Being culturally sustaining in computing education: a systematic literature review

Computer Science (CS) education has caught a wave -- of media attention, public support, public/private commitments, broad-based participation by educators, and a surge in student enrollments at the undergraduate level. It is a startling change over just the last 5 years. Over that 5 years, much has been accomplished at the high school level. The Exploring Computer Science and Advanced Placement® CS Principles courses were created to engage and inspire a diverse mix of students. Hundreds of teachers and university faculty have collaborated to develop course materials, assessments, MOOCS, and models of teacher professional development. Over 2,000 high schools now offer new CS courses, but that leaves out more than 34,000.Even then, students will need more than a single course, they will need a K-16 CS pathway. At the K-8 level, CS does not have the decades of research on the teaching and learning that is available to many other, more established disciplines. A stronger evidence base is needed as the basis for pedagogy, curricula, standards, and teacher preparation. The CS community must put greater emphasis on research in CS education and broadening participation, and it must build stronger collaborations with researchers in related disciplines.Over the last 5 years, college-level CS departments have been inundated with students. This growth is fueled by a strong job market for CS majors and an increasing awareness that computation is fundamental to many other industry sectors and academic disciplines. How will departments cope with increasing numbers without sacrificing access or quality? How will they respond to increasing diversity of ethnicity and gender, but also of interests, and career goals of their students? For those interested in CS education, it's an exciting time, but it comes with some urgency. This talk will discuss how to catch the current wave, using it to full advantage.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.

This chapter focuses on research in computer science education. The importance of including this topic in the MTCS course stems from the fact that computer science education research can enrich the prospective computer science teachers’ perspective with respect to the discipline of computer science, the computer science teacher’s role, and students’ difficulties, misconceptions, and cognitive abilities. Consequently, this knowledge may enhance the future work of the prospective computer science teachers in several ways, such as lesson preparation, kind of activities developed for learners, awareness to learners’ difficulties, ways to improve concept understanding, and testing and grading learners’ projects and tests. We first explain the importance of exposing the students to the knowledge gained by the computer science education research community. Then, we demonstrate different issues addressed in such research works and suggest activities to facilitate with respect to this topic.

Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing. Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students? Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS. Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators ( n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators ( n = 185), analyzed the data, and considered how these findings shape research conducted over the last year. Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases. Practitioner perspectives also indicated that schools serving students from lower-income families had (1) a greater decrease in the number of students who received information about CS/CTE pathways; (2) a greater decrease in the number of girls enrolled in CS classes; (3) a greater decrease in the number of students receiving college credit for dual-credit CS courses; (4) a greater decrease in student attendance; and (5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses.

Motivation: Recent efforts to expand K-12 computer science education highlight the great need for well-prepared computer science (CS) teachers. Teacher identity theory offers a particular conceptual lens for us to understand computer science teacher preparation and professional development. The emerging literature suggests that teacher identity is central to sustaining motivation, efficacy, job satisfaction, and commitment, and these attributes are crucial in determining teacher retention. While the benefits associated with a strong sense of teacher identity are great, teachers face unique challenges and tensions in developing their professional identity for teaching computer science. Objectives: This exploratory study attempts to operationalize computer science teacher identity through discussing the potential domains, proposing and testing a quantitative instrument for assessing computer science teachers’ professional identity. Method: We first discussed the potential domains of computer science teacher identity based on recent teacher identity literature and considerations on some unique challenges for computer science teachers. Then we proposed the computer science teacher identity scale, which was piloted through a national K-12 computer science teacher survey with 3,540 completed responses. The survey results were analyzed with a series of factor analyses to test the internal structure of the computer science teacher identity scale. Results: Our analyses reveal a four-factor solution for the computer science teacher identity scale, which is composed of CS teaching commitment, CS pedagogical confidence, confidence to engage students, and sense of community/belonging. There were significant differences among the teachers with different computer science teaching experiences. In general, teachers with more computer science teaching experience had higher computer science teacher identity scores on all four factors. Discussion: The four-factor model along with a large national dataset invites a deeper analysis of the data and can provide important benchmarks. Such an instrument can be used to explore developmental patterns in computer science teacher identity, and function as a pedagogical tool to provoke discussion and reflection among teachers about their professional development. This study may also contribute to understanding computer science teachers’ professional development needs and inform efforts to prepare, develop, and retain computer science teachers.

Computer Science (CS) Education research, specifically when focusing on secondary education, faces the difficulty of regionally differing political, legal, or curricular constraints. To date, many different studies exist that document the specific regional situations of teaching CS in secondary schools. This ITiCSE working group report documents the process of collecting, evaluating, and integrating research findings about CS in secondary schools from different countries. As an outcome, it presents a category system (Darmstadt Model), as a first step towards a framework that sup-ports future research activities in this field and that supports the transfer of results between researchers and teachers in CS education (CSE) across regional or national boundaries. Exemplary application of the Darmstadt model shows in several important categories how different the situation of CSE in secondary education in various countries can be. The Darmstadt Model (DM) is now ready for discussion and suggestions for improvement by the CSE-community.

Computer science is poised to become a core discipline in K12 education, however there are unresolved tensions between the definitions and purposes of computer science and public education. This study's goal is to explore how logistical and conceptual challenges emerge while designing a comprehensive K12 computer science program in a public school district. While the policy infrastructure for K12 computer science education is rapidly developing, few districts have yet implemented computer science as a core discipline in their K12 programs and very little research has explored the challenges involved in putting ideas into practice. This study reports on a committee designing a comprehensive K12 computer science education program at a small public school district in California. Through a grounded-theory qualitative interpretation of committee-member interviews and board meeting transcripts, we surfaced three themes which were the primary points of tension: how computer science is defined, how it ought to be taught, and what process ought to be used to answer these questions. Grounding these tensions in the academic discourse on K12 computer science education, this study offers recommendations to other districts designing comprehensive computer science education and suggests future directions of computer science education research that will be most useful to stakeholders of these processes.