts: a relation, 
which can be thought of as a function of two arguments, the left concept, and right concept that 
form the arguments of that function. Some examples of these predicates are DefinedAs, IsA, 
UsedFor, HasProperties, MotivatedByGoal, etc.  ConceptNet semantic network consists of over 
21 of these predicates (Havasi et al, 2003).   
The CIF went through several rounds of testing and redesigning.  The alpha design of the 
CIF was then put before a team of usability experts, the members of the Human-Centered 
Computing Lab. In this iteration the focus was primarily on the usability features of the CIF. 
To gain a better understanding of a participant?s hobby, these predicates were employ, 
which allow participants to invoke their own ontology and description of their stated hobby (see 
Figure 3).  Thus, in the CIF, if a participant cannot identify with a hobby in the drop-down list 
and decides to add a new hobby, a new window will open where the participants are prompted to 
enter their hobby and select the natural language statement(s) the predicates are extracted from 
that best describes their hobby Figure 4. 
 
37 
 
 
Figure 3: Add Your Hobby 
 
 
38 
 
Figure 4: Describe Your Hobby 
 
3.2 Component II: Culture Data Mining 
 
The vast amount of data about individual learners that can be captured through the CIF, 
creates the need for characterizing groups of learners in order to constrain their cultural traits 
used by C-CAL. Cultural data mining is a means for accomplishing this characterization of 
learners interacting with the system. Taken into consideration the vast differences between 
learners and their experiences, personal customization for each learner quickly becomes a costly 
challenge. Though the goal of C-CAL is to provide a unique personalize learning experience for 
each learner, C-CAL uses cultural data mining to capitalize on the shared cultural practices and 
 
39 
ontology?s that the learners expressively participate in or can relate to, and form clusters of 
learners.  
In general, data mining provides a means of transforming large groups of data into 
information by extracting a pattern. It also designates fitting a model to data, finding structure 
from data, or in general, any high-level description of a set of data (Fayyad et al., 1996). Data 
mining algorithms? ability to extract patterns from data facilitates a growing need to analyze a 
subset of data or a model applicable to that subset, within a large data set.  
As societies quickly move from data sets consisting of kilobytes to now petabytes of data, 
it quickly becomes a daunting task to extract useful information. As computers grow in speed, 
number-crunching capabilities, and memory, scientific researchers are edging into data overload 
as they try to find meaningful ways to interpret these data sets (Kamath & Parker, 2000). Giving 
thought to the notion of varying culture identities that exist in our society, data mining offers a 
means of extracting these unique patterns enumerated from data, as opposed to relying upon 
assumption or sweeping generalizations.  
For this reason, the idea of cultural data mining is taking root. Manovich observes that, 
until now, the study of cultural processes relied on two types of data: shallow data about many 
people (statistics, sociology) or deep data about a few people (psychology, ethnography, etc)  
(Manovich, 2009).  Utilizing data mining, detailed data about very large numbers of people, 
objects and/or cultural processes can now be collected, and no longer will one have to choose 
between size and depth (Manovich, 2009). 
In an effort to gain a better understanding of the learner and the learner?s experience, the 
study of Educational Data Mining has emerged as the area of scientific inquiry centered around 
the development of methods for making discoveries within the unique kinds of data that come 
 
40 
from educational settings, and using those methods to better understand students and the settings 
in which they learn (Baker, in press). Educational data, regardless of its origin, often has multiple 
levels of meaning hierarchy, which often need to be determined by properties in the data itself 
including issues of time, sequence, and context (Baker, in press).  
Within Educational Data Mining tools, popular methods such as prediction, clustering, 
relationship mining, discovery with models, and distillation of data for human judgment have 
been used for applications such as improving student models, discovering or improving models 
of the knowledge structure of the domain, studying the pedagogical support provided by learning 
software and scientific discovery about learning and learners. Thus, applying educational data 
mining to answer questions in any of the three areas of student models, domain models, and 
pedagogical support can have broader scientific benefits of enriching theories and assist the 
scientific community in making better provisions for learners at all stages of learning.  
C-CAL builds upon the concepts of culture and educational data mining in utilizing 
clusters to better understand learners. The data collected from the CIF was analyzed using 
cultural data mining, by running a clustering algorithm, Application Quest?, to determine the 
dominant culture of participation among the participants.   Applications Quest? is a dynamic 
software tool developed to perform holistic comparisons using a hierarchical clustering approach 
(Gilbert, 2006).  Applications Quest? (AQ) takes in numerical values and nominal attributes to 
determine clusters of similar applications. AQ compares every application to every other 
application using n C r = n! / [(n-r)! r!] comparisons, and places the result of each comparison 
into a database table called the similarity matrix. All numeric attributes are scaled to values 
between 0 and 1 and used in a squared Euclidean distance measure.  When considering nominal 
values, the Nominal Population Metric (NPM) is used, which results in values between 0 and 1 
 
41 
as well. The NPM begins by identifying the nominal attributes within the similarity matrix and 
then processes them as follows (Gilbert, 2007): 
1. Compute the total number of combinations for all applications using n C r.  
2. Compute the number of unique nominal attribute values.  
3. Compute the number of combinations for the unique nominal values using n C r.  
4. For those combinations of the nominal attribute value pairs, compute the coverage 
percentage within the application similarity matrix.  
5. The nominal population matrix shows nominal attribute pair coverage across all 
comparisons. This is an accurate measure of the impact of the nominal attribute 
value pairs based on their actual existence within the data population. The next 
step in this process is to adjust the Coverage values if necessary. This is the 
desired goal when the application is measuring difference vs. similarity.  
6. The Coverage values in the nominal population matrix are now the Nominal 
Population Metrics that can be used in clustering algorithms to accurately 
compare nominal attribute values. 
Using the squared Euclidean distance measure, AQ computes a similarity matrix. To 
determine the clusters, AQ uses a divisive clustering approach by identifying the two most 
different applications using the similarity matrix. Using the two most different applications, AQ 
forms clusters around them based on each individual application?s closeness to one or the other. 
The Applications Quest? algorithm provides diverse clusters that make it ideal for cultural data 
mining, in which similar culture attributes can be clustered holistically.  Thus, it forms clusters of 
similar cultural practices obtained from the Culture Inquiry Form. The clusters are then analyzed 
 
42 
for dominant attribute values, i.e. those that are shared the most. The dominant attribute value(s) 
represent the actual cultural traits of the cluster. 
 
3.3 Component III: Culture Dyads 
 
In a full implementation of the C-CAL system, the dominant culture obtained from the 
cultural data mining of the CIF data, would be automatically linked to the computing concepts 
based on the similarities of the understanding of attributes and processes. These newly formed 
dyads (dominant culture and computing concept) would then be infused into a template, to form 
the basis of a culturally relevant learning module.  Instead, a Wizard of Oz (Woz) method was 
employed in order to prove the utility of linking culture to learning computing concepts rather 
than focusing on the design of the system.  WOz is a method used to test device concepts and 
techniques and suggested functionality by evaluating unimplemented technology by using a 
human to simulate the response of a system (UsabilityNet, 2006). The ?wizard? or the researcher 
simulates the systems response in real time (UsabilityNet, 2006). The WOz paradigm was 
conducted to simulate and frame the information retrieval aspect of the system. Using the WOz 
paradigm allowed for the development of a model that can be later formalized.  
In this research, the ?wizard? uses the dominant culture as the input, it is then anatomizes 
in search of the generally understood attributes, practices, and processes that mirror any of the 
basic properties of the concept. This process entails an extensive review of the shared ontology 
discovered through the focus group, and is followed by additional research obtained by 
conducting a guided search using a search engine, all to depict a more contextual understanding 
of the culture.  The information from the shared ontology and the guided search allows for more 
concrete points to match the culture to the concept. The dyads that are formed as a result are 
 
43 
reviewed by at least one participant in the culture of practice. If the foundational structure used 
to relate the culture to the concept is not seen as logical to a current participant in the culture, the 
process is repeated.  
This model implemented using WOz, as explained in more detail below, is dependent 
upon trial and error practices to refine the basis of the dyads.  This refinement entails beginning 
with the dominant culture cluster attribute(s) concept obtained from the cultural data mining of 
the CIF data, and the additional information obtained regarding that concept when it was first 
evaluated. For example, given a culture of participation of golf or baking, the system uses the 
participants? given ontology from the add hobby section or that which was provided in the focus 
groups. A framework can then be established as to what this concept involves, i.e. its tasks, steps, 
purpose, procedures, objectives, and goals. Such as with baking, participants discussed the steps 
to following recipes; or in golf, they mentioned the purpose and goals of the different clubs.  
Using that information as the primary knowledgebase, an online search is then conducted on the 
cultural activity using various search engines, such as Google, Bing etc to retrieve additional 
information that will help fill in more of the systems knowledge pool of this concept. For 
instance, it is learned from guided search via the search engine: following recipes, which often 
involves following a sequential or ordered list of steps, or the understanding that golf clubs, 
though they vary in goals have a similar function.  
As the guided search stage is entered, several factors are taken into consideration.  Being 
aware that the goal for this portion of the system is to link the culture concept to our computing 
concept, the search is commenced with the definition that is derived from the computing 
concepts.  Thus, as the search is conducted for additional details related to what the culture 
concept involves, (tasks, steps, purpose, procedures, objectives, and goals of the culture concept) 
 
44 
it will be done from the perspective of those related to the computing concept.  Ideally, this 
would be a sort of matching game.  For example, match the culture and concept as they relate to 
the process of queuing or a series of steps. Also, at this stage, once the information has been 
retrieved, the wizard takes into consideration the ?who you are? aspect of the participants? 
culture, and the stored findings are strictly based on how participants have identified themselves 
demographic wise.  For example, it is unclear what is the stage or the range of participation of all 
participants that fall into a given cluster, depicted by the culture attribute of golf, are involved 
with golf. Without knowing the occasional players from the semi-pro hobbyist, there is an 
attempt to strike a balance of what would be considered general knowledge. Thus, most of the 
golf information is gathered from sites geared towards beginners, novice learners, or general 
information sites such as Wikipedia or various basic ?how to? or introduction to golf sites.   
For clusters formed around industries, the researcher review participant?s educational 
background, and thus restrict our stored findings to that which would be considered common 
knowledge with the range of education displayed by the participants in that cluster for that given 
industry. For example, if a cluster is formed around the medical industry, and the participants? 
education ranges from high school diploma to some college, the focus would be on jobs in the 
medical industry that do not require advance degrees and what would be common knowledge 
among those participants would become the focus. After gathering the background knowledge of 
the respective cluster attribute or what is understood to be the shared ontology for participants of 
that culture attribute, it is time to attempt to link them to the corresponding computing concepts.  
 
3.4 Creating Culture & Computing Dyads  
 
 
 
45 
Much like the above scenario, using the dominant attribute of each culture cluster 
produced by Application Quest?, dyads are based upon the culture?s processes, procedures and 
description.  For example, if the named culture were characterized by a series of steps, thus 
having a likeness of a function, it would be linked to functions. A culture cluster can be linked to 
multiple computing concepts especially in cases where a culture cluster has more than one 
dominate culture.  Based on what is known or expressed by the participant regarding the 
dominant culture, the linking is done on what appears to be the more apparent, clear and logical 
relationships between the culture concept and the computing concept. For example, baking is 
linked to functions based upon the participant?s acknowledgement and identification with its 
steps and processes. The linking is done based on the shared ontology expressed by culture 
participants and/or the culture processes, procedures and description that are considered common 
public knowledge. 
 
3.5 Conclusion 
 
Capturing a synopsis of the vast amounts of culture activities and processes any given 
person participates in on a regular basis can be challenging, as the researcher found very limited 
tools and resources providing guidance in this manner. In addition, because this body of research 
implements a broader definition of culture, the possibilities of the activities that encompass the 
cultural of participation of any one person can be a part of can become quite large quickly. As a 
result it was understood that in this initial design the goal will be more to employ a workable 
method as appose to a precise tool.  
Creating a culture and computing model is geared to create a method to link the 
participant?s cultural knowledge to the similar practices within a set of computing concepts. As a 
 
46 
caveat, it should be mentioned that linking learners? culture based knowledge to that of 
computing concepts is not an exact science and, thus will require human input. For example, it is 
clear or implied that a fetch and pre-fetch process is a part of the culture of doing laundry, 
however it is difficult to design a tool to decipher such a concept from a learner?s description, 
because, they may view and describe their participation in a range of methods.  Such research, 
and the automation of this discovery process, is beyond the scope of this dissertation, yet the 
same goal can easily be reached with human input.  However, this is fertile ground for future 
research. This phase will be assessed via a control study of learners. 
 
 
47 
Chapter 4 
 
System Implementation
 
 
As a proof of concept, objects, functions, and variables frequently found in introductory 
computing lessons were selected to serve as the bases for computing concepts to be introduced 
by the C-CAL system. The concepts of objects and functions were specifically chosen because it 
has been proven that they are often regarded for the higher level of difficulty they present to 
novice learners to grasp. 
The search to find a layman?s definition for the three computing concepts was initiated at 
Wikipedia and other non-expert driven domains found via Google searches.  The online 
definitions found were then combined with those used in standard introduction to computing text 
books commonly employed in college courses among novice learners such as Deitel and Deitel 
series on How to Program  (Deitel and Deitel, 2007); or Programming for the Absolute 
Beginners (Ford, 2007); and Head First Programming (Griffiths and Barry, 2009) that are 
targeted for novice learners with limited to no computing experience to forge a suitable working 
definition.  Again an iterative design process was employed. First, detailed definitions of each of 
the three terms were presented in the form of a matching game to three usability subject matter 
experts. The experts were instructed to match the term to its definition.  After several rounds of 
cycling through the design process, the feedback of the participating experts was used to refine, 
the definitions.  These definitions were then presented to two novice adult learners. The learners 
were instructed to enter a hobby into a text box (Figure 5), in which they engage in extensively, 
 
48 
possess an in-depth understanding of, and participate regularly at a high level of regular 
participation in.  The participants were then given the three definitions. Each definition was 
followed by a generic example (Figure 6), similar to the first introduction to the given topic in 
one of the traditional book sources previously discussed such as the Head First Programming and 
the Programming for the Absolute Beginners.  
 
 
Figure 5: Defining Hobby 
 
 
49 
 
Figure 6: Hobbies in Context 
 
The participants were instructed to create their own detailed example depicting the given 
definitions using the hobby they previously identified. In addition, they were instructed to 
present their examples as if they were teaching someone how to engage in their hobby. After this 
step, the working definitions for the three concepts were finalized.  
 
4.1 Component IV: Learning Modules 
 
 
50 
The design and implementation of the C-CAL learning module essentially follows the 
ADDIE model, five phases to designing e-Learning instruction: Analyze, Design, Develop, 
Implement, and Evaluate (Smith and Ragan, 1999). The design of the C-CAL learning module 
will be explained within this context.  
 
4.1.1 Analyze 
 
In this phase the problem in which the learning module will be designed to address, is 
clarified; the delivery option is determined; and the instructional goals and objectives in the 
learning environment are established. Recall, the Culture Inquiry Form (CIF) collects the 
demographic information, cultures of participation, and the learners existing knowledge and 
skills. This information provides answers to analysis questions such as the characteristics of the 
target audience and the existing, if any, learning constraints. The problem and objective 
presented in the learning module will be for learners, using their cultural knowledge, to gain an 
understanding of the computing concept, and demonstrate that understanding. Given the C-
CAL?s system focus on adult learners, though debatable in the larger body of research, adult 
learner theories such as andragogy (Knowles, 1984) are employed for the design. Thus, factors 
such as need-to-know (the reason for learning will be presented at the very beginning of the 
learning module), relevance (the learning module immediate relevance to the learner will be 
clear), and orientation (the learning module will be presented in a problem centered fashion) 
must be accounted for in the learning module. The overall goal here is to lower the entry barrier 
by presenting the system as a low risk, minimum commitment type of a system. In addition, 
because time is a major factor with the target audience, the required time commitment for the 
learners also served as an objective of how to structure the material such that the learner will not 
 
51 
be deterred from engaging. Finally, delivery medium will remain as an online application, as the 
C-CAL system is foundationally an e-Learning tool. The analyze phase institutes the basic 
direction of the learning module, now the design and development of the learning module can 
commenced.  
 
4.1.2 Design and Development 
 
Each learning module presented to learners follows the learning module template 
outlined in this design section. In the C-CAL study each experimental module consisted of five 
pages: 1) Welcome to the study, 2) culture based example (entitled ?A Good Day?), 3) the lesson 
(entitled ?Learning a Concept?), 4) the place where both the culture and concept are presented 
together for one last time, and 5) the page where user turn to demonstrate understanding entitled 
?Your Turn?. The control module follows the same template as the experimental module, with 
the exception that there is no ?Concept Relation? page.  The layout of each page will be 
discussed separately below. The learning modules were developed in php using an sql database.  
The introduction page, Figure 7 contains the basic instructions guiding the learner to 
begin the study, through the learning module and finally to the feedback section.  Participants 
begin the actual study when they click ?Begin Study? on the second page.  
 
 
 
52 
 
Figure 7: Introduction Page Screenshot 
The second page of the module is designed such that it presents an example or a scenario 
that is comparable to a situation in which the computing concept can be demonstrated or 
explained.  For the purposes of testing C-CAL the control module uses examples and scenarios 
similar to those presented in Guzdial & Ericson (2006) as seen in Figure 8.  
 
 
53 
 
Figure 8: Page 1, Control Screenshot 
 
In the experimental learning modules, the second page is the cultural based example 
entitled, ?A Good Day.? At this juncture, a detailed culturally situated scenario is presented. Our 
goal here is to initiate the introduction of the computing concept to the learner with what is 
already familiar, thus lowering the entry gate of this learning process. Each scenario is fashioned 
around the dominant cluster attribute, for the given cluster that the learner is apart of as seen in 
Figure 9, where this participant, for example, was placed in the holiday traditions cluster.  
 
 
54 
 
Figure 9: Culture Based Example Screenshot 
 
The design of page three of the learning module entails a simplified definition of the 
computing concept and its characteristics as seen in Figure 10.  
 
 
55 
 
Figure 10: Concept Defined: Control Screenshot 
 
In addition, the C-CAL learning module follows the definition with a recall of the 
scenario by continuing the culture-based example from page two. Thus, the related computing 
lesson on page three entitled, ?Learning a Concept,? is immediately introduced. This ties into the 
notion of a new vocabulary. This is done as an effort to show relevance to the learner and ease 
them into understanding this new concept.  Figure 11 demonstrates the same functions concept 
definition as presented in the control in Figure 10, however it reconnects the idea of a function to 
that of the gift-wrapping scenario that was previously presented in Figure 9. The definition 
presented in the module was massaged earlier as discussed in the C-CAL Concepts section. 
Because C-CAL is focused towards novice learners, and the main goal of building a bridge from 
their current knowledge of the culture activity depicted on page two of the learning module, to 
the computing concept introduced on page three, a streamlined, high-level explanation of the 
computing concept is presented. 
 
 
56 
 
Figure 11: Concept Defined-Experimental Screenshot 
 
At this stage, C-CAL has merely pointed out to the learner that there is a similarity to 
their knowledge base and this computing concept, and has provided the vocabulary to better 
understand the concept.  Thus, on page three of the learning module more depth is provided to 
the learner to move them from simply being aware of the concept, to gaining understanding of it 
and its parameters.  Also on this page, an example of the computing concept is offered that 
continues to draws from the original scenario. This page only exists in the experimental C-CAL 
learning module and not in the control, as its design is to reinforce the cultural base learning.  
Figure 12 continues with the introduction of the functions concept presented in the gift-wrapping 
scenario context. The learner is asked to recall the definition of the concept presented on the 
previous page. Additional information on the concept is then given to reinforce the definition and 
solidify its meaning and its characteristics. A final example is presented using the culture 
concept structured in the fashion of the computing concept.  
 
57 
 
 
Figure 12: More on Concept in Culture Screenshot 
 
Finally, on the last page entitled, ?Your Turn,? the learner is challenged to create their 
own everyday example of the contextualized computing concept and its characteristics they 
learned on the previous pages to demonstrate understanding. This can be seen in Figure 13. Hints 
are provided along the way that relate back to what the learner has been exposed to throughout 
the lesson to help guide them, to serve as a reference and to remind them of the meaning of the 
new vocabulary they are being asked to create an example around.  
 
 
58 
 
Figure 13:Your Turn Screenshot 
 
 
 
4.2 Implementation and Evaluation 
 For the purpose of this study the C-CAL system will be implemented as within subjects 
study. As such all participants will use both the control learning module and the experimental C-
CAL learning module. Figure 14 flowchart, demonstrates an example of this process. For 
example, if a participant begins with the experimental learning module they will go through the 
welcome page, the cultural example, the concept, the chance to demonstrate understanding, a 
feedback section and then immediately loop through the corresponding pages for the control 
learning module, and vise versa if they begin alternatively with the control.  The C-CAL system 
doesn?t require any training for the learners and because it is an e-Learning support tool, there 
are no facilitators for the system.  
 
 
59 
 
Figure 14: Learning Module Flowchart 
 
Each scenario was fashioned around the dominant cluster attribute, for each given cluster. 
These attributes reflect the  ?what they do? portion from the CIF, derived primarily from the 
sections of industry, hobbies and traditions.  Scenarios created around industry were designed to 
reflect the basic knowledge of someone, in the industry, with no more than a trade school level 
of education or training.  Scenarios derived from the hobbies and traditions culture segments 
reflect the general culture knowledge that is commonly shared and understood by participants of 
that activity and that often are understood at a surface level explanation of the activity. Though, 
in creating the scenarios, the culture cluster attribute was used as the base, and the ?who you are? 
information gathered to frame the scenario.  For example, for a participant in the in the cultural 
activity of golf, golf balls and clubs is understood.  After a series of iterative tests and from what 
is known about our target audience time restraints, the researcher made sure that each scenario 
was as clear and direct as possible.  
 
4.3 Conclusion 
Finally, in creating the learning instruction, using the ontology drawn from the database 
and based upon the links created, an information retrieval model was used to do a document 
 
60 
comparison and create content using the ontology from the identified culture. Within every 
culture there is a shared ontology, an organizational structure of knowledge, rich with language 
and vocabulary that is understood by participants of that culture, representing knowledge and the 
organization of knowledge in a particular domain for problem solving (Merrill, 1999).  The 
researcher believes that culturally relevant software should reflect the ontology of the culture to 
which it aims to teach. For example, the instructions given should emulate the manner in which 
instruction is given within the target audience's culture. In the domain of football, if one were 
designing a piece of software for football players, the instructions would be very brief and 
concise, without the use of superfluous language, much like the interaction between a football 
coach and his players. Similarly, the manner in which feedback is given should be representative 
of the way in which feedback is generally given within the culture of the targeted audience. It is 
also important that the learning technology makes use of the vocabulary common to the culture 
of its audience.  Furthermore, the learning technology should use a familiar vocabulary when 
discussing the main ideas, abstract concepts, as well as activities found within the tool.  
Generally speaking, all spoken or written words within the context of the educational software 
should also utilize the language conventions practiced by the target audience. The assessment 
will be designed based on the ontology.  
 
 
 
 
 
 
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Chapter 5 
 
Experimental Design & Findings 
 
 
Building from the discussion in Chapters 3 and 4, of the design and the implementation 
strategies of the C-CAL system, this chapter presents the experimental design used to address the 
research question, and the findings from the analysis. The research question: ?Is culture based 
learning a feasible option to introduce adults to computing; and does culture based learning 
enhance the learning experience for adult learners when being introduced to computing 
concepts?? will be answered by looking to the C-CAL system first via its components, then as a 
system in its entirety in addressing/answering the research question. 
The experiments used in this study assess the design of C-CAL, test and evaluate it as a 
means for capturing a person?s cultural understandings, uses these cultural understandings to 
construct a learning module teaching culturally relevant computing constraints, and then assesses 
the usability of the system in its entirety.  
The study is designed to gain a better understanding of how one?s various silos of 
knowledge concerning the culture in which he or she participates can be used to enhance their 
learning experience of computing constraints.  Specifically, the purpose of this study is to 
increase the understanding of how to utilize culture in the design of computing artifacts.  
Findings from this study are intended to inform computing designers and developers.  The data 
to be analyzed in the study were collected from several iterations of experiments that shaped the 
 
62 
development of the final system.  This study is a complete analysis of data using both descriptive 
statistics and inferential analysis. 
Each experiment is discussed below, in the context of the research question and the C-
CAL system.  The experimental design and findings are presented in the format of the approach 
and the results are followed by the analysis.  The approach used will expound upon a hypothesis; 
the experimental method used; the procedure followed; an overview of the participants and the 
measures, if a user study was conducted, else, an overview of the design strategy and 
implementation used to address specific aspects of the research question.  This chapter presents 
the methods used in this research study, the purpose and design of the study, population and 
sample selection, instrument validity and reliability, and data collection strategies. 
Data Collection: Several data collection methodologies were employed throughout this 
research. A focus group protocol, survey instruments, and a questionnaire were all employed for 
data collection. Participants were informed before each part of the study of what the study would 
entail, their rights to withdraw from the study at any time, and the estimated time commitment.  
Analysis: Various statistical analyses were employed throughout this body of research, 
comparable to the data collection used. Descriptive statistics were analyzed using statistical 
software, such as Microsoft Excel and Statistical Tools, to use for the Kolmogorov-Smirnov test 
(Kirkman, 1996) to provide a thorough analysis of the data. In addition, all the data received 
from the focus groups, instruments and questionnaires were then compared and analyzed for any 
noticeable trends or phenomena originally not accounted for throughout the study and the 
literature.  
Delimitation and Limitation of Study: Though the research produced in this study can be 
applied to various groups of learners, a delimitation of the study includes those people of color of 
 
63 
African descent, 19 years of age or older, and having limited, advanced, or formal education. The 
sample population is composed of a range of adult learners from all walks of life. The sample 
population was selected based upon availability and accessibility of subjects to the researcher 
and how closely they resembled the target audience.  
There are certain limitations to this study. The surveys were dispersed via an online 
system. The researcher had no control over response time, willingness and accuracy; however, 
because it is a self-reporting study, an accurate response is assumed. In addition, this delivery 
method opened the research up to participants that do not fit neatly into the confinements of the 
target population, thus, adding some variability to the demographics. This method was chosen 
because it was the most effective for this study. Another limitation that the researcher is aware 
of, is regarding the data collection method of experiments conducted on the entire system.  
Participants that complete both parts of the study could reflect the segment of participants that 
are self-driven and motivated.  
 
5.1 Understanding Culture of Participation (Component1): 
In chapter 3, System design section (3.2.1), the use of focus groups was demonstrated and 
proven to be a viable means to determine the adaptive responses and/or activities that reflect a 
person?s knowledge base as a participant of that cultural practice. By asking adults about their 
shared experiences regarding their participation within a community of practice, they reveal 
cultural attributes that provide insight into how they identify themselves as participants of these 
cultures.   
The design of a culturally relevant system depends heavily on the system?s ability to 
identify the culture(s) of which the learner is a participant and how the notion of culture is then 
fitted onto the learner. Because this body of research employs a definition of culture that is a 
 
64 
conjunction of the two more commonly used definitions of culture, there was a need for a tool 
that reflected a more holistic view of the learner. The challenge of component 1 was how to 
create such a tool. The replicable process presented in chapter 3 of the creation of the CIF 
demonstrates how to create such a tool and meet the need. As a result the CIF produces a holistic 
profile of each learner?s culture of participation as provided by the learner. 
 
5.2 Mining for culture (Component 2):  
Culture data mining is the second component of the C-CAL system. To validate C-CAL?s 
ability to determine the dominant culture of participation for a group and correlating processes, a 
validation study was conducted using the CIF to collect participant?s information and the 
Application Quest? algorithm to analyze the data. 
 
5.2.1 Hypothesis 
Application Quest? is an effective algorithm to accurately determine the dominant culture of 
participation for a group of CIF users.  
Independent variable: Dominate culture determination  
Dependent variable: Accuracy of dominant culture participation   
 
5.2.2 Method 
 
The questionnaire methodology of the CIF tool was used to collect participants? 
information as it provided several benefits.  Using the questionnaire methodology, responses are 
gathered in a standardized way, so questions are more objective. It also allows for faster 
collection of information from a large portion of a group.  The questionnaire methodology also 
 
65 
presents a sense of familiarity to most people; it reduces bias because of its uniform question 
presentation; and allows for easy quick analysis.  Thus, the data collected from the questionnaire 
allows for a snapshot of culture to be depicted by the system that can be easily fed into a cluster 
algorithm for further analysis.  
 
5.2.3 Procedure and Participants 
After obtaining human subjects approval, participants were recruited from Auburn 
University Comp 1200, which is the introduction to computing course, and Auburn University 
Human-Centered Computing Lab. Notices of the study were announced to students during their 
respective class times. Participants were first provided with a link to an information letter that 
explained the purpose of the study. The link to CIF was located at the bottom of the information 
letter. Participants were informed that the study would last about 5 minutes. Upon completing the 
CIF, the data was submitted and entered into the database. The study ran for about a week, just 
until there were approximately 100 participants. Upon completion of the study, the data was 
downloaded from the database, uploaded into Applications Quest?, collated, and tabulated. 
 
5.2.4 Measure 
Standard statistical data provided little information on nominal data, thus, the researcher 
relied on Application Quest? for measuring the data.  
 
5.2.5 Results and Analysis 
Overall, the study had 104 participants of which 65% were male, 35%, female.  Eighty-
one percent said they were in the age range of 19-24, 16% said they were in the age range of 25-
34, and 3% were in the age range of 45-54.  Regarding ethnicity, participants identified 
 
66 
themselves as 79% Caucasian, 17% African American, and 4% Asian. The demographic 
information for this study is depicted in Table 4. 
 
Table 4: Validation Study Demographic 
Demographic Variable N % 
Gender   
female 36 35.0% 
male 68 65.0 
   
Age   
19-24  84 81.0% 
25-34  17 16.0 
45-54  3 3.0% 
   
Ethnicity   
asian 4 4.0% 
black 18 17.0 
white 79 79.0% 
   
Education   
col 74 71.15% 
ged 16 15.38 
grad 13 12.50% 
   
Totals 104  
 
 
Application Quest? was run on the 104 collected responses. In reviewing the collected 
data, it was decided to manually regroup attributes, such as hobbies and traditions into buckets, 
much like clustering methods used in data mining.  After reviewing the entries for hobbies and 
traditions, it was discovered that there were several overlaps. Thus, the researcher decided to 
condense these separate entries into one larger grouping. For example, several entries included: 
?sports?, ?football?, ?basketball?, and ?sports in general?. All such entries were then identified 
under the larger category of sports. Another example, the entries of ?watching TV? and ?watching 
 
67 
movies?, was condensed to the category of entertainment. The buckets created for hobbies can be 
seen below in Table 5. A complete list of all the entries and buckets created based on these 
entries are in Appendix D. A similar approach was taken for the traditions attribute. For the 
traditions question, the participants entered more of an explanation of what their tradition 
entailed.  For example, one entry would be ?Christmas dinner with the family?. Observing the 
entries, the researcher created buckets around the central themes and checked all that apply for 
each given tradition. Using the example above, holidays, dinner, and family would have been the 
buckets checked off. The majority of participants in this particular study didn?t enter a tradition, 
and given the amount of variability in the entries, it was not included it in the final analysis.  
 
Table 5: Hobby Bucket 
Hobies HobyBucket 
crafting arts 
singing arts 
theatre arts 
colector colector 
cars colector 
watching 
movies entertainment 
watching TV entertainment 
music entertainment 
surfing the web entertainment 
video games entertainment 
swiming sports 
basketbal sports 
runing sports 
golf sports 
socer sports 
basebal sports 
tenis sports 
sports sports 
Hunting sports 
hiking sports 
Triathlons sports 
 
68 
 
Upon uploading the data to Application Quest?, k the desired number of clusters the 
responses to be grouped into, had to be determined. As with other clustering algorithms the 
researcher randomly determines the k-means clustering value. In doing so, the researcher will 
determine, using trial and error, which k-value is appropriate and will yield the best partitions of 
n cultures. So several numbers were tried with the goal that the clusters would remain relevant an 
acceptable result, where irrelevant entries are not grouped or forced together, and that did not 
result in several clusters all having one entry. Thus, after several runs and trials the k-means 
clustering value, the number of clusters, was set to nine.  k-means clustering value of 9 was 
chosen because it provided a more logical distribution. For example, k larger than 9 resulted in 
several clusters with only one entry, however k smaller than 9 produced a cluster that placed well 
over half of the participants into one cluster. Table 6, below, is a summary overview of the 
results. There were 84 participants in the age range 19-24, 17 in the age range 25-34 and 3 in the 
age range 45-54. Fifty-eight partipants identified their computer level as intermediate, 34 as 
expersts and 11 as novice. When asked where they most frequently use the computer 89 
particpants said at home, 8 said at work and 6 said another location. Sixty-four particpants 
identified the highest-level education obtained at some college, 16 said diploma/GED, and 13 
siad graduate or professional degree. Seventy-nine identified their ethnicity as White or 
Caucasian 18 as Black or African American and four as Asian.  Participants were asked about 
how often they used the internet for personal use 96 participants said daily, 6 said weekly and 
one no response.  Sixty-eight of the partipants identified themselves as males and 36 as females. 
Participants listed a range of hobbies they participated in, for example 46 participants listed some 
 
69 
kind of a sport as a hobby 22 listed some form of entertainment such as watching T.V. or surfing 
the web, and five participants listed fishing as a hobby.  
Table 6: AQ Summary 
AU AQ Aplication Sumary (104 aplications and 9 clusters) 
Diference Index for al Aplications 26.97% Standard Deviation 17 
Diference Index for Recomended Aplications 48.07% Standard Deviation of 14 
Age: 19-24 (84) 25-34 (17) 45-54 (3) 
CompLevel: 
intermediate (inter) 
(58) expert (34) novice (1) 
CompUsage: home (89) work (8) another (6) 
Education: 
some colege (col) 
(64) 
Diploma/GED (ged) 
(16) 
Graduate or profesional 
(grad)(13) 
Ethnicity: white (79) black (18) asian (4) 
Frequency: daily (96) wekly (6) empty (1) 
Gender: male (68) female (36) 
Hobies: sports (46) entertainment (2) fishing (5) 
 
Of the nine clusters, 86 participants fell into clusters one, five, and eight. The dominant 
attributes of those clusters are depicted in  
Table 7 below. Cluster number 5 has 44 applications (Table 8 below). Of the 44 
applications, all of them had attributes of age and gender in common while the majority, 50 
percent or more, of them had the attributes that indicated where they gained access to a 
computer (computer usage), how frequently they used a computer (frequency), and 
ethnicity in common. Clusters 8 and 1 had a similar break down with twenty-two and 
twenty-one applications, respectively, as can be seen below in  
Table 7. Table 8-Table 10 are a summary of each of these dominant clusters produced by 
Application Quest?. 
 
 
 
 
70 
 
 
Table 7:  Dominant Attribute 
  
Cluster 
Number of 
Applications 
Dominate Attributes 
5 44 Age, gender,  Usage, ethnicity, Frequency 
8 22 Usage, frequency Gender, 
level 
Age 
1 20 Frequency Level Gender 
 
Table 8: Cluster 5 Summary 
Cluster 5 Sumary (4 Aplications) 
Diference Index for this Cluster 9.2% Standard Deviation 8 
Age: 19-24 (4) 
CompLevel: inter (37) novice (5) expert (2) 
CompUsage: home (41) another (3) 
Education: col (30) GED (8) AS (6) 
Ethnicity: White (42) Black (1) Hispanic (1) 
Frequency: daily (41) wekly (3) 
Gender: male (4) 
Hobies: sports (27) fishing (4) entertainment (3) 
 
Cluster 5 profile depicts primarily white males, ranging in ages of 19-24, that utilize the 
internet for personal use daily from home that consider themselves regarding internet usage, as 
?intermediate;? and in which more than half of them identified sports as a hobby. Also in cluster 
five, understanding the homogeneity of the group and that the majority of the study?s participants 
were recruited from an undergraduate course populated by majority freshmen and sophomores, 
one can also determine that the difference in the ?highest level of education attained? depicted in 
this group can be really a matter of perspective. The highest levels of education attained in this 
group were ?some college?, high school diploma/GED, and associates degree. These categories 
were separated to allow for a variation in description to provide participants with a variety of 
 
71 
options as to how they view their academic level, however, for this body of research they will be 
viewed as similar and grouped together. This is done because the participants from this study 
were primarily college freshman and sophomore, thus, their prior education is considered 
similar. For example, the options of ?some college? and ?having obtained an associates degree? 
can be viewed as one and the same for participants. Thus, reducing the variability and further 
displaying cluster five as comprised of participants with very similar dominate cultures of 
participation. 
Cluster 8 profile depicts primarily white females, ranging in ages of 19-24, that utilize the 
internet for personal use daily from home that consider themselves, in regards to internet usage 
as ?intermediate?. Thus, cluster eight is similar to cluster five with one major difference, gender. 
Cluster 8, gender population is majority female. Much like cluster 5, there is a similarity in 
education in that the two dominant selections for highest education attained were ?some college? 
and high/GED which can, once again, based on prospective of the participant, be reduced to 
being one in the same, especially in regards to the participants in this clusters whose selection for 
highest education attained were graduate or professional degree.  
 
Table 9: Cluster 8 Summary 
Cluster 8 Sumary (2 Aplications) 
Diference Index for this Cluster 12.25% Standard Deviation 
9 
Age: 19-24 (20) 25-34 (1) 45-54 (1) 
CompLevel: inter (21) novice (1) 
CompUsage: home (2) 
Education: col (14) GED (5) grad (2) 
Ethnicity: White (17) Black (3) Asian (2) 
Frequency: daily (2) 
Gender: female (21) male (1) 
Hobies: entertainment (7) sports (6) traveling (3) 
 
 
72 
Cluster 1 profile depicts primarily white males, ranging in ages of 19-24, that utilize the 
Internet for personal use daily from home, that consider themselves regarding internet usage as 
?expert?. Thus, cluster 1 is similar to cluster 8 with one major difference, the participant?s 
perceived level expertise of Internet usage. The majority of participants within cluster one 
consider their level of expertise regarding Internet usage as ?expert?.  
 
 
Table 10: Cluster 1 Summary 
Cluster 1 Sumary (20 Aplications) 
Diference Index for this Cluster 13.98% Standard Deviation 
10 
Age: 19-24 (17) 25-34 (3) 
CompLevel: expert (19) novice (1) 
CompUsage: home (17) another (2) work (1) 
Education: col (15) GED (3) grad (2) 
Ethnicity: White (15) black (3) asian (1) 
Frequency: daily (20) 
Gender: male (18) female (2) 
Hobies: entertainment (8) sports (8) dancing (1) 
 
 
5.2.6 Conclusion 
 
The clusters produced by Applications Quest? provide an accurate and efficient way to 
capture the dominant cultures of participation. The efforts of determining dominant cultures of 
participation are normally determined after extensive ethnography studies.  Which entails a 
researcher conducting fieldwork while living like those they are studying usually for a year 
(Genzuk, 2003). Though valuable are the findings and discoveries of such fieldwork regarding a 
deeper understanding of a given culture, this process isn?t always feasible. Application Quest? 
within the C-CAL system provides a similar conclusion to that of an ethnographic study would 
have reached requiring much more time and effort.  Thus, AQ tremendously aids in the effort to 
 
73 
capture the same essence of a learner in a quantitative approach. In this very basic study it was 
easy to see and draw logical conclusions regarding each cluster. In running Application Quest?, 
industries and traditions were excluded in the specified attributes to simplify the data analysis. 
The similarities were so few they did not contribute to the clusters.  
 
 
5.3 Culture Dyads (Component 3):  
Using the dominate culture clusters provided from component 2, culture dyads, are 
formed as prescribed by the design of component 3. Here a demonstration of is provided of the 
use of component 3 for the matching of culture processes to the corresponding processes of 
computing concepts. 
 
5.3.1 Hypothesis 
A culture process can be correlated to one or more computing concept. 
Independent variable: Culture & Computing concept  
Dependent variable: culture processes, correlation between culture & computing concept 
 
5.3.2 Method 
 
The method used to match the culture and computing concept will follow the steps 
prescribed in the system design chapter. Thus, by seeking out the tasks, steps, purpose, 
procedures, objectives, and goals of the culture concept found within culture practices as related 
to the computing concept the correlations between culture and computing will be made.  
.  
5.3.3 Procedure and Participants 
 
 
74 
In this initial design of C-CAL, the matching of culture processes and computing 
concepts will be done manually. The dominant culture processes produced from the database will 
be researched to discover parallel phenomena concepts, logic, etc. to that of the computing 
concepts. Ideally, the procedure for this method will flow closely to that of Information 
Retrieval.  Information Retrieval (IR) is the process in which the information retrieval system 
responds to a request by presenting documents to the patron in a sequence, gathering feedback as 
the process proceeds, and using this information to modify future retrieval presenting a means to 
capture expressed information and text collection (Bookstien, 1983). IR?s two main components, 
indexing and query processing, allows for an effective means for an independent search of 
documents and subdocuments. Gauging the information needed via an entered query, IR attempts 
to make decisions regarding document relevance, thus, dictating the appropriate action 
(Bookstien, 1983). Indexing, mostly done by an inverted list, enables fast access to a list of 
documents containing a term (Singhal, 2001). The probabilistic model of information retrieval, 
as discussed by Singhal (2001), ranks documents in a collection by decreasing probability of 
their relevance to a query based on the estimated probability of relevance.  Probability of 
relevance for a document D is denoted by P(R|D). The Bayesian approach to IR uses the 
probabilistic IR model and applies Bayes? Law. This approach circumvents the shortcomings of 
the probabilistic model, by requiring an alternative method to produce the initial document 
ranking for the relevance judgment for the current query to estimate its parameters as discussed 
by Keim, Lewis & Magidan (1997). The Bayesian approach is able to produce an initial 
document ranking without relying on alternative retrieval methods or ad-hoc considerations, 
using the same model both before and after relevance feedback data is available, and allows for 
 
75 
incorporation of relevance feedback information from other queries. Future works of C-CAL will 
entail the automation of this portion of the tool.  
 
5.3.4 Measure 
 
The actual deliverables will themselves serve as the measure, thus, providing a yes or no 
to the feasibility of this question.  
 
5.3.5 Results and Analysis 
 
Using the method indicated above, the cultures were correlated with the given concepts 
and formulated examples/scenarios bridging the two notions. Table 11 below demonstrates some 
of the correlations that were formed. These correlations or links/matches were made across 
hobbies, traditions, and industries. A more thorough list can be found in Appendix E. 
 
Table 11: Sample of Correlated Culture and Concept 
Culture 
Name 
Link Concept Example 
music play-list objects 
It?s a nice day and you just got some new music to 
ad your computer. You figured this would be a 
god time to create a new play-list. So you sort al 
your music by title and gather your favorite tracks 
that wil give you the sound you are seking for this 
play-list. Then you sort your music by rhythm and 
search through and se what other songs in your 
colection would be a god fit. You want one more 
short song to ad your play-list so then you search 
through your list of songs my time periods. Your 
play-list is just about done. Now its time to put it to 
the test. 
 
76 
training 
preparing 
to teach a 
training 
clas 
functions 
Its a nice day and a friend is in ned of your 
expertise to help him prepare to present his first 
training clas. You carefuly explain and 
demonstrate the detailed proces of preparing the 
leson, gathering and sorting the material, creating 
handouts, and creating visual aids. Finaly, you are 
done, and your friend sems to be ready to start 
his first training clas. Now, its training day, and al 
we have to do is wait, for the traine's to arive. 
shoping 
loking for 
something 
in a store 
variables 
It was a nice day. You are out shoping. Sudenly, 
something caught your eye. It is an ad for a Big 
scren LCD TV on sale for only $20. Imagine 
entering the very large store with lots of 
departments, tables, shelves, etc. Al these places 
have diferent things stored in them. You head to 
the department for your Big scren LCD TV. Once 
you find the right department, you have to find the 
type that was on sale. 
golf golf bals objects 
Its a great day for golf, and a friend has asked you 
to help them learn the basics. As you make your 
way acros the gren you start of by explaining 
some basic golf fundamentals the diference in golf 
clubs, golf bals, varying golf holes, while using 
examples that include known golf players and 
comonly known golf concepts. You make it over 
to the first hole, after a couple of demonstrations, 
you guide them through their first swing. 
server 
training a 
traine 
functions 
Its a nice day and you are heading into work at the 
restaurant today, they just hired a new server and 
asked you to train him. You carefuly explain and 
demonstrate the detailed proces of a servers 
responsibility of explaining the menu to the 
customer, taking the customer's order, and 
delivering the customer's meal from the chef. 
Finaly the training sesion is over, and the new 
server sems to be ready to start serving the 
customers. Now al we have to do is wait, for them 
to arive. 
 
 
5.3.6 Conclusion 
 
 
77 
Using the process designed and outlined in this body of research, culture concepts and 
computing concepts were successfully linked. Thus, demonstrating the feasibility of creating 
such dyads and concluding on a method to correlate culture to computing.   
 
5.4 Creating culture based learning modules (Component 4):  
The use of culture data mining for the creation of culture based learning modules uses the 
dyads of component 3 to facilitate creating tailored lessons as discussed in the design of 
component 4.   The creation of culture based learning modules using the above-mentioned 
components is demonstrated below. 
 
5.4.1 Hypothesis 
A tailored lesson based on the correlated matches of the culture processes and computing 
concepts can be manifested.  
Independent variable: culture 
Dependent variable: correlated matches, lessons 
5.4.2 Method 
 
Basic modules were designed around these matches to reflect the computing concept to 
be learned. Thus, there will be template lessons for each of the computing concepts with portions 
that can be interchanged to personalize the learning experience.  Drawing from the database, 
which will store the ontology derived from the focus group and the additional ontology entered 
in the CIF, methods derived from the Wizard of Oz study will be used, to match the ontology of 
the participant in the portions of the template lesson to create the tailored lessons.  
 
5.4.3 Procedure and Participants 
 
78 
 
The procedure for creating the learning modules followed the five phases to designing 
eLearning instruction: Analyze, Design, Develop, Implement, and Evaluate as discussed in the 
system design chapter. 
 
5.4.4 Measure 
 
The actual deliverables will serve as the measure, thus providing a yes or no to the 
feasibility to this question. 
 
5.4.5 Results and Analysis 
 
Using the correlated matches of the culture attributes and computing concepts, along with 
the method discussed above, learning modules/lessons were successfully created, which were 
accessed as web pages online. The modules were broken down into four or five pages (the 
experimental module contained one more page than the control).  The first page of the modules 
began on with a culturally situated example that demonstrated an idea, process, or notion that is 
understood within the given culture, but also demonstrated the computing concept. On the next 
page, using the module template, a very basic overview of the computing concept is introduced 
in hopes to lay the foundation for the learner. On the same page, immediately after the 
introduction of the computing concept, the previously culturally situated presented example is 
recalled. This is done in hopes to assist the learner to situate this new knowledge in their mental 
model as it pertains to their already understood and accepted understanding of their culture 
activities (see Figure 15).  The next page, with additional basic details of the computing concept, 
provides a more in-depth understanding of the concept. Also provided, is an additional example 
 
79 
situated within the same culture scenario that was originally introduced but presented in the 
fashion, naming the attributes of the concept (see Figure 16).   
 
 
Figure 15: Learning Module Sample Page 1 
 
 
80 
 
Figure 16: Learning Module Sample Page 2 
5.5 Enhancing the learning experience of adult learners when being introduced to 
computing concepts  
 
5.5.1 Data Collection 
To initiate the study, potential participants were provided a link via email or a message 
sent directly to them via Facebook, a social network site, which connected them directly to the 
information letter. At the bottom of the information letter another link was provided to the 
Culture Inquiry Form (CIF). Participants were asked to complete the form in its entirety.  
Participants were told that the purpose of the form was to obtain demographic information and 
some basic information regarding hobbies, traditions, and industries they have worked in 
 
81 
previously.  An approximate time commitment of less than five minutes was also mentioned.  
Additional instructions regarding part two of the study was also provided. The initial groups 
were told they would receive a link via email to part two of the study a week later. This was due 
to having to populate the database before the initial clusters could be run. The time frame for 
later participants was drastically reduced because the links for part two were then available 
within hours. Participants were informed that the purpose of the lesson was to introduce them to 
a computer concept. Finally, approximate time commitment of the second portion of the study 
was approximated to being about 15 minutes. 
 
5.5.2 Method 
 
Study participants for this portion ranged in age, gender, and education background (see 
Table 12: Full Study Demographics). Female participants accounted for more than 63% of this 
study, of which 46% ranged in the age of 25-34 years of age.  African-Americans accounted for 
more than 62% of all participants. Over 66% of the participants reported their education level as 
ranging from some college to an associates degree. 
 
Table 12: Full Study Demographics 
Demographic Variable N % 
Gender   
female 51 63.75% 
male 29 36.25 
   
Age   
19-24  20 25.0% 
25-34  37 46.25 
35-44  10 12.50% 
45-54  5 6.25 
55-59  4 5.0% 
 
82 
60-64  3 3.75% 
65-74  1 1.25 
   
Ethnicity   
asian 3 3.75% 
black 50 62.50 
hispanic 5 6.25% 
other 1 1.25 
white 21 26.25% 
   
Education   
AS 22 27.50% 
BS  15 18.75 
col 31 38.75% 
ged 9 1.25 
grad 3 3.75% 
   
Totals 80  
 
5.5.3 Control Treatment 
 
The control treatment for this study represents traditional instructions used for 
introduction to computing. One of the challenges for this task was the lack of resources available 
that focused on the targeted audience. The majority of the introduction to computing materials 
the researcher came across was geared towards traditional students enrolled in a degree seeking 
program at an institution of higher learning, or assumed that the user has some computing 
background prior to beginning the lessons. Thus, the researcher sought a control treatment with 
claim and purpose for a similar type target audience, geared towards learners that have no prior 
computing experience.  For this reason, the researcher was lead to selecting Mark Guzdial and 
Barbara Ericson?s (2006) ?Introduction to Computing and Programming in Java: A Multimedia 
Approach? as the control treatment.  Though this text was designed for use in an institution of 
higher learning, it contains a core focus on introducing computing and programming to novice 
learners culturally situated in a multimedia context. Thus, it appeared to be the best-case scenario 
 
83 
in a selecting a tool to compare to C-CAL that had, generally speaking, a related method to reach 
a comparable audience.  
 
5.5.4 System design to support system usability for adult learners 
5.5.4.1 Hypothesis 
 
The learners will deem the design of the system as being usable in respect to usability. 
Independent variable: culture 
Dependent variable: usability factor 
 
5.5.4.2 Method 
 
After participants navigate through the C-CAL system, they are directed to a feedback 
page. The participants were asked to provide their feedback for the C-CAL system, in which 
questions on the usability of the system are explored.  The Technology Acceptance Model 
(TAM) was adapted to determine the perceived ease of use and the perceived usefulness of the 
C-CAL system to gauge usability.  TAM is frequently used in information systems research to 
gauge users perception towards accepting and using technology (Davis, 1989; Davis, Bagozzi, & 
Warshaw, 1989; Gao, 2005). Derived from psychology research of Theory of Reasoned Actions 
(TRA), TAM proposes that perceived ease of use and perceived usefulness of technology are 
predictors of user attitude toward using the technology, subsequent behavioral intentions, and 
actual usage (Gao, 2005). Perceived usefulness (PU) is defined by Davis as "the degree to which 
a person believes that using a particular system would enhance his or her job performance 
(Davis, 1989)". Davis defines perceived ease-of-use as the degree to which a person believes that 
 
84 
using a particular system would be free from effort. TAM has been used in a variety of studies. 
In this study, TAM is used to assess the C-CAL system in its purpose of delivering information 
to and interacting with the user through a computer interface.   
The instrument was designed so that it measured the perception towards the use of the C-
CAL system. The questionnaire, written in English, is adapted from the questionnaires of Davis 
(1993). The primary modification was the rewording of the question items and reducing the 
number of items to five items per construct. Items were removed that were not directly 
applicable to this proof of concept of the C-CAL system and that did not reflect the overall goal 
of the instrument.  Several items in the original Davis questionnaire (1993) were in regards to 
the participant?s perception of the tool in relation to their jobs.  All such questions were removed 
in the adapted version employed.  These questions were seen as irrelevant to the study. All the 
items utilized a five-point Likert scale ranging from ?strongly agree? to ?strongly disagree? with 
a middle neutral point. 
 
5.5.4.3 Procedure & Participants 
 
Upon completion of the learning module, the participants were redirected to a survey 
hosted by Survey Monkey? where they were provided with five questions regarding perceived 
usefulness and five questions regarding perceived ease of use. They were asked to fill out the 
survey to provide a subjective evaluation of the system performance. Because the study is based 
on a within study design, if participants conduct the experimental version of the study first, they 
are directed to the survey to provide feedback and then redirected to the control version of the 
study, and finally another feedback page for the control version. This occurs in reverse if the 
participant begins with the control version first.  
 
85 
 
5.5.4.4 Measure 
 
Descriptive statistics, such as the mean, the standard deviation, and the range, were 
collected from the survey.  Cronbach?s alpha was used to measure the reliability of the 
instrument. Further inferential statistical analysis was conducted by using the t-test and the 
Kolmogorov-Smirnov test (K-S-test) analysis to determine if a statistical difference exists 
between the control and experimental groups of the study. The t-test assesses whether the means 
of two groups are statistically different from each other. The K-S test is a nonparametric test, 
which makes no assumption about the distribution of data, for the equality of continuous one-
dimensional probability distributions (Kirkman, 1996). The K-S test quantifies a distance 
between the maximum vertical deviation of the two cumulative proportion or curve of the 
treatment versus the control, as the statistic D. Given the statistic D, a P-value is also able to be 
determined, which is ultimately use to determine if there is a statistical significance between the 
treatment and control group by evaluating the distributions considered under the null hypothesis. 
 
5.5.4.5 Results & Analysis 
 
After removing all erroneous and incomplete entries from the data set, the data was 
organized for analysis. The remaining thirty complete entries were assessed and are summarized 
below.  
5.5.4.5.1 Perceived Usefulness 
 
 
86 
Respondents were asked via five items how useful they perceived the C-CAL system. 
Below, their response of the experimental treatment (Table 13) is compared to that of the control 
treatment (Table 14).   
a. On average, responses for question one revealed that 86.7% and 
76.6% of participants of the experimental treatment and of the 
control strongly agreed or agreed that they found the program 
useful, respectively.  
b. On the second question,  ?using this program would make it easier 
to learn computing concepts,? 90% and 83.3% of participants of 
the experimental treatment and of the control, respectively, 
strongly agreed or agreed that they found the program useful.   
c. On the third question,  ?this program appears to do everything I 
would expect it to do,? 80% and 73.3% of participants of the 
experimental treatment and of the control, respectively, strongly 
agreed or agreed that they found the program useful.    
d. On the fourth question, ?using this program would enhance my 
effectiveness in learning computing concepts,? 86.6% and 76.6 of 
participants of the experimental treatment and of the control, 
respectively, strongly agreed or agreed that they found the program 
useful.  
e. On the final question, ?This program would make the things I want 
to accomplish easier to get done,? 90% and 76.6% of participants 
 
87 
of the experimental treatment and of the control, respectively, 
strongly agreed or agreed that they found the program useful.  
 
88 
 
 
 
Table 13: Perceived Usefulness Experimental Results 
EXPERIMENTAL 
PERCEIVED USEFULNES 
Answer 
Options 
Strongly 
Agre 
 Agre  Neutral Disagre 
Strongly 
Disagre 
Response 
Count 
I found this 
program 
useful 
15 50.0% 11 36.7% 4 0 0 30 
Using this 
program 
would make it 
easier to 
learn 
computing 
concepts 
16 53.3% 11 36.7% 2 1 0 30 
This program 
apears to do 
everything I 
would expect 
it to do. 
14 46.7% 10 3.3% 6 0 0 30 
Using this 
program 
would 
enhance my 
efectivenes 
in learning 
computing 
concepts 
13 43.3% 13 43.3% 3 1 0 30 
This program 
would make 
the things I 
want to 
acomplish 
easier to get 
done. 
14 46.7% 13 43.3% 3 0 0 30 
answered questions 30 
skip questions 0 
 
89 
 
 
Table 14: Perceived Usefulness Control Results 
CONTROL 
PERCEIVED USEFULNES 
Answer 
Options 
Strongly 
Agre 
 Agre  Neutral Disagre 
Strongly 
Disagre 
Response 
Count 
I found this 
program 
useful 
10 3.3% 13 43.3% 6 1 0 30 
Using this 
program 
would make it 
easier to learn 
computing 
concepts 
10 3.3% 15 50% 4 1 0 30 
This program 
apears to do 
everything I 
would expect 
it to do. 
9 30.0% 13 43.3% 7 1 0 30 
Using this 
program 
would enhance 
my 
efectivenes 
in learning 
computing 
concepts 
10 3.3% 15 43.3% 4 1 0 30 
This program 
would make 
the things I 
want to 
acomplish 
easier to get 
done. 
11 36.7% 12 40.0% 6 1 0 30 
answered questions 30 
skip questions 0 
 
 
At first glance, the initial findings of perceived usefulness show there is little to no 
difference between respondents? perception of the experimental treatment and the control.  There 
appears to be a slight preference for the experimental treatment, however, it is not clear if there is 
a statistical difference, thus, an additional analysis was conducted.  Table 15 displays the mean, 
 
90 
the standard deviation, and the minimum and maximum of the experimental treatment and the 
control treatment and the results of the performed t-test.   Assuming the null hypothesis, the 
probability of this result is 0.184. Using the conventional significance p- value of 0.05 we are 
unable to reject the null hypothesis, thus, indicating there is no statisical difference between the 
experimental treatment and the control for perceived ease of use.  
 
Table 15: PU Analysis 
PU  
N 30 
Exp PU Mean 8.33 
Con PU Mean 9.567 
  
Exp PU SD 3.437 
Con PU SD 3.64 
  
Exp PU Max 17 
Con PU Max 20 
  
Exp PU Min 5 
Con PU Min 5 
  
Exp PU Range 12 
Con PU Range 15 
  
t-test= 0.184 
 
The t-test assumes normal distribution. Given the increase risk of error with a t-test on 
non-normal distributions and when datasets are not sufficiently large a K-S-test was also 
conducted.  The K-S-test was conducted on each question that pertained to perceived usefulness. 
In addition, it was conducted on the sum of the responses of each question.  In conducting the K-
S-test it is confirmed that both datasets do not show a normal distribution. Table 16 shows the 
statistic D and the corresponding p-value for each question regarding perceived usefulness and 
 
91 
the statistic D and corresponding p-value for the sum of each question.  The p-values for each of 
the K-S-tests conducted are all well above the conventional significance value of 0.05.  The 
results of the K-S-test of each question and of the sum of questions confirm the t-test findings 
and the initial assumption of no significant difference between the experimental treatment and 
the control. Figure 17 shows the cumulative fraction plot that displays how the data is 
distributed.  
 
Table 16: PU K-S-Test Results 
PU D P-value 
I found this program useful 0.167 0.760 
Using this program would make 
it easier to learn computing 
concepts 0.200 0.5370 
This program apears to do 
everything I would expect it to 
do. 0.167 0.760 
Using this program would 
enhance my efectivenes in 
learning computing concepts 0.100 0.970 
This program would make the 
things I want to acomplish 
easier to get done. 0.133 0.9360 
SUM 0.167 0.76 
 
 
 
92 
 
Figure 17: PU K-S-Test Plot 
5.5.4.5.2 Perceived Ease of Use 
 
Respondents were asked via five items to express their perceived of ease of use of the C-
CAL system. Below, their response of the experimental treatment (Table 17) is compared to that 
of the control (Table 18).   
a. On average their responses for the first question ?Learning to use this 
program would be easy to me,? 90% and 83.3% of participants of the 
experimental treatment and of the control strongly agreed or agreed that 
they found the program useful respectively. 
b. On the second question, ?I would find it easy to get this program to do 
what I want it to do,? 83.3% and 80% of participants of the experimental 
treatment and of the control, respectively, strongly agreed or agreed that 
they found the program useful.   
5 10 15 20
.9
.7
.5
.3
.1
KS?Test Comparison Percentile Plot
X
Percentile
 
93 
c. On the third question, ?My interaction with this program would be clear 
and understandable,? 90% and 83.3% of participants of the experimental 
treatment and of the control strongly agreed or agreed that they found the 
program useful, respectively.  
d. On the fourth question, ?It would be easy for me to become skillful at 
using this program,? 96.6% and 83.3% of participants of the experimental 
treatment and of the control, respectively, strongly agreed or agreed that 
they found the program useful.  
e. On the final question, ?I would find this program easy to use,? 83.3% of 
participants of the experimental treatment and of the control, strongly 
agreed or agreed that they found the program useful, respectively. 
 
94 
 
 
Table 17: Perceived Ease of Use Experimental Results 
EXPERIMENTAL 
PERCEIVED EASE OF USE 
Answer 
Options 
Strongly 
Agre 
 Agre  Neutral Disagre 
Strongly 
Disagre 
Response 
Count 
Learning to use 
this program 
would be easy 
for me 
17 56.7% 10 3.3% 3 0 0 30 
I would find it 
easy to get this 
program to do 
what I want it 
to do 
16 53.3% 9 30.0% 5 0 0 30 
My interaction 
with this 
program would 
be clear and 
understandable 
12 40.0% 15 50.0% 3 0 0 30 
It would be 
easy for me to 
become skilful 
at using this 
program 
19 63.3% 10 3.3% 3 0 0 30 
I would find 
this program 
easy to use 
14 46.7% 11 36.7% 5 0 0 30 
answered questions 30 
skip questions 0 
 
 
95 
 
Table 18: Perceived Ease of Use Control Results 
CONTROL 
PERCEIVED EASE OF USE 
Answer Options 
Strongly 
Agre 
 Agre  Neutral Disagre 
Strongly 
Disagre 
Response 
Count 
Learning to use 
this program 
would be easy 
for me 
15 50.0% 10 3.3% 5 0 0 30 
I would find it 
easy to get this 
program to do 
what I want it 
to do 
13 43.3% 11 36.67% 6 0 0 30 
My interaction 
with this 
program would 
be clear and 
understandable 
13 43.3% 12 40.0% 5 0 0 30 
It would be 
easy for me to 
become skilful 
at using this 
program 
13 43.3% 12 40.0% 5 0 0 30 
I would find this 
program easy 
to use 
13 43.3% 12 40.0% 5 0 0 30 
answered questions 30 
skip questions 0 
 
There appears to be a bit more variability between the experimental treatment and the 
control of the perceived ease of use compared to that of perceived usefulness judging by the 
projections of the initial tables.  Though there appears to be more of a range within the individual 
questions regarding percentage, the majority of the respondents seem to favor both systems 
equally.  Additional analysis is conducted to determine if there is a significant difference 
between the two groups. Table 19 displays the mean, the standard deviation, and the minimum 
and maximum of the experimental treatment and the control and the results of the performed t-
test. 
 
96 
Table 19: PEOU Analysis 
PEOU  
N 30 
Exp PEOU Mean 8.1 
Con PEOU Mean 8.63 
  
Exp PEOU SD 3.294 
Con PEOU SD 3.615 
  
Exp PEOU Max 15 
Con PEOU Max 15 
  
Exp PEOU Min 5 
Con PEOU Min 5 
  
Exp PEOU Range 10 
Con PEOU Range 10 
  
t-test= 0.53 
 
Assuming the null hypothesis, the probability of this result is 0.553. Using the 
conventional significance value of 0.05, we are unable to reject the null hypothesis, thus, 
indicating there is no statistical difference between the experimental treatment and the control for 
perceive ease of use.  
The t-test assumes normal distribution. Given the increase risk of error with a t-test on 
non-normal distribution and when datasets are not sufficiently large, a K-S-test  was also 
conducted.  The KS-test was conducted on each question pertaining to perceived usefulness. In 
addition, it was conducted on the sum of the responses of each question.  In conducting the KS-
test, it is confirmed that both datasets do not show a normal distribution. 
Table 20 show the statistic D and the corresponding p-value for each question and the 
statistic D and corresponding p-value for the sum of each question.  The p-values for each of the 
KS-tests conducted are all well above the conventional significance value of 0.05.   
 
97 
 
Table 20: PEOU K-S-Test Results 
PEOU D P-value 
Learning to use this program 
would be easy for me 0.067 1.00 
I would find it easy to get this 
program to do what I want it to 
do 0.100 0.970 
My interaction with this program 
would be clear and 
understandable 0.067 1.00 
It would be easy for me to 
become skilful at using this 
program 0.133 0.9360 
I would find this program easy to 
use 0.033 1.00 
SUM 0.100 0.97 
 
The results of the KS-test of each question and of the sum of questions confirm the t-test 
findings and the initial assumption of no significant difference between the experimental 
treatment and the control. Figure 18 shows the cumulative fraction plot, which displays how the 
data is distributed. 
 
 
98 
 
Figure 18: PEOU K-S-Test Plot 
 
The solid line represents the data for the experimental treatment and the dotted line 
represents the data for the control treatment responses.  Using a logarithm scale for the x-axis it 
is clearer to see where the majority of the data is positioned. PU in Figure 17 appears to show 
more of a non-normal distribution than PEOU in Figure 18. Both plots confirm previous findings 
of no statistical difference between the two treatment groups.  
 
5.5.5 Demonstrating cognitive understanding 
5.5.5.1 Hypothesis 
 
The participants will be able to immediately apply their knowledge by constructing their 
own culturally based example of the computing concept they just learned. 
 
5.5.5.2 Method 
 
6 8 10 12 14
.9
.7
.5
.3
.1
KS?Test Comparison Percentile Plot
X
Percentile
 
99 
The participants will be required to create an example of the computing concept covered 
in the given lesson with all its parameters. 
 
5.5.5.3 Procedure & Participants 
 
After participants engage in the learning experience via the learning module they are 
given an opportunity to demonstrate their knowledge. Given a definition, description, and 
example of the computing concept, the participant created their own example.  Participants in 
this study were recruited from online via email or media groupings such as Facebook.  This 
study targeted adult learners with limited to no classroom experience beyond a high school 
diploma and/or those removed from engaging in the learning process during the course of the 
technology age. 
 
5.5.5.4 Measure 
 
Bloom?s Taxonomy was applied to assess the results of the examples generated by the 
participants in the C-CAL portion entitled ?Your Turn.?  The results were coded using the top 
level of the cognitive process dimension, based on Thompson et al (2008) categories of the 
revised taxonomy, to assess participants? demonstrated understanding of the material they just 
learned. The cognitive process dimension across the top of the level consists of six levels defined 
as Remember, Understand, Apply, Analyze, Evaluate, and Create (Forehand, 2005).  Thompson 
et al (2008) description of these cognitive categories was used, in which they provide an 
interpretation along with examples specific to computing geared towards computer science 
 
100 
educators as our guide to assessing participants? inputs in hopes this will better equip us in 
assessing the participants cognitive understand of the material.  
 
5.5.5.5 Results & Analysis 
 
The ?Your Turn? portion of the study focused specifically on the participants? ability to 
demonstrate understanding of the computing concept they just learned and to use it to create their 
own culturally situated example of the same concept.  All respondents had to agree to the 
consent document to get access to begin the C-CAL study. Thus, it was not needed to begin this 
portion of the study.  Skip logic was not added, thus, participants could choose to complete all 
parts or no parts of the ?Your Turn? portion of the study.  Respondents were not obliged to make 
a selection. The results were filtered based upon completion. Therefore, if participants? entries 
were left completely blank, their entry was filtered out. There was also a means to filter out 
responses with large blocks of missing data. After cleaning the data, 25 pairs remained. Because 
the study was conducted as a within study, it resulted in 50 valid entries which were analyzed.  
The computing concept variables, portion of the ?Your Turn? did not have sufficient entries for 
both the control and experimental for analysis, so it was removed. This can be attributed to 
participants not returning to complete the study or beginning the study and then opting not to 
complete both parts. It was observed that, in the randomization, participants that began the study 
as a part of the control group had a substantial higher withdrawal rate either.   
Every lesson for each concept ended with participants being given the opportunity to 
demonstrate their understanding. This was called ?Your Turn?. The participants were asked to 
come up with their own illustration of the computing concept. They had to create an example of 
the computing concept in which they had to specify all of its parameters or attributes. Table 21 
 
101 
and Table 22 are samples of the ?Your Turn? responses of the control treatment group and the 
experimental treatment group, respectively, of which the computing concept was functions. For 
the functions concept participants had to supply a name, a set of parameters, and a return value 
for their function.  As you can see in the tables below, the quality of their responses varied 
greatly. Further analysis was conducted to better assess the findings. 
 
Table 21: Results for "Your Turn" for the Functions Concept -Control Group 
CONTROL 
functionID fName fparameters freturn 
1 Creating a triangle 
Gp forward in a diagonal directionb for 30 steps, then 
turn around and walk diagonaly forward in the 
oposite direction, then turn right and go forward 
until you reach your starting point Triangle 
2 cuting a mat board 
determine materials neded by (1) measuring (2) 
resources available framed picture 
5 hairdreser 
firstaval thisis the step to wash the hair . you drap the 
customer you make sure they don't wait than you 
wash. a god job 
6 Party Planing 
Geting the necesary drinks and fod, Inviting the 
necesary guests, Finding the necesary venue one great party 
7 turning on your car 
1) unlock driver side dor 2) open dor and sit 3)take 
key and place it in the ignition 3) turn your key away 
from you 4) wait to hear your car fuly start then shift 
into Drive basic skil 
8 
Art of 
Comunication 
noun + verb = sentence. A naming word + Another 
word that aserts something about the naming word 
and voila! 
A complete 
sentence 
9 Rectangle 
Go forward 10 steps, then turn right, Go forward 20 
steps, then turn right, Go forward 10 steps, then turn 
right, Go forward 20 steps, then turn right, Draws a rectangle 
10 Painter 
Buy paint, Put paint on surface to be painted, Clean 
up brushes Painted surface 
11 adition ad one to one 2 
 
 
 
102 
 
 
Table 22: Results for "Your Turn" for the Functions Concept- Experimental Group 
EXPERIEMNTAL 
1 
sending an 
email log-in, new-mesage, write, find sender, send sent email 
2 
Responding to 
studies 
decide if you want to help that person calculate 
risks involved decline or acept solicitation 
satisfaction that time was wel 
spent 
4 
Babysiting 
Children 
1. Be very gentle and caring towards the children. 
2. Folow their parents instructions. 3. Make sure 
you have a list of emergency contacts. Hapy children and parents. 
6 Busines Employe's God Busines 
7 
Living on your 
own 
1)moving out of your parents home 2) buying your 
own grocery?s 3)suporting yourself independence 
8 
Art of 
Comunication 
II 
n+v and n+v = cs To every sentence ad a 
conjunction (and, or, nor, but, yet, so,) plus 
another set of noun and verb. Compound Sentence 
9 Teacher 
Explain concept, give asignment, grade 
asignment 
Taught a group of high schol 
students 
10 Seder Buy sed, Load sed, Drive tractor to spread sed 
11 Student Take notes and study for test God grades 
 
 
Similarly, Table 23 and Table 24 are samples of the ?Your Turn? responses of the control 
treatment group and the experimental treatment group, respectively, of which the computing 
concept was objects. For the objects concept, participants had to supply an object name, some 
attributes associated with the object and a method for the object.  As you can see in the tables 
below the quality of their responses varied greatly. Further analysis was conducted to better 
assess the findings. 
 
103 
 
Table 23: Results for "Your Turn" for the Objects Concept- Control Group 
CONTROL 
objectID oName oAtr oMethod 
17 Walet Leather 
Discretely transport personal 
items 
16 Steak juicy, brown, large fry it or bake it and eat it 
19 learningT learningTe LearningTest 
20 A job 40 hours a wek it wil give me pice of mind 
21 Dres Blue Sewn 
22 auto blue transport me 
23 medication smal round water pil swalow the pil 
24 
cokie caled 'Colege 
Days' 
oatmeal raisin with choclate chips 
and pecans it can be sold or eaten 
25 papers medium, white, lined writen on, fold, picked up 
26 Phone smal/compact 
comucation/conect with others 
by dailing 
33 motorbike two whels acelerate 
41 bal round bounce 
42 a batery smal. round, with opsite ends 
generate power for an eletric 
device 
43 home 3 bedrom provides shelter 
44 lamp hologen shine 
45 It is a fruit,mango gren and litle bit of yelow 
First of al it neds to be planted 
and the ground and wait a couple 
years before you can have it ready 
to eat. 
 
 
104 
 
Table 24:Results for "Your Turn" for the Objects Concept -Experimental Group 
EXPERIMENTAL 
objectID oName oAtr oMethod 
16 CD Spindle Round, Plastic 
Neatly stack multiple compact 
disks 
15 Jeans Size 7, color dark blue 
Make you lok thiner while 
wearing them 
19 job 40 hours a wek give me a pice of mind 
20 Frame 8x10 place a picture within it 
21 computer laptop help me gather info 
22 clasmate M loves to have fun taught to dance 
23 
musical baby 
swing beige and soft 
play music and swing in 2 
direction 
24 Bentley car, red, convertable, slek it drives, stops, plays music 
25 Haven Camp for HIV + Kids 
Having fun in a safe place / 
teaching healthy living,etc. 
28 ibruprofen round, pink, hard stops imflamatory 
29 pen 
round, with a point and is about 5in 
long 
helps put words on a page, writes 
with ink, or it can draw a picture 
30 celphone Samsung, blue, flip style 
make cals, provides date and 
time, take pictures 
31 botle blue can be filed 
32 Lesly F. Maniga 
He is one of the most famous 
haitian that stil alive. 
He is very educated and he can 
rebuild the haitian education 
system. 
 
 
Following a similar analysis convention as discussed in Thompson et. al (2008), a team 
of computer scientists, those that were instrumental in conceptualizing the learning modules, 
were assembled to code and classify the responses using Blooms Taxonomy. Because the 
learning module presented a very basic understanding of the concepts and the manner the ?Your 
Turn? portion was framed, it was decided that only the lower three levels of Blooms Taxonomy, 
Recall, Understand, and Apply, would be applicable in assessing the responses. Each person was 
given a breakdown of Thompson et. al (2008) cognitive categories as seen below:  
 
105 
Remember is defined as ?retrieving relevant knowledge from long-term memory? 
(Anderson et al. 2001). In the revised taxonomy, this category includes recognizing and 
recalling. This is interpreted in programming assessment terms to mean: 
1. Identifying a particular construct in a piece of code; 
2. Recognizing the implementation of a subject area concept; 
3. Recognizing the appropriate description for a subject area concept or term; 
4. Recalling any material explicitly covered in the teaching program. This might be 
factual knowledge, the recall of a conceptual definition, the recall of a process, the recall of an 
algorithm, the recall of a design pattern, or the recall of a particular algorithm or design pattern 
implemented as a solution to a specific problem in exactly the same context as a classroom based 
exercise. 
 
Understand is defined as ?constructing meaning from instructional messages, including 
oral, written, and graphical communications?. In the revised taxonomy, this category includes 
Interpreting, Exemplifying Classifying, Summarizing, Inferring, Comparing, and Explaining. 
This is interpreted in programming assessment terms to mean: 
1. Translating an algorithm from one form of representation to another form; 
2. Explaining a concept or an algorithm or design pattern; 
3. Presenting an example of concept or an algorithm or design pattern. 
 
Apply is defined as ?carrying out or using a procedure in a given situation?. In the revised 
taxonomy, this category includes Executing and Implementing. This is interpreted in 
programming terms to mean: 
 
106 
1. That the process and algorithm or design pattern is known to the learner and both 
are applied to a problem that is familiar, but that has not been solved previously in the same 
context or with the same data or with the same tools; or  
2. That the process and algorithm or design pattern is known to the learner and both 
are applied to an unfamiliar problem 
 
In addition, the analyzers were also supplied with the definitions of functions and objects 
that the participants were presented with in the study (see Appendix F). Each analyzer was 
instructed to code the responses as a 1 if they thought the learner demonstrated that cognitive 
category and 0 if they did not.  As a caveat, the analyzers were cautioned that the participants 
were novice learners, who completed these responses immediately after completing the learning 
module. Table 25 and Table 26 present the descriptive statistics and the probability value as a 
result from the t-test for the objects and functions, respectively.  
Table 25: Object Concept Analysis 
Objects  
N (exp/con) 16/15 
  
Exp Obj Mean 2.13 
Con Obj Mean 2.063 
  
Exp Obj SD 1.060 
Con Obj SD 1.181 
  
Exp Obj Max 3 
Con Obj Max 3 
  
Exp Obj Min 0 
Con Obj Min 0 
  
t-test= 0.86208758 
 
 Table 26: Functions Concept Analysis 
Functions  
N (exp/con) 13/14 
  
Exp Func Mean 1.786 
Con Func Mean 1.462 
  
Exp Func SD 1.12 
Con Func SD 0.87 
  
Exp Func Max 3 
Con Func Max 3 
  
Exp Func Min 0 
Con Func Min 0 
  
t-test= 0.41324582 
 
 
 
107 
In addition, we also conducted a KS-test as presented in Table 27 as an additional step to 
account for the non-normal distribution. Figure 19 and Figure 20 depict the K-S-Test comparison 
percentile plot as a visual of the distribution for the objects response and the functions response 
respectively. 
Table 27: Results for "Your Turn" K-S-Test Analysis 
KS-Test D P 
Functions 0.2308 0.828 
Objects 0.133 0.98 
 
 
 
Figure 19: Objects K-S-Test Plot 
 
0 1 2 3
.8
.6
.4
.2
KS?Test Comparison Percentile Plot
X
Percentile
 
108 
 
Figure 20: Functions K-S-Test Plot 
 
In observing the results from the t-test and the p-value of the KS-Test, we can conclude 
there is no statistical difference between the experimental treatment and the control treatment for 
the ?Your Turn? responses. In understanding the context of which the ?Your Turn? was 
completed, the researcher realizes there are several factors that played into the responses. 
Because this was conducted as a within study, some participants could have experienced fatigue 
as they moved into the second test. Also, participants were asked to demonstrate understanding 
on concepts they were still processing. In general, for many learners, more time is required for 
the learned information to be absorbed and computed before they can successfully apply it in a 
useful manner. 
0 1 2 3
.8
.6
.4
.2
KS?Test Comparison Percentile Plot
X
Percentile
 
109 
Chapter 6 
 
Conclusion 
 
 
6.1 Summary 
The C-CAL system gave rise to a new methodology and design that allows for the use of 
culture in the introduction of computing concepts to adult learners that are not formally educated. 
Its main goal is to provide a means to capture a learners culture(s) of participation; organize them 
into workable clusters based upon their culture of participation; use the dominate culture of each 
cluster and link it with a computing concept; and then create learning modules based upon the 
association created of the dominate cluster and the computing concept.  Through 
experimentation, the C-CAL system has shown to be a viable alternative to introducing 
computing concepts to adult learners. The combination of the Culture Inquiry Form  (CIF) and 
the Application Quest? clustering algorithm proved to be an informative means to identify a 
persons culture and organize and present it in a fashion that it can be used as an input for future 
design endeavors. Using an iterative design process, two methodologies were developed by 
which culture and computing can be correlated and then a learning module could be crafted. 
There was no statistical difference between the C-CAL system and the control in regards to 
perceived ease of use and perceived usefulness, thus indicating the C-CAL system performed 
just as well as the control. However, C-CAL was consistently rated higher than the control on 
along all measures. 
 
110 
This manuscript began by asking three key questions that led to the development of the 
C-CAL system.  
First, the researcher asked, if one?s culture of participation can be identified and captured. 
In attempting to gain the rich culture information captured via ethnographic studies the CIF was 
crafted to capture a holistic view of individuals. The focus groups conducted provided the scope 
of questions that respondents best connected to, the ontology associated with some common 
cultures of participation and in-depth understanding of how participants relate and participate in 
their respective cultures. CIF resulted in as the electronic manifestation of those findings.  
 Second, the researcher asks if culture data mining can be used to create culture 
based learning modules.  To answer this question, three sub-questions were addressed. Can C-
CAL accurately capture a learners? culture of participation and correlating processes? This 
question was answered via a validation study utilizing the Application Quest? clustering 
algorithm. This algorithm proved to be an informed means for capturing and organizing clusters 
of responses based upon the common culture attributes and processes the participants identified.  
Next, can culture processes be matched to the corresponding processes of computing concepts 
and can tailored lessons be designed around these matches?  Using an iterative design process a 
methodological approach and template were forged for the correlating culture and computing and 
for design of learning modules that employ culture in introductory lessons computing concepts. 
Based on the positive findings of the sub-questions and the designed deliverables produced, the 
question of the use of culture data mining in the production of learning modules can be answered 
in the affirmative.   
Finally, the researcher addressed, does culture based learning enhance the learning 
experience for adult learners when being introduced to computing concepts? This question was 
 
111 
also examined in two sub-questions.  Initially, does the design of the system support the system?s 
intended use in terms of usability for adult learners?  Results of the Technology Acceptance 
Model revealed that, on average, over 50% of all participants perceived the C-CAL system to 
being useful and easy to use.  Next researchers had to determine if participants could 
demonstrate cognitive understanding of the concepts they just acquired via their interactions with 
the C-CAL system.  In constructing their own culturally based example of the computing 
concept they just learned, participants were able to demonstrate their ability to recall, understand, 
and apply their knowledge at a rate comparable to that of the control method.  In addition, there 
was no statistical difference found between the C-CAL system and the control. Indicating that 
the C-CAL system performed just as well as current methodology introducing computing 
concepts making it a viable alternative. However, C-CAL outperformed the control across all 
measures. 
 
6.2 Conclusion 
This research puts forth the novel idea of the C-CAL system that embodies the notion of 
a design that meets the needs of non-traditional learners in computing education by the use of 
computing tactics such as data mining and informational retrieval to create a self-directed online 
autonomous learning application.  As a result of experimentation, this experimental concept was 
compared and found to perform just as well, and sometimes slightly better than traditional, non-
culturally engaging methods currently used. Thus indicating the C-CAL system is a feasible 
system to utilize whose benefits can manifest itself in the array of adult learners returning to 
educate them. The nuances of this study basically accumulated into one overarching thought, is 
culture based learning a feasible option to introduce adults to computing and does culture based 
 
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learning enhance the learning experience for adult learners when being introduced to computing 
concept? Can one capture culture, and use it as a design parameter that will prove to being 
beneficial for adult learners in learning computing.  At the conclusion of this study the C-CAL 
system has proven its ability to answer in the affirmative on this question.  Further studies are 
required to assess C-CAL?s full range of capabilities and impacts on learning and the ultimately 
get a statistical gain in C-CAL over the control. 
 
6.3 Contributions 
This research demonstrated that the C-CAL system is a viable alternative to current 
methods of introducing computing concepts to adult learners. The C-CAL system in all its 
components fills a void in making the following contributions to the field of Human Centered 
Computing and Computer Science Education: 
? A practical tool for identifying culture of participation for a group 
o Research in better understanding and connecting with users is ongoing.  In 
the design world there are frequent collaborations and efforts to connect 
with social scientists to gain a deeper understanding of the target audience. 
However, in situations where those connects do not exist, many of our 
technological designers suffer. It is unfeasible and illogical to ask a 
resource restricted project or scientist to spend years collecting the rich 
culture understanding of people that ethnographers have been doing for 
years. Yet is that very knowledge that is needed to ensure that our designs 
are capable of reaching our globally diverse society.  Thus, the Culture 
 
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Inquiry Form is the first step in bringing the two worlds together when it 
would have not been possible otherwise.  
? Modeling a process for the use of culture as a design construct 
o Though there is some research on the significance of culture in computing, 
and an assortment of efforts of how best to account for it in computing 
designs, there are still some challenges of how exactly do we use a holistic 
view of the target audience culture of participation within the design 
construct. This model presents a process by which a computationally 
enriched learning experience can be fashioned around the learner?s culture 
of participation using their own ontology and their views of how things 
relate within that culture context.  
? Introduced a new system for increasing digital fluency for among Adult learners 
o The C-CAL system provides a rich culturally induced learning 
environment, where culture is dictated and defined by the target audience.  
6.4 Future Research 
The C-CAL system was designed as a proof of concept, thus there were several lessons 
learned in the course of this design and discoveries made during analysis that warrant further 
investigation and continued research.   
A portion of this study was conducted as a Wizard of Oz study. After refining a process 
that appears to capitalize fully on the information that can be used in better depicting and 
providing detailed picture of a cultural concept, it is now possible to complete the system design 
such that C-CAL can be a fully autonomous system.  It is also speculated that the replacing of 
the wizard of oz study could eliminate chances of human error that could factor into the 
 
114 
participants perception of the system.  A comparison study on the replacement of the Wizard of 
Oz study with an Information Retrieval design, would be interesting to explore the trade-offs of 
the system with the inclusion and removal of the human element.  
In this study the researcher was fortunate to have conducted a focus group in which we 
obtained much of the background information and ontology associated with various culture 
attributes later used in the study.  Future research would be heavily dependent upon semantic 
relations established in areas of ?add hobby.? Thus the next version of C-CAL should require a 
response from all participants for such options. In additions the same semantic relations should 
be utilized in gaining a better understanding the industries participants have been worked in, in 
the past.  
Future research is needed on testing the C-CAL system with a homogeneous group of 
participants.  Participants in the C-CAL study were from all walks of life various regions of the 
country and from abroad. However there is much to be learned on the impact of C-CAL on a 
group having range of commonalities. For example, it would be interesting to conduct the study 
with a group of construction workers.  
The Technology Acceptance Model (TAM) has been expanded to the Unified Theory of 
Acceptance and Use (UTAUT) Model where the core constructs are performance expectancy, 
effort expectancy, social influence, and facilitating conditions that were not featured in this study 
but are worthwhile in future research (Venkatesh et. al., 2003).  
Though the C-CAL system and all its components was designed primarily for culturally 
relevant computing for adult learners, it uses can been seen in other domains. The ability to 
better connect with a target audience is a universal need that spans product development, military 
strategy and various other markets.  Further research is needed on this matter.  
 
115 
 
6.5 Final Thought 
 
?Start where you are. Use what you have. Do what you can?- Arthur Ashe 
This research endeavor began with a man who wanted to be a grocery bagger. The 
researcher watched a man enter a grocery store to apply for a job as a bagger. As the man was 
directed to a nearby computer to complete an application, she watched on as disappointment and 
defeat came over his face and he turned and left the store.  The man looked to be in his mid-
forties, his education status and academic background is unknown, his acquired skills and area of 
expertise is also unknown.  However what is assumed is that he lacked the basic skills needed to 
seek employment that would impact his earning capacity and change his life.   
In December 2007, the US had officially entered into a recession.  This resulted in job 
loss placed at 8.4 million (Bureau of Labor Statistics, 2010). The hardest impacted group over 
the course of the recession, and the group that still struggles to find employment, are adults who 
possessed, limited to no formal education beyond high school.  As a result it is probable, that 
millions of Americans have found themselves in the same position as the researcher?s want-a-be 
bagger did years ago. Many of them will turn to education, willing or reluctantly. Though armed 
with a lifetime of knowledge that now seems void and useless, these digital immigrants will have 
to learn fast beyond, the basic skills they have acquired from past employment or their 
occasional web surfing or social networking or face marginalization.  
The C-CAL system is an effort to reach out to this growing sector of our population. It is 
an attempt to help them start where they are and use what they have, with the lifetime of 
knowledge and skills they already have there by building confidence and lowering the entry bar. 
 
116 
It is an attempt to provide guidance into a new way of thinking that can lead to them being 
contributors of our ever changing society. The researcher understands that in looking at the 
bagger story it would have suffice to simply embark on a design that would introduce basic 
computer skills (such as this a mouse, this is a screen, etc). However as discussed by the National 
Academies of Science, those skills are not enough to sustain one in this society. Fluency, the 
ability to continue on with the learning  process as technology evolves, and an understanding that 
computational thinking is already embedded in their current knowledge set can empower people. 
 
117 
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Appendices 
 
 
Appendix A: Culture Participation Focus Group Protocol (CPFGP) 
Adult Learners Focus Group Protocol    
  May 2008 
Culture of Participation 
 
Overview 
 
Goal: The goal of this focus group is to help us discover the culture of 
participation that is most prominent for these adult learners. Thus, we seek 
to get a better understanding of the knowledge and experiences these 
learners bring to their learning experience.  The goal is to capture the 
understanding and language of the cultural attributes that these adult 
learners do as part of their individual or joint activities, events, and 
planning.  We are interested in discovering the general and particular 
contexts for computing relevant teaching and practice, so we are 
encouraging adult learners to talk freely and describe and discuss their 
interactions and strategies.   
 
Timeframe: 1-2 hours 
 
Guidelines: 
 
Instructional Sheet 
Welcome participants and hand out informational sheets as they come in. 
The moderator will read the information sheet out loud, as participants 
follow along on their own copy.  Each participant must agree to participate 
and to being recorded prior to beginning the study and recording.  
 
Establish rapport. 
You want the participants to feel as comfortable as possible.  Offer snacks. 
Make sure you introduce yourself and what you are doing at Auburn 
University and how you are part of this study. You should give them a 
simple explanation of the study such as, We are trying to discover the 
different activities things that they participate in that, together or alone, 
that may use the same fundamental concepts of problem solving skills in 
computing.  Computing constraints may be under the surface of their 
favorite activities, so we want to learn about the ones that are most 
 
127 
obvious to you, and we will want to ask about all kinds of other activities 
that you participate in. 
 
Emphasize that the interview does not have any right or wrong answers, 
does not count towards grades, and is separate from the school. Explain 
that we will record the interviews with digital voice recorders so we can 
remember exactly what they say and look back when they are all talking 
together. Make sure to tell them that the audio is for research purposes and 
they will be given fictitious names when we use the audio records. 
 
Tell them the basic structure of the interview and the timeline. Make sure 
to tell them that they do not have to answer any questions that make them 
uncomfortable, and they can end the interview at any time. Make sure to 
tell them where the lavatories are, and remind them that they will have a 
break about one-half way through the interview. 
 
 
TURN ON THE AUDIORECORDER 
 
HIT RECORD 
 
 
Be sensitive to your interviewee?s situation.  
Remember that people?s lives are private places.  Let people speak as 
much as they want and try not to rush them too much.  People may want to 
add to each other?s stories and descriptions, and that will be wonderful. 
 
Use prompts. 
 
Ask for examples as a way to prompt. Also pick up their language and ask 
what they mean. For example, ?Well, I always estimate, but that?s not 
computing. Ask, ?Tell me more about the times you estimate?? Head the 
conversation to examples of things that they mention that may be on the 
inventory. Ask them to be specific and Give you a problem and the ways 
they approach or solve it.
 
128 
 
Questions 
 
A. Ice Breaker 
 
1. Tell me how things are going for you. 
 
a.              Explore further on specifics of response, if student doesn?t provide details: e.g., if 
student says, ?It?s been a lot harder than I thought,? ask, ?In what ways?? 
 
Note: Follow the subject?s lead on how to handle this question ? in some cases, it might be 
best used as an icebreaker, just to begin the conversation and move on to other 
questions.  However, some subjects will have quite a lot to say, on a range of topics.  In 
this case, don?t be too quick to move off of this question ? explore all of the issues the 
subject seems willing to get into. 
 
b.              Before moving on to other questions, give subject a chance to say more, e.g., 
?What else is happening with you these days?? 
 
 
 
B. Contexts and activities: 
 
?People participate in all kinds of activities in different places and times. 
We will talk about some of these places and times and ask you to tell us 
about your own experiences. 
 
Home Design and Improvements: 
Have you decorated or made any improvements to the inside or outside of 
your home?  What are they?  Can you describe what you did?  When you 
do it? A favorite story about it? Who do you do it with?  Where? 
What was the most fun about the project?  Most complicated? 
 
What needs or challenges have you had in making your home ?work? for 
you? How have you addressed them? 
 
*Hobbies, Collections: 
Do you have hobbies?  What are they?  Can you describe what you do?  
When you do it? A favorite story about it? Who do you do it with?  
Where? 
Do you have a collection?  Tell me about it? How do you learn about your 
collection?   
 
*Special/favorite Activities/Leisure  
 
 
129 
How do you like to spend your free time? Family time?  How do you 
prepare for those events?  
 
Any special holidays or celebrations? What do they involve? 
 
Out of school or home activities? Play games? Play or watch sports? 
Courses you take?  Arts or music you watch or participate in? Programs 
you participate in? Do you do anything special on weekends or in the 
summer?  
 
Do you play games? Which ones? With whom? When? How do you get 
new games to play? 
 
Cooking: 
Does anyone in the family cook?  Who? How often? What are your 
favorite dishes to cook?  Can you tell me about the recipes? Any favorite 
cooking stories? Do you cook for parties, charity events?  A large number 
of people? What is that like?  What do you prepare?  Tell me about a time 
you cooked a large quantity? 
 
 
Shopping: 
What kind of things do you like to shop for?  Food? Clothing? Tools? 
Browse? Tell us about some shopping excursions and stories? 
 
Work: 
What kind of work experiences did you have in the past?  Was training 
necessary to get that job? Are there any favorite stories from work? 
 
Planning travel: 
Do you travel locally or long distance? What travel?  How do you plan for 
it?   
Tell me about a trip you have taken or are planning. Describe how you 
think about it?   
Do you commute to work or to school? 
 
Special Family Events: 
What are the most special events to you? Can you tell us about them and 
how you prepare for them? 
 
*Communication Structures and Patterns 
How do you keep in touch with family and friends? 
Relatives live close by or some distance away? 
Do you use cell phones, land lines, email, Internet, cards and letters, face-
to-face to arrange and meet with them? 
 
 
130 
 
 
End-of Interview Questions: 
 
 
Is there anything else you think we should know? 
 
 
 
 
 
131 
 Day 2: Full Group 
 
A. Strategies 
1. What would you say has been the most difficult thing here for you so far? [Students might 
mention non-academic difficulties. Make sure to explore further to get at academic 
difficulties.] 
 
a.              How did you handle (or how are you handling) that? 
 
The following sub-questions can be asked if time permits. 
 
b.              What else have you found difficult? 
 
                                       i. How have you handled that? 
 
c.              Do you have strategies for handling difficult situations? 
 
                                       i. Explore further for specific examples. 
 
2. What?s been easy for you here so far? 
 
a.              Explore further if subject doesn?t elaborate. 
 
 
B. Task 
 
Teach Task 
 
Instruct participants to think about their favorite activity. Think 
about why it?s their favorite.  
 
Learning Sources 
How did you learn or get involved in that activity/task? 
How often do you participate in that task? Can you name some times 
when you used that activity? Can you tell me more about that?  Describe a 
day or time to me?  A story? 
 
Then explain to the group how to do some part of that activity or how 
best to engage in that activity.  Give them 15 minutes to write it down, 
and then each person will report out to or teach the group how to do 
this activity. We will give them a worksheet to help organize their 
thoughts and let them know we have paper, pens/pencils, etc 
available. 
 
C. Computing in a minute story 
 
 
132 
Can each of you tell me about an experience you had with computing that 
reminded you of some other activity that you do?  It can be from school or 
home, positive or negative, recent or distant past. Try to tell it in one 
minute.  
 
Prompts: Have a story about computing in your own life prepared and tell 
it in less than a minute. For example: ?When I lived in New Jersey I 
wallpapered the kitchen in my apartment with a large patterned paper. I 
had to measure the walls and calculate the number of rolls I needed. It was 
complicated because of the length and width, plus the length of the pattern 
and getting it to line up evenly down each panel. The paper was expensive 
and I was on a budget so I didn?t want to purchase too much.  The people 
in the store were really helpful. I had to match the design on the paper 
with the measurements I took of the walls. I felt good that I finished the 
wall and got it right and didn?t have too much extra paper left over.? 
 
D. What is computing to you 
 
Can you think of any other time you might use computing that we 
have not mentioned? 
Start with simple computing and work up: 
Here is a list of kinds of computing.  Can you tell me a time when you do 
them? (Show list that includes the following kinds of computing 
constraints and an example for each: data structures, condition statements, 
variables, odds, logical reasoning. 
Abstraction and decomposition in tackling a large complex task. 
Looking up a name in an alphabetically sorted list 
? Linear: start at the top 
? Binary search: start in the middle 
? Standing in line at a bank, supermarket, customs & immigration 
? Performance analysis of task scheduling 
? Putting things in your child?s knapsack for the day 
? Pre-fetching and caching 
? Taking your kids to soccer, gymnastics, and swim practice 
? Traveling salesman (with more constraints) 
? Cooking a gourmet meal 
? Parallel processing: You don?t want the meat to get cold while 
you?re cooking the vegetables. 
? Cleaning out your garage 
? Keeping only what you need vs. throwing out stuff when you run 
out of space. 
? Storing away your child?s Lego pieces scattered on the LR floor 
? Using hashing (e.g., by shape, by color) 
? Doing laundry, getting food at a buffet 
? Pipelining the wash, dry, and iron stages; plates, salad, entr?e, 
dessert stations 
 
133 
For any they choose: 
When? 
How often? 
Can you tell me about a time you remember doing this? 
 
Closing 
End the interview by thanking them for their time and asking if they have 
any questions for you. Recheck their contact information and ask if they 
would be interested in participating in other research and activities.  
 
 
134 
 
Appendix B: Culture Participation Survey 
 
Culture Participation Survey
Please respond to the following questions by marking next to the response that best describe you
Male Age:20-24 yearsFamily size:
Female 25-34 years Number of adults in 
35-44 yearshouse (18 or older)
Ethnicity: 45-54 years
American Indian and Alaska Native55-59 years Number of children  (0-17)
Asian 60-64 years
Black or African American 65-74 years
Hispanic or Latino75-84 years
Native Hawaiian and Other Pacific Islander 85 years and over
White or Caucasian
Industry (Please check the job category(s) that best describe your past job experience(s))
Accounting/ Auditing/ Financial ServicesEducation/ TrainingLibrary
Advertising/ Marketing/ Public RelationsEmployment Services/RecruitingLobbying/ Grass Roots/Advocacy
Aerospace/ Aviation EngineeringMaintenance/ Repair
Agriculture/ Forestry/ FishingEnvironmentalManufacturing/ Electronics
ArchitectureFashion/ ModelingMedia/ Publishing/ Journalism
Arts/ EntertainmentGovernment/ MilitaryNonprofit/ Charitable
AssociationsGraphics/ DesignOther
Automotive/ Motor Vehicle/ PartsHealthcare/ MedicalPrinting
Banking High Tech / ITReal Estate
Beauty/ Personal CareHospitality/ Tourism/ TravelRestaurant/ Food Service
Biotechnology/ PharmaceuticalHuman ResourcesSales/ Retail/ Wholesale
Communications/Public RelationsInsuranceScience
Computer-Hardware/SoftwareInterior Design/ FurnishingsSocial Services
Construction/ Trades International/ International TradeSports/ Fitness
Consulting ServicesInternet/ E-CommerceTelecommunications
CounselingLandscaping Transportation/ Logistics 
Defense Law Enforcement/ SecurityUtilities/ Gas/ Electric
Domestic/ Childcare/ EldercareLegal Veterinary/ Animal Care
Economics
Highest levelLess than high school/ no diploma  
Where do you use the computer most often? of educationHigh school diploma (including equivalency)
(Please check all that apply) AttainedSome college, no degree
Own a home computerAssociate degree
Have a PC at work Bachelor's degree
Utilize a PC at another locationGraduate or professional degree
What level do you consider yourself in regards to internet usage:How often do you use the internet for personal use :
No experience Daily Monthly 
Novice Weekly Rarely 
Intermediate Bi-weeklyNever
Expert
Machines, electronic and Computers:
What machines, electronics do you have in your house?
What do you do with the ones you have?
How much time do you spend with the various machines in your home?
Where else do you get access to machines? (friends, work, family members, libraries, schools)
 
135 
 
Appendix C: Culture Inquiry Form  
 
 
 
 
136 
 
137 
 
Appendix D: Hobbies and Traditions Buckets 
 
Hobby Bucket 
Hobies HobyBucket  Hobies HobyBucket 
church church  basketbal sports 
watching 
movies entertainment  sports sports 
swiming sports  golf sports 
crafting arts  church church 
basketbal sports  Hunting sports 
watching TV entertainment  Hunting sports 
reading reading  Hunting sports 
reading reading  fishing fishing 
colector colector  sports sports 
traveling traveling  
watching 
movies entertainment 
watching 
movies entertainment  fishing fishing 
reading reading  watching TV entertainment 
runing sports  
watching 
movies entertainment 
shoping shoping  music entertainment 
also runing sports  flying flying 
sports in 
general sports  
sports in 
general sports 
traveling traveling  
dirt biking, 
girlfriend sports 
fishing fishing  music entertainment 
golf sports  runing sports 
watching TV entertainment  singing arts 
shoping shoping  runing sports 
flying flying  tenis sports 
watching 
movies entertainment  
surfing the 
web entertainment 
church church  
beach, 
boyfriend entertainment 
sports in 
general sports  watching TV entertainment 
formula 
team, 
building col 
stuf   
dirt biking, 
girlfriend  
fishing fishing  sports sports 
 
138 
watching 
movies entertainment  church church 
runing sports  sports sports 
socer sports  
sports in 
general sports 
sports in 
general sports  hiking sports 
sports in 
general sports  coking coking 
sports in 
general sports  sports sports 
Basebal sports  Hunting sports 
reading reading  coking coking 
socer sports  
watching 
movies entertainment 
fishing fishing  golf sports 
swiming sports  socer sports 
dirt biking, 
girlfriend   Triathlons sports 
watching 
movies entertainment  tenis sports 
cars colector  music entertainment 
tenis sports  
surfing the 
web entertainment 
watching TV entertainment  basketbal sports 
dancing dancing  Basebal sports 
basketbal sports  video games entertainment 
reading reading  watching TV entertainment 
traveling traveling  Hunting sports 
tenis sports  shoping shoping 
sports sports  Friends friends 
traveling traveling  working out sports 
beach, 
boyfriend entertainment  theatre arts 
basketbal sports  Friends friends 
 
 
 
Traditions Bucket 
 
139 
 
TraditionsTfamilyTholidaysTreligiousTgamedayTpartyTget2getherTmovieNightTvacationTsharedMealsTdrinkingTstudying
family reunion, major holidaysxx
n/a
holiday dinnersx x
kwanzaa
fireworks with family on 4th of July.xx
. 
Gift exchange with family on 
Christmas day. xx
family reunions on my Grandfather 
birthday xx
Mid-week bible studyx
volunteering on christmas and/or 
thanksgivingx
none
Family Christmas reunion - cookingxx x
Churchx
Familyx
easter egg huntx
annual family vacationsx x
sunday game day/lunchx x
opening presents on Christmas day, 
not any earlierx
party x
Fraternity x
occasional trip to europe to see my 
familyx x
Thanksgiving family reunionx
Family traditionsx
eating at the tablex
Sunday lunch with the whole family!x
drinking x
thanksgivingx
National Lampoon's Christmas 
Vacation at christmas time with my 
family. xx
christmas
dinner as a family once a weekx x
toomers corner, thanksgiving, 
christmasx x
Family get togethers for 
thanksgiving/christmas/new yearsxx
get-to-gethersx
movie nights x
yearly vacation x
Auburn Football x
Family traditionsx
religous trx
family traditionsx
spring Festival x
Sunday Morning Worship Servicex
Homework and studyingx
family traditionsx
tailgating
family traditionsx
family dinners
x
Basketball x
Eating x
Christmas dinnerx
holiday get-togethersx
movies every christmasx x
christmas
family traditionsx
going out with friendsx
furious masturbation
Christmas with the familyxx
family traditions
Christmas breakfastx x
family reunions.x
sunday lunches x
holiday mealsx
Christmas
Holidays togetherxx
Aunts house every year for 
Christmas. Thanksgiving traditions.xx
Thanksgiving Dinner
supper time x
having pasta for christma dinnerx
Holidays togetherx
y Gatheringsx
family traditionsx
Thanksgiving, Christmas, going to 
football gamesx x
snowboarding
Tailgating, Family Functionsx x
family traditions including holiday 
dinnersxx x
Christmas
Churchx
Thanksgiving at my cousin's housexx
family traditions
Family Reunionx
family traditions
we always get together for 
thatnksgiving every yearxx
annual family christmas dinnerx
family holiday traditions
 
140 
 
Appendix E: Culture-Concept Examples  
 
Culture Name Concept  Example 
fishing objects 
A colection of atributes and behaviors describing 
something For example, take a brand new cel phone, 
each contact in the contact list has the same blank 
lines, names, number, email etc. When you fil it up, 
you create an instance of a contact by giving it 
atributes (the contact name) and behaviors (get a 
number) 
shoping objects 
It was a nice day. You was surfing the Internet. 
Sudenly, something caught your eye. It is Big 
scren LCD TV on sale for only $20 and even beter, 
fre shiping! <br> You clicked the advertisement 
link and went to the online store, found the 
discounted Big scren LCD TV, and put it into 
shoping cart. Since you are a member of the store 
already, al you had to do was enter your user ID and 
pasword, click the "Login" buton, selected the 
saved biling/shiping adres, selected the credit 
card for payment, clicked "check out" buton and.. 
Done!<br> The online store sent a confirmation e-
mail to you. Now al you have to do is wait. And, you 
know, it's always the most dificult part. <br> 
shoping  variables 
It was a nice day. You are out shoping. Sudenly, 
something caught your eye. It is an ad for a Big 
scren LCD TV on sale for only $20. Imagine 
entering the very large store with lots of 
departments, tables, shelves, etc. Al these places 
have diferent things stored in them. You head to the 
department for your Big scren LCD TV. Once you 
find the right department, you have to find the type 
that was on sale. <br> 
 
141 
baking functions 
It was a nice day. Some kids in the comunity have 
come to you to learn how to bake cokies for their 
fundraiser. You take out your baking suplies and 
gather al your ingredients. Now its time to show 
these eager beavers how its done. After explaining to 
them when to ad al the ingredients and how to 
measure acordingly we finaly have our big batch of 
cokie dough. <br> Now you carefuly explain to 
your captivated audience how to spon the dough 
and its placement on the cokie shet. You stres the 
importance of the amount of cokie dough per cokie 
the efects of this size is to big or two smal. Then 
you remind them to be mindful of the space betwen 
the cokies so they have rom when they expand. 
<br> Finaly our cokie shet is filed with cokie 
dough, and now its time to put them in the pre-
heated oven. Now al we have to do is wait. And, you 
know, it's always the most dificult part. <br> 
 
142 
holiday functions 
Picture this, its the holidays, you have voluntered to 
overse the comunity fundraising event of gift 
wraping. You, several friends, and family have come 
together to make it a suces.Prior to embarking on 
your journey, you al sat through a briefing on the art 
of gift wraping. This entailed detailed skils of:<br> 
1) gather your materials- lay them out on a clean, 
flat work surface. Remember to remove the price tag 
from the gift before wraping it <br> 2) positioning 
the gift: Place the box containing the gift along the 
length of wraping paper and unrol enough paper to 
wrap it around the box, leaving at least a 2-inch 
overlap. <br> 3) paper cuting- Eyebal the wraping 
paper at the ends of the box. Trim away any extra 
paper so that the remaining flaps are long enough to 
cover the box but short enough to fold over smothly 
into flaps. 4) edge folding- Position the gift box so 
that one short end is facing you. Grasp the left and 
right edges of the wraping paper and push the sides 
in so that top and botom flaps are formed. Make 
sure the edges are pushed in as far as they wil go 
without riping the paper. Tape the edges to the 
box.<br> 5) tape placement- Bring one lengthwise 
edge of the wraping paper to the center of the box 
and secure it with tape. Turn the oposite edge of 
the paper under aproximately 1 inch and bring this 
to the center of the box as wel so that it overlaps the 
first edge, and tape it down. <br> 6) bow/ribon 
tying- Wrap a long piece of ribon around the gift 
box lengthwise, then twist the ribon at the 
lengthwise seam to wrap it around the box width-
wise. <br> 7) gift labeling- If you have a card, slide 
it under the ribon and secure it with tape on the 
underside. If you have a gift tag, use the lose ends 
of the ribon to secure the gift tag (if it has a hole in 
it), or adhere it directly to the gift (if it has adhesive 
on it.)<br> <br> So now you each pick a 
comfortable spot, a wraping paper patern that you 
prefer and you get started. The lines quickly form 
and you al start wraping away pre-cautiously 
wraping as you learned in your briefing. <br> 
 
143 
dinerFamily objects 
You have just sat down at the table of your family 
holiday diner, and you quickly scan the table. The 
table is filed with al your favorites, mac and chese, 
mash potatoes, gravy, grens, etc. You are ready to 
dig in. So you sip on your drink as you wait for 
everyone to take their seats, and the host to enter to 
begin the festivities.<br> 
familyTogether objects 
You are at your family get together and seing family 
you haven't sen in a long time. As they start to 
arive you are trying to remember the names of al of 
your first cousins, aunts and uncles based on your 
childhod memories. So you start making a list of 
names and some details that you remember about 
them (like height, size,) and some random emories 
(your aunt that makes your favorite pie or your 
cousin wet the bed when you were kids). You are 
quickly forming your list as the family starts pouring 
in, and heading your way. Now to put your memory 
to the test.<br> 
church functions 
It is a nice day, you are headed to church. Once at 
church, the church greter asks you to asist her in 
seating people as they enter, because you are 
expecting a lot of guests today. She explains a 
detailed proces of greting, seating, and then giving 
each guest a flier. Finaly you are ready to help seat 
the guests. Now al we have to do is wait, for them to 
arive. <br> 
music objects  
Its a nice day and you just got some new music to 
ad your computer. You figured this would be a god 
time to create a new playlist. So you sort al your 
music by title and gather your favorite tracks that wil 
give you the sound you are seking for this playlist. 
Then you sort your music by rhythm and search 
through and se what other songs in your colection 
would be a god fit. You want one more short song 
to ad your playlist so then you search through your 
list of songs my time periods. Your playlist is just 
about done. Now its time to put it to the test.<br> 
 
144 
charity objects  
Its a nice day and a wealthy philanthropist has asked 
you to recomend several nonprofit/charitable 
organizations to donate, to based on your 
background and experience in working with charities. 
So you start to think about the best way to 
diferentiate betwen the charities. As you start to 
create your list, to simplify things, for each charity 
that you recomend you provide the name, the 
description and its purpose, so that the philanthropist 
can be wel informed of the charity that they are 
donating to. You've done a prety god job, so now 
you send your list to the philanthropist so now you sit 
and wait. <br> 
tcom variables 
Picture this, you are working for a telecomunication 
company and have ben charged with the task of 
geting some new comunication equipment to 
reduce cost. There are some products that you have 
in mind. Your mision today is to figure out the 
product information so you can get the right 
products. For starters you realize that you at least 
ned the make, the model, and the product ID 
number for each product. Now the hunt is on. <br> 
movies objects  
Its a nice day and you finaly got some time to relax 
and catch a movie. First you have to decide what 
kind of movie you are in the mod to se. You start 
of by sorting them by category/genre (comedy, 
action, drama, family, etc) to narow down your 
movie choices. Then you sort the available movies by 
leading actors (any of your favorite actors?) and then 
plots (what's the subject of the movie). You are down 
to your final two movies and you use the movie 
length as the deciding factor. Now its time to start 
the movie. <br> 
server functions 
Its a nice day and you are heading into work at the 
restaurant today, they just hired a new server and 
asked you to train him. You carefuly explain and 
demonstrate the detailed proces of a servers 
responsibility of explaining the menu to the 
customer, taking the customer's order, and delivering 
the customer's meal from the chef. Finaly the 
training sesion is over, and the new server sems to 
be ready to start serving the customers. Now al we 
have to do is wait, for them to arive. <br> 
 
145 
 healthcare objects 
Its another great day at work in the healthcare 
world, and your supervisor has just requested the 
medical history of several patients. Your supervisor is 
in a meting and has asked you to get some 
information together on patients. You scan the 
patients chart, picking out the key information for the 
patient (such as patient name, medical 
condition,medication, etc.) and quickly send over the 
necesary information. <br> 
golf objects 
Its a great day for golf, and a friend has asked you to 
help them learn the basics. As you make your way 
acros the gren you start of by explaining some 
basic golf fundamentals the diference in golf clubs, 
golf bals, varying golf holes, while using examples 
that include known golf players and comonly known 
golf concepts. You make it over to the first hole, after 
a couple of demonstrations, you guide them through 
their first swing. <br> 
training functions 
Its a nice day and a friend is in ned of your 
expertise to help him prepare to present his first 
training clas. You carefuly explain and demonstrate 
the detailed proces of preparing the leson, 
gathering and sorting the material, creating 
handouts, and creating visual aids. Finaly, you are 
done, and your friend sems to be ready to start his 
first training clas. Now, its training day, and al we 
have to do is wait, for the traine's to arive. <br> 
 
 
146 
 
Appendix F: Object and Function Analyzers Definition 
 
Objects 
What is an object? In computing, they say an object commonly means a data structure 
consisting of data fields and procedures (methods) that can manipulate those fields. But 
really, object is just the way computing world defines a THING. So any THING, 
PERSON, OR PLACE can be an object. 
For example, in the story you just saw, almost every noun can be defined as objects:  
<<removed: cultural example>> Defining objects, then putting them together to 
construct a computer program to solve problems is called Object-Oriented Programming. 
An object usually contains two parts - Attributes, and Methods. 
Earlier we said that objects are what we in the computing world call THINGS. 
Attributes describe the 'thing' characteristics. So attributes can be a name, size, style, type, etc  
Methods describe what the 'thing' can do, the behavior. . So methods can be actions that the 
'thing' can do, or that can be done on it  
 
Functions 
What is a Function? A Function is a portion of a program that independently performs when 
you tell it to, its the behaviors or actions of a program. For example a song can be 
played, an alarm can get or set time, and a dog can bark.  A Function can be thought of 
as the steps or the instructions of a portion of a program that you can repeat. 
<<removed: cultural example>> 
 
Earlier we said that a Function is a portion of a program that independently performs when 
you tell it to.  A function is designed so that it can be coded to be started ("called") 
several times and/or from several places within one execution of a program, including 
from other functions. 
A Functions can also be designed so that it can obtain a specific set of data values from the 
program that called it (its parameters), and eventually provide a specific set of values to 
it (its return values).