Designing Learning Activities to Support
Young Women’s Interest in Programming
and Computational Thinking
Harang Kim
[Media Technology: Strategic Media Development] [Advanced Level, One-year Master Program] [15 Credits]
[Spring 2020]
[Supervisor: Daniel Spikol] [Examiner: Fredrik Rutz]
Acknowledgements
First, I would like to express my sincere gratitude to my supervisor, Daniel Spikol, for his continuous support of my thesis and all his valuable feedback and inspiration. I would like to thank him for his enthusiasm, encouragement, patience, and good sense of humor. Additionally, his guidance helped me throughout the research and writing of my thesis. I could not have imagined finishing this thesis without his supervision.
Besides my supervisor, I would like to thank Suzan Boztepe and Fredrik Rutz for their insightful comments. I would also like to thank all the interviewees, participants, and organizations for sharing their thoughts.
Abstract
Over the last few years, the importance of computer science education for children has been promoted more and more vigorously. In addition, the demand for technology occupations has increased rapidly, and there are many job opportunities in computer science. However, there are not many women working in this field. One of the reasons is young women’s lack of interest in computer science. This study investigates how to attract young women to computer programming and support computational thinking through design and develop learning activities. This study’s approach includes several related researches, theories, and methodologies. Interviews, workshops, and observations were used to determine design requirements. The results demonstrate that tangible and meaningful artifacts are effective educational tools for computer programming. Based on the results, this research developed a prototype, “TomatoBox,” a do-it-yourself kit that creates toys while providing an enjoyable activity to learn programming.
TABLE OF CONTENTS
1 INTRODUCTION ... 1
1.1 THE IMPORTANCE OF PROGRAMMING AND COMPUTATIONAL THINKING ... 1
1.2 PROBLEM ... 2 1.3 PURPOSE ... 3 1.4 RESEARCH QUESTION ... 4 1.5 TARGET GROUP ... 4 1.6 LIMITATIONS ... 5 2 BACKGROUND ... 6
2.1 REASONS FOR THE LACK OF WOMEN IN COMPUTER SCIENCE ... 6
2.2 THEORY ... 8 2.2.1 Computational Thinking ... 8 2.2.2 Constructionism ... 9 2.2.3 Tangible Artifacts ... 10 2.3 RELATED RESEARCH ... 10 2.3.1 Learning Tools ... 11 2.3.2 Gender ... 12 2.3.3 Approach ... 12 2.3.4 Organizations ... 13 2.4 SUMMARY ... 14 3 METHOD ... 16 3.1 DESIGN STRATEGY ... 16 3.1.1. PARTICIPATORY DESIGN ... 16
3.1.2. DESIGN-BASED RESEARCH ... 17
3.1.3. THE ROLE OF CHILDREN IN THE DESIGN PROCESS ... 18
3.2 DESIGN PROCESS ... 19
3.3 DESIGN METHOD ... 22
3.3.1. AFFINITY MAPPING AND THEMATIC ANALYSIS ... 22
3.3.2. INTERVIEWS ... 23
3.3.3. FUTURE TECHNOLOGY WORKSHOP (FTW) ... 24
4 DESIGN PROCESS ... 26
4.1INTERVIEW ... 26
4.2WORKSHOPS ... 28
5 RESULTS ... 32
5.1 INTERVIEW RESULTS ... 32
5.1.1. Analysis and Synthesis of Interviews ... 32
5.2 WORKSHOP RESULTS ... 36
5.2.1 Participants ... 36
5.2.2 Themes ... 38
5.3 ANALYSIS AND SYNTHESIS OF ORGANIZATION OBSERVATIONS ... 39
5.4 DESIGN REQUIREMENTS ... 40
6 PROTOTYPE ... 42
6.1EACH STAGE OF THE PROTOTYPE ... 42
6.2FINAL PROTOTYPE,“TOMATOBOX” ... 44
6.3EVALUATION ... 46
7 DISCUSSION AND CONCLUSION ... 47
7.1DISCUSSION ... 47
7.2CONCLUSION ... 48
REFERENCES ... 50
APPENDIX A: INTERVIEW TEMPLATES ... 60
APPENDIX B: INTERVIEW NOTES ... 63
APPENDIX C: A BRIEF STRUCTURE OF THE WORKSHOP WITH CHILDREN ... 85
APPENDIX D: INFORMED PARENTAL CONSENT FORM ... 86
1 Introduction
1.1 The Importance of Programming and Computational Thinking
Programming is a process of thinking about the fundamentals of problems and determining solutions. Programming helps people to think and solve problems in an organized way, and this emphasizes the importance of learning programming at a young age (Heininger et al., 2017). Programming has positive impacts on children’s achievements in mathematics and science, their language ability, their creativity, and their social-emotional interactions (Horn et al., 2009). Learning programming has become a core and essential component in the curriculum of many countries. In England, computer science has become a compulsory part of the school curriculum for children aged between five and sixteen (Brown et al., 2014). The mayor of New York city declared that computer science classes would be offered to all students by 2025 (Taylor & Miller, 2015). Former U.S. President Obama also emphasized the importance of computer science education in his weekly address from the White House. Obama said that “In the new economy, computer science is not an optional skill. It is a basic skill, right along with the three R’s (reading, writing, arithmetic). Nine out of ten parents want it taught at their children’s schools. So, I have got a plan to help make sure all our kids get an opportunity to learn computer science, especially girls and minorities. It is called Computer Science for All” (2016, paragraphs 5–6).
In the Fourth Industrial Revolution, new technologies have created changes in society’s ways of living and working. Many jobs are becoming automated and require people to do something more than simply work with computers. Robots are even replacing humans in some occupations; robots, rather than bank tellers, assess customers’ assets (Agarwal, 2017), and chatbots answer questions from consumers (Legters, 2019). This digitalized new job market has created new requirements for employee qualifications. Therefore, it is crucial to be familiar with the technology field, not only for information technology (IT) workers but also for various occupations (Heininger et al., 2017). For younger generations to compete in this new high-technology world, programming skills and computational thinking are essential.
Computational thinking affects students’ academic skills as well as their problem-solving abilities (Calao et al., 2015). Computational thinking can be adapted for other subjects in elementary and secondary schools since it is cross-disciplinary (Yadav et al., 2016). Computational thinking helps students to deal with complicated problems and other disciplines and also supports them to succeed in a technological society (National Research Council, 2011). Computational thinking is based on algorithms, which are sequences of steps for problem-solving or accomplishing tasks (Yadav et al., 2016). Denning (2009) claimed that computational thinking is “the ability to interpret the world as algorithmically controlled conversions of inputs to outputs” (p. 30).
Even though covid-19 has destroyed job markets, technology companies show growth in job openings (Forbes, 2020). Therefore, the importance of technology is increasing, and no-one can predict the future, but technology will become a more essential part of our life. Thus, having skills in this area can be a powerful strength in the unpredictable job market of the future. To have equal opportunities in future job markets, young women need to be supported to become involved in the technology field.
1.2 Problem
Novice programmers are unfamiliar with programming environments and tools, such as languages, software, and syntax. Thus, it takes time and effort to study programming. It is challenging to comprehend programming concepts and structures since programming needs different ways of thinking and understanding, which are unlike general situations. Students are unused to dealing with the complicated syntax of programming (Bosse & Gerosa, 2017). Thus, failure rates in introductory programming courses and dropout rates after introductory programming courses are high (Nikula et al., 2011; Yadin, 2011).
Apart from these problems, there is a gender gap in the technological field. Computer science–related job markets have seen a decrease in the percentage of female employees since the 1990s (ComputerScience, n.d.). The percentage declined from 35% to 26% between 1990 and 2013 (ComputerScience, n.d.). Furthermore, only 23% of High School girls take the advanced placement computer science examination, 19% received
a bachelor’s degree in computer and information science, and only 26% of the computing workforce is female (Conway et al., 2018). In 2016, only 18.7% of women earned a bachelor’s degree in computer science (National Foundation Science, 2019). There are many minority groups in computer science, but women are probably the largest (Kelleher et al., 2007).
The low number of female students in computer science results in reduced gender diversity at work. According to Statista, only a quarter of the computing workforce is female across the Organization for Economic Co-operation and Development (OECD) countries. In the United States are found the world’s largest technology companies: Apple only has female employees in 23% of technology jobs, 21% are female at Google, and 20% are female at Microsoft (Maxwell, 2019). In England, women only occupy 5% of leadership positions in the technology industry (Maxwell, 2019). Also, only 11.5% of game developers were female in 2009 (Tassi, 2014). Everyone has a right to have the knowledge and experience to make good choices on technological issues.
1.3 Purpose
This study supports young women’s interest in computer programming and computational thinking by designing learning activities. Learning activities can be specific tools, processes, or programs that make computer science enjoyable for young women, so that they are willing to learn programming without any fears or worries. It is vital to keep a balance in the technological field. Technology companies’ imbalances in diversity create immediate costs, such as low market share, human resource (HR) costs, and public relations costs (Conway et al., 2018). Furthermore, economies become more competitive and benefit from equality, as Klaus Schwab says: “The economies that will succeed in the fourth industrial revolution will be those that are best able to harvest all their available talent” (Maxwell, 2019).
Many IT jobs offer decent working conditions, and computer science has the smallest pay gap between men and women (AAUW, 2020). From a business perspective, women’s choices affect up to 85% of purchasing decisions, and diversity is an engine of innovation (Paul et al., 2011). Regarding gender diversity, diverse teams are seen as more creative, innovative, and profitable (Conway et al., 2018). The U.S. Bureau of
Labor Statistics said that the employment of software developers is expected to grow 21% from 2018 to 2028. This increase in demand for computer software developers is much faster than the average increase in demand for all occupations (Software Developers, 2019). This rapid growth in the need for software developers accompanies the explosive growth of mobile-based internet usage (O’Brien, 2019). Thus, increasing young women’s interest in computer science can provide them with more opportunities in the future job market.
1.4 Research Question
Based on the problem of the lack of women in computer science, the research question that guided the study is as follows:
"How to attract young women to computer programming and support computational thinking through design and develop learning activities?"
Through this research, learning activities were designed to support young women’s interest in computer programming and computational thinking.
1.5 Target Group
For this research, the focus group was young women, 11–14 years old. There were several reasons to focus on this age group. Girls show less interest in STEM careers than boys from early adolescence, so it is essential to arouse their interest in computer science before they have negative stereotypes about the field of technology (Sullivan et al., 2015). Many girls lose their confidence and competence during adolescence. Girls who are nine and ten years old are full of confidence but after puberty they start to doubt themselves and their thoughts (Margolis & Fisher, 2003). In addition, about 30% of female undergraduate students who decided to major in computer science were influenced by a high school programming course (Graham & Latulipe, 2014). It is crucial to interest girls in computer science before they lose their self-confidence. However, this research also conducted interviews and workshops with women who are not in this target age group. They were also in this target age group when they were young. Thus, it is possible to get ideas such as why non-computer-science female
college students did not get interested in this field. Also, women experts in the computer science field can give opinions about what skills are necessary to specialize in this field or why they get involved in this area. These data can be background and basis for solutions.
1.6 Limitations
There may be several different factors that affect someone’s interest in programming. These factors include age, environment, culture, instructor, teaching methods, course structure, type of evaluation, previous knowledge, and external factors. The workshops took place with young women, and everyone has a different background. Thus, the results may have been influenced by several factors.
Qualitative methods, such as interviews and workshops, were the right choice for this study. However, when using these methods, it is difficult to collect hard facts, such as numerical values. If this study had used quantitative methods, the results could have more credibility. For instance, the data could have provided stronger evidence if this study conducted quantitative survey questions and statistical analysis.
There were factors hindering the recruitment of the target group. Because of Covid-19, some of the schools in Malmö were closed or not allowing visitors. Thus, there was a time-constraint on the recruitment of participants.
2 Background
The background chapter considers the following: reasons for women’s lack of interest in programming; theories; relevant studies; and, to comprehend related concepts, programming education organizations. This background provides ideas and supports answers for the research question.
2.1 Reasons for the Lack of Women in Computer Science
Women and men show differences in their interests from early elementary school, which is one of the reasons for the gender gap in the technology industry (Maltese & Tai, 2010). Girls tend to spend less time playing with computer games, spatial and science-related games, and technological toys (Cheryan et al., 2015). Since girls are not interested in the basic premises and rules of computer games, they use a computer only for homework; therefore, computers are not creative or enjoyable tools for girls (Sullivan et al., 2003). Boys are likely to spend more time playing with technological activities, and they thus have more opportunities to increase their self-confidence in computer science (Nugent et al., 2010; Terlecki & Newcombe, 2005). The gender gap in the technological field may occur because of this lack of early experience with computers for girls (Cheryan et al., 2015).
Computer games are effective ways to improve children’s confidence with computers and also a well-known reason why boys are more familiar with computers than girls. (Barker &Aspray, 2006). Computer games are usually developed for, purchased by, and used by boys and young men, almost excluding girls and women (Barker & Aspray, 2006). Henn (2014) states that “the idea that computers are for boys became a narrative. It became the story we told ourselves about the computing revolution. It helped define who geeks were, and it created techie culture” (paragraph 7).
Furthermore, girls have a stereotype that boys are better at robotics and programming (Master et al., 2017). Girls with less motivation than boys for computing do not have enough experience to create interest and self-efficacy in technology (Barker & Aspray, 2006; Martin & Dinella, 2002). Since personal computers target men and boys, families
tend to buy computers for boys rather than for girls (ComputerScience, n.d.). Boys are more interested and confident in technology than girls. Many studies have talked about women’s lower self-efficacy in mathematics, engineering, and computers, which are known as stereotypically male-dominated subjects (Sullivan et al., 2015). Bandura (1994) defines self-efficacy as “people’s beliefs about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives” (p. 71). Low self-efficacy in these subjects has created a considerable gap between men and women in the field of computer science (Sullivan et al., 2015). In addition to disinterest in computers, fears about computing culture also affect the lack of young women in the computer science field (Kelleher et al., 2007). According to Cheryan et al. (2015), “Students’ stereotypes about the culture of these fields— including the kind of people, the work involved, and the values of the field—steer girls away from choosing to enter them. Computer science and engineering are stereotyped in modern American culture as male-oriented fields that involve social isolation, an intense focus on machinery, and inborn brilliance” (paragraph 1).
Furthermore, Spertus (1991) claims that “factors include the different ways in which boys and girls are raised, the stereotypes of female engineers, subtle biases that female face, problems resulting from working in predominantly male environments, and sexual biases in language” (p. 1). Furthermore, researchers found that the introductory curriculum for computer science reduces women’s interest in majoring in computer science (ComputerScience, n.d.).
Margolis and Fisher’s (2003) research considers several reasons for lower enrollments of young women in computer science. Most computer science courses have only a few female students, so they are not sufficiently friendly environments for young women. Courses are too abstract, discussing language details and syntax rather than applications. Young women think computer science has a “geeky” and boring culture; thus, they do not want to be included in that image or to be with those types of people. They also have a stereotype that computing is a male-dominated activity, and even their counselors or parents do not encourage them to study computer science. Furthermore, they fear getting low grades and knowing less than others, and some young men encourage this fear.
2.2 Theory
Research into programming education deals with various theories, such as those considering computational thinking, constructionism, and tangible objects. This research adapts these theories.
2.2.1 Computational Thinking
According to Wing (2006), computational thinking concerns “solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science. Computational thinking includes a range of mental tools that reflect the breadth of the field of computer science” (p. 33). Not only computer scientists but everyone should have computational thinking as a foundational ability (Wing, 2006). Children’s analytical skills in reading, writing, and arithmetic can be developed by computational thinking (Wing, 2006). Computational thinking uses abstraction and decomposition to design complicated tasks or systems (Wing, 2006). Computational thinking is a form of analytical, mathematical, engineering, and scientific thought that approaches solving problems; designing and evaluating complex systems; and understanding computability, intelligence, human behavior, and the mind (Wing, 2008).
Wing (2006) argues that computational thinking is “conceptualizing; fundamental; a way that humans think; complements and combines mathematical and engineering thinking; ideas, not artifacts; for everyone, everywhere” (p. 35). Conceptualizing means thinking abstractly at multiple levels and it is more than programming alone. The characteristic of fundamental skill is something that everyone should know in this modernized society (Wing, 2006). Computational thinking is not about humans thinking like computers, but humans using computers as equipment to solve problems. Computational thinking is not about software or hardware artifacts; it is more about solving and approaching problems, communicating, interacting with others, and handling our daily lives. Tools can be used for reinforcement of computational thinking concepts, since tools make abstractions more concrete and dynamic (Wing, 2006). Furthermore, children today are familiar with the process of using tools and are confident in exploring and
playing with them (Wing, 2006). Thus, researchers can use these trends since children become accustomed to using computational instruments at home and school.
The research goal is to create methods to support not only programming but also computational thinking. Computational thinking takes precedence over programming; since computational thinking provides analytical skills to solve and approach problems, computational thinking is a critical factor.
2.2.2 Constructionism
Constructionism assumes that children learn more easily when they are required to make meaningful creations (Papavlasopoulou et al., 2019). According to constructionism, learners are not passive receivers but dynamic finders, who discover knowledge (Papavlasopoulou et al., 2019). In addition, learning effectiveness can be attained by building artifacts, and computational culture uses digital media and computer-based technologies to support the construction of artifacts (Papavlasopoulou et al., 2019; Kafai & Resnick, 2012).
Papert (1980) insists that “the vital aspect of constructionism is the requirement of ‘objects-to-think-with’—objects in which there is an intersection of cultural presence, embedded knowledge and the possibility for personal identification” (p. 11). In a constructionist environment, children can build their knowledge based on their previous experiences (Papert, 1980). Papavlasopoulou et al. (2019) claim that “constructionism’s basic idea is that the most effective learning experiences are those that include active creation, socially meaningful artifacts, interaction with others, and the use of elements that support one’s learning and thinking” (p. 416). Constructionism is selected as the background theory for this research. Children learn something easily by building their creations. This theory is the foundation for the workshop activities in the research. Based on the concepts of constructionism, children make paper toys and write songs with a programming robot during the workshop.
2.2.3 Tangible Artifacts
There are several types of research into using tangible user interfaces to improve learning effectiveness (Tada & Tanaka, 2015). Price (2013) stated that “tangibles generally refer to interfaces where computational power is embedded in everyday artifacts or customized objects, which can be wirelessly networked or linked to various forms of digital representation” (p. 2).
Children can code more directly and less abstractly with tangible programming than pictures (Wang et al., 2014). Thus, tangible programming makes children more interested in programming (Wang et al., 2014). Tangible programming has been created for novice programmers to provide an easier learning process. Participants improve their skills when they design tasks with physical artifacts, such as using pen and paper or other tangible materials. These physical artifacts allow participants to take action immediately (Papavlasopoulou et al., 2019). Tangible interaction can help children become more actively involved and self-motivated in educational activities (Horn et al., 2009). Tangible interfaces allow users to create directly based on current knowledge and real-world experience (Horn et al., 2009). The prototype idea for this project is based on the theory of tangible objects that help to explain abstract concepts more concretely.
2.3 Related research
Related studies concern programming education for young women and children. These related studies are based on theories such as computational thinking, constructionism, and tangible artifacts. Each study uses different workshops and tools, but their goal is the same: increasing participants’ interest in programming. These related studies provide insights and guidelines for how the project should proceed and how it should approach solutions. These associated studies are categorized according to four subjects: tools, gender, approach, and organization. These four factors are the critical criteria that were applied when choosing related research.
2.3.1 Learning Tools
The first study used programming robots to increase children’s STEM (science, technology, engineering, and mathematics) motivation. Children were assigned to several groups, and each group performed different activities. The results showed that the robot treatment group had higher technological motivation than other groups (Master et al., 2017). The robot treatment group played with a pet robot and programmed a robot using a smartphone. This research shows that playing with robots can be more practical than other activities. This research was chosen because it compares robots with other activities. This comparison makes it clear that using robots is more effective than other methods. This finding needs to be considered for the project; the results provide a guideline for the research question.
Another study used a Thymio II, which is a miniature robot designed for education. In the workshop, children were given the opportunity to look at some functions of the robot, such as light emitting diode (LED), distance sensors, and motors. This study argued that children joined this workshop not only for fun but also to learn something, and the Thymio II fit their needs, which span fun and seriousness (Magnenat et al., 2012). However, although children were able to learn concepts such as use of the sensor or the loading of a program into the robot, theoretical concepts were difficult to understand in an hour-long tutorial (Magnenat et al., 2012). This research provides two notable conclusions: children are willing to learn, and a programming robot is suitable for that purpose; and a robot was not sufficient to facilitate the comprehension of theoretical concepts. Based on these results, the current project should consider what aspects could improve the comprehension of theoretical concepts.
The third study considered children’s programming learning experience while using a block-based programming language, Scratch, and building games. This research was based on constructionism and design-based research. The study included two coding workshop sessions. The first session concerned interacting with robots. Children filled in worksheets with some questions about robots and controlled robots by coding simple loops (Papavlasopoulou et al., 2019). The second session involved making games using Scratch. This study was reviewed
because it uses the same tools, theory, and methods as the present study. Thus, the project can be compared and can provide insights into what may be lacking in the present project.
2.3.2 Gender
In this section, studies that focus on young women are reviewed. These studies were selected because they have the same target group: young women in secondary schools. The first study used a workshop that introduced computer programming by using Storytelling Alice, a programming environment used to create 3D animated stories by providing a set of high-level animations, 3D characters and scenery, and a tutorial with story-based examples (Kelleher et al., 2007). In this workshop, participants could build virtual worlds while writing down their programs. This workshop gave them positive thoughts about interactive graphics and storytelling elements (Hu, 2008). The authors concluded that storytelling made syntax more understandable for participants. This result suggested an idea for the prototype: focusing on the effect of animated stories and interactive graphics could be one way to approach the prototype for young women.
The second study concerned designing an after-school computing program for young women in secondary schools. Students researched daily usage of technology and areas connected with technology, such as medicine and fashion (Sullivan et al., 2015). They wrote some instructions for their peers to follow and they also had a session using Scratch programming to make animations and interactive computer games. This study used post questionnaires, and the results showed some positive outcomes concerning self-efficacy in computers and careers in computer science (Sullivan et al., 2015). In this after-school program, students were assigned several activities, such as researching relevant areas with technology or writing instructions for peers. These various types of activities provide ideas of what can be achieved during the current study’s workshops.
2.3.3 Approach
The last study had a unique approach to tools. This study was reviewed because the approach to solutions can provide insights for the current project. It used a LilyPad
Arduino, which is a sewable microcomputer that can be used to learn programming and engineering concepts (Kafai, Fields, & Searle, 2014). This research used the concept of electronic textiles that combine “high masculine technologies of engineering and computing with arguably low feminine technologies of crafting and sewing” (Kafai, Fields, & Searle, 2014, p. 538).
Electronic textiles help to make abstract aspects of programming more transparent while emphasizing the significance of aesthetics in learning (Kafai, Fields, & Searle, 2014). In the workshop, students started by sketching their projects and drawing circuit schematics. Next, they sewed and crafted with textile materials and programming the Lilypad Arduino (Kafai, Fields, & Searle, 2014). This electronic textile broke down students’ prejudices against what can be made and the types of people who can work in the field of technology (Kafai, Fields, & Searle, 2014). This study proved that different materials and techniques can combine and create synergy. This research gives an insight into how to approach a diverse group without bias. This gender-neutral point of view should be reflected in the current project, so that is does not become stereotypical in gender.
2.3.4 Organizations
Several organizations teach programming, and some of them are even focused on women. These organizations try to minimize the gender gap in the computer science and technology industry while providing free programming workshops. It was useful to see what activities have been used to help women to get involved in the technology industry through these organizations.
The first organization was Girls Who Code, a non-profit organization that aims to increase the number of women in the computer science field. It offers a free seven-week-long introductory computer science program for girls in grades 10–11 (Girls Who Code, n.d.). The current study considered this organization because it has the same target group. They usually provide art, storytelling, robotics, video games, websites, apps that are related to computer science, guest speakers, workshops, and field trips.
The other organization was Pink Programming, a volunteer-led organization in Sweden that offers free programming workshops for women (Pink Programming, n.d.). Through this organization, women who are interested in programming can learn how to code or build on existing skills. This organization was chosen because the researcher has experience with their workshops. The method of teaching was similar to copying and pasting from the teacher’s code; the result was creative, but the process was not. Based on this workshop experience, this project is more focused on the learning process rather than solely on outputs.
2.4 Summary
Although there are several beneficial aspects to computer science, women are reluctant to get involved in this area. The reason is that they have low self-confidence, motivation, and interest in this field (Master et al., 2017; Sullivan et al., 2015). Therefore, this research aimed to find ways to solve this problem by designing learning activities to support young women’s interest in computer programming and computational thinking. Several theories and related studies provided an opportunity to explore how the learning activities should be designed and developed for young women.
Theories of computational thinking and tangible artifacts demonstrated that using physical objects helps to improve learning and interest in programming and computational thinking. Furthermore, creating personal and meaningful artifacts can support learning abilities, according to constructionism. These findings are reflected in this project.
In one of the related studies, a robot was used as an educational tool for programming. The results proved that tangible objects were useful tools for learning. However, the researchers used end-products, which were already made. As their tools were produced by somebody else, their method departs from constructionism, which is learning something easily by making meaningful artifacts (Papavlasopoulou et al., 2019). To fill this gap based on the theories previously discussed, this project plans to encourage participants to create their own significant objects.
This study reviewed the related research to define what process and activities should be done with young women. The findings from the related studies provided an idea to frame the area of learning activities. The related studies do not use any stereotypical tools or processes and they try to offer various activities and opportunities to increase young women’s interest in programming and computational thinking. This project should also adopt this perspective. There may be certain areas that attract more females than males, but this research focuses on young women only as a target group.
3 Method
This chapter describes design strategies, design process, and design methods. Design strategies are theoretical foundations and provide guidelines. The design process is based on double diamond design, and it is a background for the research procedure. Future technology workshop, affinity mapping, thematic analysis, and interviews are used for design methods to collect data. These methods elicited design requirements from results. Figure 1 provides a condensed visual depiction of the structure of strategies and a process that is used for this research.
Figure 1. Description of the structure of the design strategies and a design process.
3.1 Design Strategy
3.1.1. Participatory Design
In participatory design, researchers and users interact closely via interviews, workshops, prototyping, focus groups, and other techniques, and users create the project to demonstrate their values and goals (Spinuzzi, 2016). Participatory design emphasizes participants’ involvement in design approaches. This study selected participatory design because participants’ commitment is crucial for this project. The project includes interviews and workshops with non-computer-science college students, experts in a computer science field, and children. Their role had a
significant impact on the design process. By applying participatory design, this study can acquire knowledge about participants’ work and experiences. The other reason for choosing participatory design is its feature of iterative experiments to develop a design. Without iterative reflection and design, participants would not be able to give their opinions critically or answers efficiently.
Participatory design is a research methodology often used in user-centered design, human-computer interaction, computer-supported cooperative work, and other related areas. Participatory design involves users’ design approaches, such as producing artifacts, structures, practical knowledge, and work organizations (Spinuzzi, 2016). These methods ensure that participants’ interpretations are included in the research as a vital part of the process, not only to understand empirical activity, but also to envision, and shape positively for users (Spinuzzi, 2016). The result of participatory design usually includes designed artifacts and work arrangements or environments (Spinuzzi, 2016).
There are three primary stages in participatory design: the initial exploration of the work, the discovery processes, and the creation of a prototype. Initial exploration work involves meeting designers and users; including technologies; and developing teamwork, workflow, work procedures, and routines (Spinuzzi, 2016). In the discovery processes, designers and users apply a variety of techniques and interact closely to comprehend work organization and imagine the future workplace (Spinuzzi, 2016). This stage can include several methods, such as role-playing or organizational games, future workshops, workflow models, interpretation sessions, storyboarding, and organizational toolkits (Spinuzzi, 2016). The prototyping stage involves iterative co-exploration by designers and users to create artifacts that are suitable for the conclusion of the discovery process stage (Spinuzzi, 2016).
3.1.2. Design-Based Research
Design-based research is a methodology for researching and designing technology-enhanced learning environments, which are learning, and instructional systems based on technology (Wang & Hannafin, 2005). Design-based research supports
innovative environments and creates new design possibilities. This method was applied because this project aimed to design learning activities for computer programming and computer science. By applying design-based research, this project was able to use a combination of various approaches to accumulate data from many sources.
Design-based research demonstrates how educational innovations work in real life and why and when they work (DBRC, 2003). It is a combination of empirical educational research and the theory-driven design of learning environments (DBRC, 2003). Design-based research designs and explores every area of designed innovations, including artifacts, activity structures, institutions, and curricula (DBRC, 2003). Design-based research is an iterative cycle of design; consecutive iterative cycles of design provide accumulated data and implementation experiences, and theories arise based on these data and experiences. This study tried to identify the target group’s needs and determine solutions by working with people. This study aimed to design and create something new step by step through the iterative process.
Design-based research includes various methods, such as “survey, expert review, evaluation, case study, interview, inquiry methods, and comparative analysis” (Wang & Hannafin, 2005, p. 10). Quantitative and qualitative methods are used by researchers to investigate different design aspects, to emphasize related problems and needs, and to document reasons and methods of adjustment (Collins et al., 2004).
3.1.3. The Role of Children in the Design Process
It can be challenging to involve children in the design process, but it is vital to consider the effects of children as new technology learners. That is why this project chose this method. Children can be users or testers, and researchers can observe them playing with technologies. Children can help researchers to understand the effects of current technologies to develop future educational environments; their answers can be used for technology development.
In the technology design process, a child can be a user, tester, informant, and design partner, and each role can build and affect the technology-design process (Druin, 2002). With the child as a user, researchers can observe, take videos, or test the child’s abilities when using technology. A tester tests technological prototypes that researchers have not presented to the industry yet. Druin (2002) stated regarding the role of tester “children’s relationship to adults can be through indirect observation or feedback; their relationship to technology can be in using prototypes; and the goals for inquiry may range from wanting to better understand usability and design issues, to exploring the educational impact of technology, to using technology as a tool for inquiry about a larger educational issue” (p. 5). As an informant, a child can inform the design process by observing current technologies or making low-technology prototypes to provide ideas on design sketches. In the role of design partner, a child is a stakeholder contributing to the process (Druin, 2002).
3.2 Design Process
Double diamond design is applied to the design process to determine problems and solutions. The double diamond design diagram has four stages: discover, define, develop, and deliver (Design Council, n.d.). The prototype is developed and improved by going through each phase of the double diamond diagram. This method was chosen for this study because it has four clear stages. This project required a clear structure for the design process. By diverging and converging with each phase, this method can help to clarify the design process and answer the research question. In the “discover” phase, observations of other organizations were made, and interviews were conducted with non-computer-science college students and computer science experts to determine current problems. These interviews were also intended to investigate the target group’s needs. In the “define” stage, positive or negative perspectives about programming education were elicited and defined as the foundations for solutions. In the “develop” phase, findings from the “define” stage were improved to create a prototype. For the last stage, which is the “deliver” phase,
the final prototype was tested and evaluated.
Figure 2. The modified double diamond model adjusted for software products and services. (Week 4, 2017)
Design Council established the double diamond diagram to explain the design process in a simple, graphical way. Figure 2 provides a brief visual depiction of each stage. According to the Design Council (n.d.), “the double diamond diagram maps the divergent and convergent stages of the design process, showing the different modes of thinking that designers use” (p. 6).
A “discover” stage consists of market research, user research, managing information, and design research groups. It is a stage that initiates ideas or inspiration to discover the target market’s needs and to determine current problems (Design Council, n.d.). The “define” stage transforms needs into business objectives, including project development, project management, and project sign-off. Based on findings from the “discover” phase, problems are defined and changed into solutions (Design Council, n.d.). Design Council (n.d.) states that in the “develop” phase, “design-led solutions are developed, iterated, and tested within the company. Multi-disciplinary working, visual management, development methods, and testing are key activities and objectives” (pp. 19–20). The “deliver” stage includes final testing, approval, launch of products or services, evaluations, and feedbacks. “Deliver” is the final stage of the process. This stage is for discovering any limitations before manufacturing and for damage testing (Design Council, n.d.).
This project was divided into two phases. In the first phase, “discover and define”, it included pre-interviews, workshops, post-interviews, discussion, and analysis of the results. In the next phase, the “develop and deliver” phase, the prototype was developed, evaluated, and refined iteratively.
Discover and Define
The “discover and define” phase was an exploration of users’ needs through interviews and workshops. This phase started with the pre-interviews with non-computer-science college students, female experts in computer science, and children. The workshop included two activities: interaction with a programming robot and making toys. Participants did some programming exercises using a programming robot, mBot: they wrote songs, turned on different colors of LED lights, and made several movements. The second activity was making toys. After these two activities, participants took part in a post-interview session or discussion.
Develop and Deliver
The “develop and deliver” phase encompassed developing the prototype. In the develop and deliver phase, the prototype was developed, tested, and refined based on the results from the interviews, workshops, and observations. This process of improving the prototype was iterative. Figure 3 provides a visual depiction of the design process.
Figure 3. A description of the design process adapted from double diamond design. (Design Council, n.d.)
3.3 Design Method
This research used several methods throughout the design process. Interviews and workshops are used to collect data. Affinity mapping and thematic analysis are selected to elicit keywords from interviews and workshop results.
3.3.1. Affinity Mapping and Thematic Analysis
To analyze the interviews and workshops, affinity mapping and thematic analysis were used. These methods were used to identify keywords for the design requirements. Affinity mapping and thematic analysis were chosen for this research because they are effective methods for analyzing qualitative data. Thematic analysis was used to code the interviews, workshops, and observations; affinity mapping was used to sort the codes. These procedures identified several keywords, which are essential factors in the design requirements for the prototype.
Affinity mapping is a method that organizes a large amount of data into relevant groups (Dam & Teo, n.d.). It analyzes groups to gain insights from user research (Naylor, 2019). Through affinity mapping, researchers can find patterns and themes by grouping data and determining connections between groups (Naylor, 2019). Affinity mapping helps to connect individual factors and define problems to develop solutions (Dam & Teo, n.d.). Affinity mapping has four steps: recording all notes, looking for connecting patterns, making a group for each theme, and creating a name for each theme (Naylor, 2019).
Thematic analysis identifies repeated subjects, ideas, and patterns of meaning (Caulfield, 2019). It is commonly used for qualitative data, such as interview transcripts, and has a six-step process: “familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up” (Caulfield, 2019, paragraph 2). Familiarization requires becoming familiar with the data by transcribing, reading, or taking notes from interviews (Caulfield, 2019). Coding is highlighting phrases and summarizing them with shorthand labels (Caulfield, 2019). For example, one of the interviewees said that she was afraid of programming: this sentence could be coded as “worry.” Generating themes requires determining patterns among codes and combining them into themes (Caulfield, 2019). These methods helped this study
to consider interviews and workshops thoroughly. Table 1 provides an example of thematic analysis.
Interview Notes Codes
She said the robot helped her to
understand programming more easily. That is because the perception of programming was presented in a practical way. She argued that programming could sound scary somehow. There is anxiety connected with the word.
• Positive perspective of tangible objects • Fear
Table 1. A thematic analysis from the non-computer-science students’ interviews.
3.3.2. Interviews
Interviews are the most common methods to collect qualitative data (Dörnyei, 2007). Researchers can investigate people’s opinions thoroughly since interviews are reliable methods to elicit narrative data (Kvale, 1996). Interviews are useful methods to construct and negotiate meanings in an ordinary setting (Cohen, 2007). Interviews enable not only to analyze, and report detailed opinions of interviewees, but also to make them express their feeling and thoughts (Alshenqeeti, 2014).
The interviewees were non-computer-science college students, female experts in the computer science (CS) field, and children who have not had any programming education. It was a one-to-one, semi-structured interview, including several open-ended questions. There were two interview sessions: the pre-interview and the post-interview. The aim of the interviews was to understand each group’s perspective on programming and any changes in the participants’ perceptions and to obtain feedback on the workshop. This research wrote down interviews on a computer. The researcher jot down interviewees’ answers and key points. This research did not record interviews, because of confidentiality of the details or fear of repercussions and intimacy. Interview answers were used as data to determine the design requirements for developing the prototype.
3.3.3. Future Technology Workshop (FTW)
The future technology workshop method was adopted because it aims to design and build models for future technologies and it offers several workshop sessions. Future technology workshop tries to discover solutions for future technology by investigating the difference between present and future technology. It has three stages: brainstorming, fantasy, and implementation. These stages are another reason why this method was chosen. Each stage was applied to the structure of this project’s workshops.
The first phase is a brainstorming stage, which is called the critique phase. It is a stage for uncovering current problems connected with the design task. For this project, this stage encompassed the pre-interview sessions. The second is the fantasy phase, in which participants picture a future based on problems identified in the critique stage (Vavoula & Sharples, 2007). In this project’s workshop, this stage involved making toys. Participants imagined and created their own toys, whatever they wanted to make. The last phase is the implementation phase. In this phase, there is a discussion about the results of the critical and fantasy phases and feasible action plans are developed (Vavoula & Sharples, 2007). This stage embodied the discussion sessions for this project. In the discussion session, participants shared their opinions about what causes young women to become interested in programming.
The workshop consisted of programming exercises, paper prototypes, and a discussion. Participants were divided into two teams: the programming team, and the design team. Each team had their own mission. After finishing the task, they switched team so that every participant could experience all the activities. The main concept of the programming exercise was playing with a robot. The purpose of the paper prototype session was making toys. Finally, there was a discussion about what motivates young women to become interested in programming. Participants could talk about their opinions freely. The researcher observed workshops and took notes about what participants did during the programming exercises or paper craft sessions.
Brainstorming Stage
on their experiences of the programming session. People can form creative ideas through brainstorming (Vavoula & Sharples, 2007). During the brainstorming, there were some questions about previous thoughts concerning programming and personal interest in programming. There were also open-ended questions that helped participants to express themselves regarding programming. With the answers from the open-ended questions, it was possible to analyze what aspects participants had enjoyed, found interesting, learned, and interpret their perspectives into solutions.
Fantasy Stage (Envisioning)
In the fantasy stage, participants could make their low-technology prototypes. In this stage, participants were intended to envision future technology and consider how it supported their lives (Vavoula & Sharples, 2007). The role-play was intended to bring the future into the present. Participants in this project role-played with the prototypes while imagining that the prototypes were already existing future technologies to help to learn programming. This role-play helped participants to engage in future activities and make their ideas tangible.
Implementation Stage (Discussion)
After making their own toys (the paper prototypes) and the fantasy stage, the participants took part in a discussion session. In this session, participants discussed a topic based on their experience related to their needs and prior sessions (Vavoula & Sharples, 2007). Through a group discussion, it is possible to “reflect and gain insights into what activities in the far future might be like in relation to the design task at hand” (Vavoula & Sharples, 2007, p. 18).
4 Design Process
This research starts with observations of organizations that teach programming to children. After the observation, this research had interviews and workshops with college students individually. The next step was interviews with experts. The following step was also with college students and included craft sessions. Children were the last participants for this research. Figure 4 provides a visual depiction of a research sequence.
Figure 4. The sequence of a research.
4.1 Interview
Non-computer-science female college students
This interviewee group was female college students who had studied neither computer science nor engineering. The research chose this interviewee group to figure out the reason why they did not study in computer science. It is to see what factors made them not get interested in programming. The pre-interview included nine questions and asked about participants’ previous experiences, and their current and future interest in programming. The post-interview was conducted after the programming exercises and papercraft session. The post-interview included five questions about the participants’ opinions after the workshop. Some of the questions were the same as the pre-interview questions to discover whether participants’ perspectives had changed after the workshop. The last question was intended to start a discussion.
Experts in the Technology Field
The experts’ group was women who were professionals in the technology field or female students who were studying or had studied computer science. This interviewee group is selected to elicit the necessary skills for studying computer science and see what made them want to get involved in this field. The interview with experts aimed
Organization
Observations Interviews (Experts)
Interviews Workshops (Children) Interviews Workshops (Students) Interviews Workshops (Students)
to uncover inspiration, to elicit ideas for solutions, and to discover what aspects motivated them to contribute to the technology field. The interview had fifteen questions about what, when, and how they became motivated to participate in this field. Their experiences in computer science classes or projects were also discussed.
Children
The last group was young women between nine and fifteen years old. This research decided to interview children since they were the closest to the target group. By interviewing this target group, their needs could be discovered. This interview was more like a natural conversation rather than a formal interview since the interviewees were children. This interview used question cards: every question was written down on paper to help the children to understand the questions better. Most of the interview questions were the same as the questions for the non-computer-science female college students. Several questions about their friends were added to understand what young women of this age enjoy doing, their favorite toys and hobbies to find out interviewees’ general interests apart from programming. Post-interview questions included participants’ opinions about the workshop and whether tangible objects were helpful in understanding programming. Table 2 provides what researches have done with each group.
Table 2. Number of interviewees and number of times the works were conducted. Interview
(Number of people) (Number of times) Workshop
Organization observations (Number of times) Non-CS students 5 (3 individuals + 1) 4 Experts in CS 6 Children 3 1 Total 14 5 2
4.2 Workshops
Programming Exercise
The programming exercise used an mBot,1 and participants programmed using mBlock,2 a programming software using Scratch.3 Participants could create music, turn LED lights on, and make the mBot move forward and backward. Besides these basic actions, they could code any activities that they wanted to try with mBot, for example, line followers or obstacle sensing.
Figure 5. The programming exercises.
Making toys
The design team’s activity was to produce toys using paper prototypes. The main concept of this session imagined that it was possible to create any toy desired. For this session, a variety of craft tools were prepared: colored paper, colored pencils, stickers, colored threads, scissors, and glue. Participants could make anything that they wished: any shapes, colors, animals, or cars. Participants could decorate the mBot to develop it in any way that they wanted. Participants took part in a role-play imagining that their toys already existed.
1 mBot [website], https://www.makeblock.com/mbot, (accessed 1 April 2020). 2 mBlock [website], https://www.mblock.cc/en-us/, (accessed 1 April 2020). 3 Scratch [website], https://scratch.mit.edu/, (accessed 1 April 2020).
Figure 6. Making toys.
Discussion
In the discussion session, the question “what motivates young women to become interested in computer programming and computer science?” was discussed. Participants were encouraged to express their opinions and they could draw a mind map for brainstorming. The aim of this session was to share everyone’s opinion: participants could talk about challenges as well as solutions.
Tools
During the workshop, participants played with an mBot. The mBot is a programming robot for children to learn visual programming, electronics, and robotics. It uses the programming software mBlock, a drag-and-drop programming language based on Scratch (Makeblock, 2013). Scratch is a block-based programming language, which is a programming language where instructions are mainly represented as blocks. Children can use to make their own interactive stories, games, and animations (Scratch, n.d.). By coding in Scratch, children can express their thoughts and see the results of their decisions (Papavlasopoulou et al., 2019). Scratch is a visual programming tool and it is less cognitively challenging for computational practices, so it is suitable for problem-solving and creative thinking (Papavlasopoulou et al., 2019). Papavlasopoulou et al. (2019) argued that “the children had the opportunity to plan, problem-solve, code, debug, collaborate, communicate, and reflect on their coding experience using Scratch” (p. 422).
The reason for using a robot was based on theories that robots are an effective tool for developing educational performance. There are many types of research concerning using robots as an educational tool (for example, Kumar and Meedan, 1998; Beer et al., 1999; Nostrand, 2000; Weinberg et al., 2001). Some reports show that robotics projects help to develop performance in mathematics, physics, and engineering (Nagchaudhuri et al., 2002). Children are attracted by robots because there of the increasing depictions of robotics in television programs, magazines, websites, robot toys, and construction sets (Petre & Price, 2004).
Petre and Price (2004) claim that “in robotics, students’ learning is concrete, associated with phenomena they create, observe and interact with, and so the abstractions they derive are grounded and relevant. Problems are open-ended, permitting many solutions and many approaches. Hence, robotics affords opportunities for learning problem-solving techniques and processes, integrates several domains, exposes realistic constraints and issues, and leaves room for creativity” (p. 148).
Robotics is multi-disciplinary and includes many technical topics, such as algebra, electronics, and programming, so it can make a particular educational impact (Johnson, 2002). Robots have characteristics such as concreteness, complexity, and a connection to deep human needs that make robots a motivating technology. Furthermore, children can play with robots without adult intrusion and use robots to extend their knowledge to solve problems. Thus, robots have robust pedagogic value (Petre & Price, 2004).
4.3 Description of Workshop
The First Workshop (Individual)The participants were women college students who were not studying computer science. The participants’ goal was to turn on LED lights and play music with an mBot. The workshop’s goal was to understand the perceptions of women college students who were unfamiliar with programming and to discuss the design of a better environment for programming from women’s perspectives. This workshop was also intended to discover whether tangible objects provide better comprehension of programming or not.
The Second Workshop (Team)
The second workshop’s participants were also female non-computer-science college students. They worked as a team. Workshop activities were similar to the first workshop and included two interviews and one programming exercise. Paper prototypes (creating toys) and discussion sessions were added from the second workshop. Participants drew anything that they wanted to make as their toys. Next, they discussed ways of supporting young women’s interest in the computer science field.
The Third Workshop (Children)
The third workshop’s participants were young women from nine to fifteen years old. The workshop session consisted of interviews, programming workshops, paper prototypes, and a discussion. There were more craft materials for the third workshop than for the second since the target group was children.
5 Results
In this chapter, the information and data collected are analyzed and synthesized to determine design requirements for the prototype.
5.1 Interview Results
There was a total of thirteen participants for the interviews. Five interviewees were non-computer-science female college students, six interviewees were female experts in computer science and three were young women aged between nine and fifteen years old who had no previous knowledge of programming.
All the interview results were analyzed and synthesized to develop design requirements for the prototype. By using affinity mapping and thematic analysis, this research identified several keywords for the problems and solutions. It was vital to collect information and data to discover the problem and devise solutions; the collected data provided direction to guide the search for an answer.
5.1.1. Analysis and Synthesis of Interviews
1. The reason why non-computer-science female college students are not interested in computer science and programming
There were several answers to this question. One was fear and anxiety toward programming and computer science. These subjects sounded difficult and reminded the students of mathematics. Most of them did not like mathematics, or they were not good at mathematics. They thought people who study this field are smart and superior at mathematics, unlike themselves. They had low confidence in their academic abilities in general.
Programming could sound scared somehow. Programming is just the word that people do not know, but there is anxiety behind the word. She thought she was not that smart enough to study programming. She is a bit scared of learning programming since she does not know much about it (Interview notes, 2020)
The second reason was that they had not had opportunities to experience programming. None of their schools provided proper computer science and programming classes; thus, they had no opportunities to learn it. It was impossible to say whether they liked programming or not because they had no idea what programming even looks like or how it works.
From her elementary school to high school, there were any classes about programming. Her middle school had a computer class, but it was about Microsoft offices. She does not have any programming experiences for her entire life.She had never gotten any programming educations (Interview notes, 2020).
The third reason was that they did not have a willingness to commit their time to programming. They did not have time for it, or they had never thought about it. They did not want to spend extra time on programming as they already have strong interests in their major fields, such as politics or social science. Computer science and programming were not attractive enough to cause them to change their interests. However, some interviewees were willing to learn programming if it was related to their interest fields.
She just never felt that interested in programming. She did not have a passion for investing her time for it. If someone were interested in it, they had to do extra-curriculum. It was something that you have to commit your own extra time (Interview notes, 2020).
2. The reason why women experts in the technology field like computer science and programming
The majority of answers related to creativity and outcomes. The interviewees said that programming is a way of making something new; people can create something that they want to use. In addition, programming shows precise results. It provides visible outcomes so that people can see their effort. Furthermore,
they liked the way of thinking in computer science and programming: it is not merely memorizing something, but more about thinking logically. As well as logical thinking, they also enjoyed solving problems: it can be a struggle, but it is good to feel accomplished after determining a brilliant solution.
The reason why she gets interested in programming is its creativeness and precise results. She likes programming because it is a way of solving problems. Furthermore, she said programming made people be creative and draw smart solutions. She likes the creativity of programming and problem-solving (Interview notes, 2020).
The last reason did not concern programming itself but the benefits of being in this field. There are many job opportunities for those with computer science degrees. There is a high demand for programmers, and insufficient supply. As the introduction to this thesis argued, there is a gender gap in the computer science field, so many companies are trying to hire female programmers. Thus, once a woman has studied in this field, it is much easier for her to find a job than it is for a man.
3. Necessary skills to be successful in computer science and programming
The interviewees talked about several different kinds of skills, such as logical thinking, mathematics, creativity, and a structured mindset for problem-solving. However, most of them argued that the most crucial ability was motivation. Motivation can make a significant difference between people who have it and those who do not have it.
For the skills to be successful in programming, she emphasized motivations rather than any skills. She said people do not need skills for programming, but they should have motivations for it. She also claimed that it is good to have logical thinking and structured mindset (Interview notes, 2020).
