ACM Transactions on Computing Education最新文献

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques’ feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools’ performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

Improving equity and inclusion for underrepresented groups in the field of Computer Science (CS) has garnered much attention. In particular, there is a longstanding need for diversity efforts that center the experiences of Black women, and specific actions to increase their representation—especially given the biases that they often encounter in the field. There is limited research concerning Black women in CS, specifically their conceptions of the field and their overarching CS identity development. More research in this area is especially important given the marginalization that Black women often experience at the intersections of their race and gender. Guided by a combination of critical theoretical lenses, this qualitative study examines Black women's conceptions of what it means to be a Computer Scientist and the degree to which those conceptions map onto how they see themselves in the field. Moreover, we explore experiences that help to bolster Black women's CS identity. The findings highlight key aspects of what it means to be a Computer Scientist for the Black women in this study—notably the ability to use computing to make societal contributions. Also, the results accentuate key nuances in the participants’ personal CS identification, particularly as it relates to the resilience required to overcome unique barriers that many Black women encounter when engaging within the field. Moreover, the findings highlight the importance of social support systems to facilitate Black women's CS identity development. Implications for policy and practice within education and industry are discussed.

Self-regulation of learning (SRL) is an essential ability for academic success in multiple educational contexts, including programming education. However, understanding how students regulate themselves during programming learning is still limited. This exploratory research aimed to investigate the regulatory strategies externalized by 51 students enrolled in an introductory programming course. The objective was to identify the SRL strategies used by these students during multiple phases of the learning process and compare the SRL behavior of high and low-performers. The following research questions guided this investigation: RQ1) What regulation of learning strategies are used by programming students?; and RQ2) How do the SRL strategies used by high and low-performing students differ?. The findings demonstrate that learning to program involves complex psychological resources (e.g., cognition, metacognition, behavior, motivation, and emotion) and that students present heterogeneity in their SRL repertoire. In addition, high and low-performing students showed significant differences in how they regulate, which can contribute to understanding the factors that may contribute to learning programming. Lastly, we argue that for analyzing SRL strategies, it is necessary to consider the specificities of programming education, which motivated the development of a conceptual framework to describe the identified strategies and regulatory phases in this learning domain.

The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the Association for Computing Machinery (ACM) computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of “ethics,” and those that did used many different conceptions, from broadly-applicable ethical theories, to social impact, to specific computing application areas (e.g., data privacy, hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., standalone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning.

Objectives. At the K-12 level, “CS for All” initiatives across the country strive to increase equitable access to and participation in computer science (CS). However, there are many open questions about the implementation and effectiveness of these initiatives, including the extent to which exposing young people to CS early on can shape their longer-term CS interest and engagement. In this paper, we examine CS participation among 6^th-8^th grade Black girls—and assess whether CS participation during middle school shapes CS interest and engagement during their first year of high school. We focus on Black girls in the hopes of developing a more nuanced and rigorous understanding of computing experiences at the intersection of racism and sexism in this field.

Participants. The focal group of students in this study are 6^th-8^th grade Black girls from New York City. We employ a comparative lens in this paper, contextualizing the CS experiences and outcomes of Black girls to Latinas, Asian, and White girls, as well as Black boys.

Study Method. We primarily rely on quantitative data for this paper, applying a critical lens to our analyses and interpretation. More specifically, we conduct descriptive analyses of course-taking patterns as well as survey data that focus on student attitudes and beliefs about CS. We then carry out inferential analyses of students’ administrative records examining how, if at all, middle school CS participation is related to high school outcomes for Black girls. We employ a comparative lens and rely on qualitative data to make sense of our results.

Findings. We find that, troublingly, Black girls in the district are disproportionately less likely to receive CS instruction in middle school. Black girls are also less likely than Black boys, Latinas, and White girls to feel that they belong in CS. However, Black girls in CS courses report similar levels of engagement, family, and peer support, as well as value for CS relative to other students in the district. Finally, we find that participation in CS courses in middle school does not increase the likelihood that Black girls will select high schools that offer CS courses or take a CS course during their first year of high school.

Conclusions. Our findings suggest that to increase equitable access and participation in CS, it is not enough to simply expose students to CS coursework. We call for sustained attention to the experiences that Black girls have in their CS classes as well as broader structural barriers that might shape CS course-taking.

Background and Objective. Teacher assessment research suggests that teachers have good conceptual understanding of CT. However, to model CT based problem-solving in their classrooms, teachers need to develop the ability to recognize when and how to apply CT skills. Does existing professional development (PD) equip teachers to know when and how to apply CT skills? What factors should PD providers consider while developing trainings for CT application skills?

Method. This retrospective observational study used a binomial regression model to determine what factors predict teachers’ probability of performing well on a CT application skills test.

Participants. Participants of this study were 129 in-service K-12 teachers from a community of practice in India.

Findings. Results show that teachers who have received at least one CT training, who have a higher teaching experience, and are currently teaching CT will have a higher probability of applying CT skills correctly to problems irrespective of the subject they teach and their educational backgrounds. However, receiving higher number of CT PD trainings was a negative predictor of teachers’ performance.

Implications. Implications for school administrators, professional development providers, and researchers are discussed. Teachers need ample opportunity to teach CT in their teaching schedules. Continuous professional development does not necessarily result in improved CT application skills unless careful consideration is given to the pedagogies used and to the resolution of misconceptions that teachers may have developed in prior training. Mixing plugged and unplugged pedagogical approaches may be beneficial to encourage transfer of CT application skills across different types of problems. Lastly, there is a need to develop valid and reliable instruments that measure CT application skills of teachers.

Cheating has been a long standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools have provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a dual-anonymous “quality assurance” marking exercise across four end-of-module assessments across a distance university CS curriculum. Each marker received five ChatGPT-generated scripts alongside 10 student scripts. A total of 90 scripts were marked; every ChatGPT-generated script for the undergraduate modules received at least a passing grade (>40%), with all of the introductory module CS1 scripts receiving a distinction (>85%). None of the ChatGPT taught postgraduate scripts received a passing grade (>50%). We also present the results of interviewing the markers, and of running our sample scripts through a GPT-2 detector and the TurnItIn AI detector which both identified every ChatGPT-generated script, but differed in the number of false-positives. As such, we contribute a baseline understanding of how the public release of generative AI is likely to significantly impact quality assurance processes. Our analysis demonstrates that, in most cases, across a range of question formats, topics and study levels, ChatGPT is at least capable of producing adequate answers for undergraduate assessment.