ACM Transactions on Computing Education最新文献

In recent years, computing education researchers have investigated the impact of problem context on students’ learning and programming performance. This work continues the investigation motivated, in part, by cognitive load theory and educational research in computer science and other disciplines. The results of this study could help inform computing assessment design. If the context and authenticity of a programming problem aid student performance then, instructors’ time in creating appropriately contextualized programming problem descriptions is time well-spent. On the other hand, if the context of a programming problem hinders performance, then instructors should leave it out of programming problems.

Recent studies investigating the impact of programming problem context on student success have arrived at different conclusions.

Presented here is a series of experiments, conducted over three years, investigating the impact of context on novice programmers’ success in algorithmic programming assignments using three contextualized tasks and their generic counterparts. This experiment series also looked into the possibility of ‘authenticity’ as a factor affecting performance. Common sense would suggest that a student would perform better on a problem if they understood or cared about it. Contextualization could provide authenticity and authenticity could provide interest. Research suggests that perceiving a problem as authentic has a positive effect on engagement and learning. Alternatively, if a problem is ‘just an abstract set of numbers’, it may be harder to make sense of the details and the lack of context could consequently contribute additional cognitive load to an already challenging assignment.

The results of this study show that assignment context and problem context authenticity have no effect on the performance of novice programmers. We think however, that contextualization could be worth investing in to support students’ interest in computing. Additional implications of the results suggest that instructors can assign equivalent versions of the same problem in varied contexts to suit their students’ interests without worrying if the context will hinder performance.

Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Chile

Integrating graduate education with professional skills development is still a challenge. People's beliefs about learning impact their learning processes. Therefore, we need to understand the mindset of graduates to determine best practices for promoting professional skills development. In this study, we explore the perspective of computing graduates within an Engineering educational program. This study aims to answer the following research questions: Which skills do computing graduates perceive they need in the workplace, and how do they position themselves regarding these skills? What learning beliefs do computing graduates hold regarding the skills required in the workplace? The research team conducted semi-structured interviews with computing graduates within an Engineering program to analyze their experiences. The interviews were analyzed using grounded theory. As a result, the participants viewed professional and technical skills as independent, with professional skills perceived as more relevant to work success. The participants considered themselves to possess these skills. Our findings identify four learning beliefs within a fixed and growth mindset: (1) An essential personal characteristic that context may influence, (2) A learning outcome determined by early experiences, (3) A learning process associated with informal learning experiences, and (4) A learning process associated with formal learning experiences. We acknowledge the differences in perception between alums and the industry regarding possessing essential professional skills. We also highlight the need to position these skills as learnable during lectures contributing to Computing and Engineering Education.

Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect for data quality.

To this end, we outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. With Co-ML, beginners can build image classifiers through a distributed experience where data is synchronized across multiple devices, enabling multiple users to iteratively refine ML datasets in discussion and coordination with their peers.

We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13-18 worked in groups to build custom ML-powered mobile applications. Our analysis reveals how multi-user model building with Co-ML, in the context of student-driven projects created during the summer camp, supported development of DDPs including incorporating data diversity, evaluating model performance, and inspecting for data quality. Additionally, we found that students’ attempts to improve model performance often prioritized learnability over class balance. Through this work, we highlight how the combination of collaboration, model testing interfaces, and student-driven projects can empower learners to actively engage in exploring the role of data in ML systems.

Research shows that the global society as organized today, with our current technological and economic system, is impossible to sustain. We are living in an era in which human activities in highly industrialized countries are responsible for overshooting several planetary boundaries, with poorer communities contributing the least to the problems but being impacted the most. At the same time, technical and economic gains fail to provide society at large with equal opportunities and improved quality of life. This paper describes approaches taken in computing education to address the issue of sustainability. It presents results of a systematic review of the literature on sustainability in computing education. From a set of 572 publications extracted from six large digital libraries plus snowballing, we distilled and analyzed the 89 relevant primary studies. Using an inductive and deductive thematic analysis, we study (1) conceptions of sustainability, computing, and education, (2) implementations of sustainability in computing education, and (3) research on sustainability in computing education. We present a framework capturing learning objectives and outcomes as well as pedagogical methods for sustainability in computing education. These results can be mapped to existing standards and curricula in future work. We find that only a few of the articles engage with the challenges as calling for drastic systemic change, along with radically new understandings of computing and education. We suggest that future work should connect to the substantial body of critical theory such as feminist theories of science and technology. Existing research on sustainability in computing education may be considered rather immature as the majority of articles are experience reports with limited empirical research.

As computing educators begin to recognize that their students need strong ethical foundations, there is a growing interest to integrate meaningful ethics education into undergraduate computing curricula. In order to achieve this, it is crucial to understand how students respond to ethical interventions in the classroom. This review examines the acceptance of ethical interventions in undergraduate computing courses, using the realist synthesis method to identify underlying theories of student acceptance, and refine them through available studies. Four theories were identified in a synthesis of 13 reports, providing insight into what may improve student attitudes towards ethical interventions in which contexts and under which circumstances. The findings of this realist review offer guidance to intervention designers, researchers, and educators seeking to meaningfully engage students with ethics in computing education.

Collaboration is an important aspect of computing. In a classroom setting, working with others can increase a student’s motivation to attempt more challenges, reduce the difficulty of complicated concepts, and bring about greater overall success. Despite extensive research in other domains, there has been minimal exploration within computing on what impacts a student’s decision to seek social assistance in highly competitive university environments. To understand what affects introductory programming students’ social help-seeking behavior in this context, we conducted 32 semi-structured interviews with students and performed thematic analysis and qualitative coding on the ensuing transcripts. Our qualitative analysis revealed 18 significant factors. We noticed that the decision to seek social help involved a two-fold process; first, the decision to engage in social help-seeking and subsequently, the decision of who to ask for help. Furthermore, we found that help-seeking in computing is not fundamentally different from other disciplines, although some of the factors were unique to the topic of computing and the specific environment of this study. Factors related to communication style, the type of question being asked, and the school’s cheating policy were central when discussing code, an integral part of computing. Regarding the environment, students repeatedly reported that the competitive major, the explicit and implicit class standards, and feelings of intimidation, among others, influenced them. These findings suggest that understanding both steps and the sociocultural context is important in order to effectively lower the barriers to asking for help.

This article reports on a design-based research study centered on the conception and the assessment of the Pyrates application. This online serious game aims at introducing Python programming to K12 students while easing the transition from block-based to text-based languages. After we present the various aspects underlying the block-to-text transition as well as the related existing applications, we describe the design of Pyrates. Firstly, we built the levels of the game in order to deal with the different fundamental concepts of programming in a constructivist approach. Next, we were inspired by advantageous characteristics of block-based programming editors to create the editing environment of Pyrates. In order to assess this conception, we tested the application in eight classrooms with 240 French 14-15 years old students. Students’ activity traces have been collected and were augmented by a qualitative online survey. By analysing this data set, we showed that the levels’ design generally allows to apprehend the targeted concepts consistently with the constructivist principles. Regarding the editing environment, we established that it supports the block-to-text transition in several aspects: concept transposition (general models and illustrative examples), reduction of errors (beginners aware syntax analyser), command catalog (programming memo for discovery and syntax reference), and program composition (copy button which limits keyboarding). Finally, Pyrates, which has already been played over 140,000 times, offers practitioners an environment that facilitates the transition from blocks to text, as well as a serious game to master the fundamental concepts of Python programming, and novel avenues to follow for tool designers.

Objectives. We aim to understand, from a motivational perspective, how Black undergraduate women in computing make sense of their intersectional computing experiences. We examine their motivation to major in computing, their experiences as Black women in computing, and how these vary across institutional contexts.

Participants. A sample of 77 Black undergraduate women in computing programs (computer science, computer engineering, information systems, information technology, and software engineering) from diverse college settings was recruited to participate in the study.

Study Method. Participants responded to an online survey that included several Likert-scale measures for identity, motivation and retention variables, demographic questions, and two open-ended questions: Why did you choose to study computing? and What is it like for you to be a Black female student in computing?

Findings. Key themes that emerged from our quantitative and qualitative analyses revealed the range of motivational factors driving Black undergraduate women to study computing and the nuances across their intersectional experiences in the computing education context. Additionally, Black undergraduate women located in HBCU computing education contexts demonstrated both similar and unique perspectives and experiences compared to their counterparts in non-HBCU settings.

Conclusions. In addition to the isolation, pressure, power dynamics and epistemic violence experienced by Black women in the intersectional computing education context, the findings of this study display the unique cultural-social-psychological strengths of Black women in their positive valence and resilience, as well as their joy, empowerment, and achievement in computing. Increasing and complicating our knowledge of the motivations and intersectional experiences of Black women in undergraduate computing education, as well as understanding when and how their beliefs and perspectives vary across institutional context, will better inform efforts to retain them and promote their success both in college and into their careers.