ACM Transactions on Computing Education最新文献

Many practitioners might struggle with becoming productive in different software engineering (SE) roles due to misalignment of the skills learnt during the university time with what is expected in the industry. Companies spend significant resources to train the personnel, whose academic backgrounds are not only based on “computing disciplines”. Hiring properly trained practitioners allows employers to spend less time while incorporating them more efficiently into the workforce; for employees, knowing the most important skillset is helpful to increase their chance of employability. On the other hand, for academia, understanding the necessary skillset is critical to make curriculum updates. To achieve these objectives, we conducted a survey, which was responded to by 628 software practitioners, who completed their undergraduate degree in Turkey, working in 13 countries. This paper sheds light on the most important (hard and soft) skills in the industry by presenting various cross-factor analyses as well as their coverage in the academic curriculum (mostly in Turkish universities). The results showed that the most important skills are related to various factors such as profiles of the practitioners (e.g., SE role(s), work experience) and the characteristics of the product developed by the practitioner. The findings revealed that both academia and industry should invest in skills improvement: academia can make necessary educational updates according to industrial needs; whereas industry can provide practical experiences to students. By creating the awareness of the expected skillset, both practitioners and academics will benefit from the results, which help close the gaps that can and should be achieved through more Industry Academia Collaborations (IACs).

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.

Programming education is strongly emerging in elementary and high school. Diversity and inclusion are important topics, however, insights on suited programming materials for younger learners with visual impairments are lacking. A wide range of programming materials for children exists, diverse in both what is being programmed (output) and how this is done (input), yet often relying on visual features. An understanding of the usability and accessibility aspects of these different materials is important to inform educational practice and to increase understanding of what makes programming materials suited for low vision and blind children. The aim of this study is to explore the usability and accessibility of programming materials currently used in education to low vision and blind children in the Netherlands. A focus group was conducted with six teachers or IT experts, all working with the target group in special education. The thematic analysis of the discussion of 25 materials (including unplugged lessons, robots and robotic kits, block-based and text-based languages) showed the potential of several materials, especially unplugged lessons, and the continuing search for suited materials and workforms specifically for the blind children. Furthermore, prioritizing “fun” and close connections to children’s daily life as well as careful explorations of usability at the cognitive level came forward as important factors for future research and development in programming materials for low vision and blind children. These insights can contribute to obtaining an inclusive approach to programming for young learners.

Objectives. Java is a popular programming language for use in computing education, but it is difficult to get a wide picture of the issues that it presents for novices; most studies look only at the types or frequency of errors. In this observational study, we aim to learn how novices use different features of the Java language.

Participants. Users of the BlueJ development environment have been invited to opt in to anonymously record their activity data for the past 8 years. This dataset is called Blackbox, which was used as the basis for this study. BlueJ users are mostly novice programmers, predominantly male, with a median age of 16 years. Our data subset featured approximately 225,000 participants from around the world.

Study Methods. We performed a secondary data analysis that used data from the Blackbox dataset. We examined over 320,000 Java projects collected over the course of 8 years and used source code analysis to investigate the prevalence of various specifically selected Java programming usage patterns. As this was an observational study without specific hypotheses, we did not use significance tests. Instead, we present the results themselves with commentary, having applied seasonal trend decomposition to the data.

Findings. We found many long-term trends in the data over the course of the 8 years, most of which were monotonic. There was a notable reduction in the use of the main method (common in Java but unnecessary in BlueJ) and a general reduction in the complexity of the projects. We find that there is only a small number of frequently used types: int, String, double, and Boolean, but also a wide range of other infrequently used types.

Conclusions. We find that programming usage patterns gradually change over a long period of time (a period in which the Java language was not seeing major changes) once seasonal patterns are accounted for. Any changes are likely driven by instructors and the changing demographics of programming novices. The novices use a relatively restricted subset of Java, which implies that designers of languages specifically targeted at novices can satisfy their needs with a smaller set of language constructs and features. We provide detailed recommendations for the designers of educational programming languages and supporting development tools.

This article reviews literature on worked examples in the context of programming activities. We focus on two types of examples, namely, code-tracing and code-generation, because there is sufficient research on these to warrant a review. We synthesize key results according to themes that emerged from the review. This synthesis aims to provide practical guidance for educators and shed light on future research opportunities. While there is established work in some areas (e.g., dynamic code-tracing examples in the form of program visualization tools, utility of subgoals in code-generation examples, and incomplete examples in the form of Parsons puzzles), there are also gaps. Thus, the article concludes with directions for future work on examples in computer science education.

In this paper, I reflect on how my personal conceptions on “theory” have developed and become more diverse and elaborated during my career. I discuss early conceptions I learned in school and during my university studies, followed by the growing awareness of computing education research as a field that is distinct from many areas of computer science. Becoming aware of how research is carried out in social sciences and how theories are used in these contexts raised my interest in understanding what this implies for computing education research as a field, which has been building its own identity during the past 20+ years.

Helping students learn to identify and respond to situations involving discrimination is important, especially in fields like Computer Science where there is evidence of an unwelcoming climate that disproportionately drives underrepresented students out of the field. While students should not be considered responsible for fixing issues around discrimination in their institutions, they do have a role to play. In this paper, we present the results of a study in which 318 undergraduate computer science majors were presented with scenarios of discrimination and asked to identify the issues, rate the severity of the issues, and ideate 3–5 responses to address the described situations. They were also asked to identify which of their responses would likely be most effective in addressing discrimination and which of their responses they would be most likely to use if they were in the situation described in real life. Our results show that while students generally are able to identify various forms of discrimination (sexism, racism, religious discrimination, ethnic discrimination, etc.), any ambiguity in a scenario led to students describing the scenario as less severe and/or as an example of oversensitivity. We also show that students come up with many passive responses to scenarios of discrimination (such as ignoring the situation or wishing it had not happened in the first place). Students in our study were more likely to say they would deploy passive responses in real life, shying away from responses that involve direct confrontation. We observed some differences between student demographic subgroups. Women and BIPOC students in CS tend to think these issues are more severe than men and White and Asian students in CS. Women are more likely to ideate direct confrontation responses and report willingness to use direct confrontation responses in real situations. Our work contributes a methodology for examining student awareness and understanding of diversity issues as well as a demonstration that undergraduate computer science students need help in learning how to address common situations that involve either intentional or unintentional discrimination in an academic environment.