Background and Context: To align with growing demand for cloud experts, universities offer courses to equip students with relevant skills. However, limited research exists on best practices for teaching cloud computing, which integrates multiple areas of computing. Objective: This study examines a large-scale cloud computing curriculum that integrates project-based authentic assessment with AWS Academy resources, and proposes a pedagogical framework for teaching cloud skills. Method: We designed a curriculum inspired by Project-Based Learning (PBL) and the rigour/relevance framework to bridge the theoretical gap between industry certifications and university requirements. Student reflections on PBL were analysed using a mixed-methods approach, supplemented by educator interviews and student surveys. Findings: This study identifies key challenging areas in teaching cloud computing, including cloud security, scalable architectures, and foundational knowledge of networking, Linux, and debugging. Educator interviews highlighted difficulties aligning AWS Certification content with university delivery structures, limited group work opportunities, and increased workload from certification exams and frequent curriculum updates. Implications: Industry-provided cloud curricula, while valuable for technical currency, are not sufficient alone to develop the integrated architectural and problem-solving skills required in real-world contexts; deliberate pedagogical scaffolding is needed to bridge the gap and connect vendor-specific training with academic learning outcomes. Grounded in both the rigour/relevance framework and authentic learning principles, the four-stage, project-based curriculum presented here offers a replicable model for embedding certification-aligned content into a semester-based subject. This study also identifies prerequisite knowledge and cloud computing concepts that students perceived as particularly challenging, providing insights that may inform curriculum design and teaching practice.
Current practices in computing education for accommodating neurodivergent students, while well-intentioned, often remove students from the very collaborative activities they will encounter in the profession. By doing so, these practices disadvantage both neurodivergent students, who graduate without practicing collaborative experiences the profession expects, and their neurotypical peers, who lose the opportunity to learn how to work across neurotypes. This results in students being academically included while remaining professionally excluded . In this opinion piece, we argue that this gap reflects accommodation defaulting to the path of least administrative resistance, rather than to design choices that scaffold participation. Treating neurodiversity as a design challenge for inclusion, rather than as a compliance problem, would change what students experience in our classrooms; and, in time, what they encounter in the profession.
Background . Despite growing awareness, the lack of digital accessibility continues to hinder meaningful inclusion. According to WebAIM’s 2025 report, only 5.2% of the top one million website homepages fully meet accessibility standards—a mere 3.1 percentage points improvement in Web Content Accessibility Guidelines (WCAG) compliance over the past 6 years. This limited progress highlights a persistent skills gap in the technology industry, largely driven by software engineers’ insufficient foundational knowledge and technical competence in digital accessibility. Numerous studies indicate that formal education often fails to adequately equip future software professionals with the necessary accessibility competencies. Methods . We developed AccessQuest , a highly interactive serious game designed to teach five WCAG Level-A success criteria to computing students. AccessQuest is part of a design-based research project with two goals: advancing research knowledge and providing educational value, with each informing the other. This article reports on the first iteration of the design-based research cycle, with future iterations planned to refine the game design based on the findings presented here. The game’s effectiveness was evaluated using a combination of quantitative and qualitative measures, including pre- and post-tests, gameplay data analysis, post-gameplay surveys, and focus group discussions (N = 67, all undergraduate computing students). Results . Learners demonstrated significant knowledge gains, with pre–post test scores rising from 2.82 to 7.49 ( \({\rm{p}} < 0.001\) ). For the open-ended questions in the post-survey concerning the application of WCAG in real-world contexts, participants scored an average of 6.72 out of 10, with strong inter-rater agreement ( \(\kappa=0.84\) ). Gameplay data reflected the learning-curve effects, with longer times spent in Level 1 and progressively shorter durations in later levels, with no spikes. Focus group discussions revealed that students conceptualized accessibility as an inclusive design concern, recognized its ethical and business value, and described the game as engaging and relatable. Voluntary replays reflected curiosity and enjoyment, while participants suggested enhancements such as clearer navigation, pause functionality, and reward systems. Conclusions . AccessQuest shows promise as a scalable intervention for bridging the accessibility skills gap. It improved conceptual understanding and supported the application of knowledge in open-ended, real-world scenarios while leveraging gamification elements to sustain engagement and motivation. These findings highlight the potential of serious games to enhance accessibility education and better prepare future software professionals for inclusive technology design.
Objectives . Our replacement of RAPTOR, a flowchart-based programming environment, with Python in an introductory computing course at a post-secondary institution provided a novel opportunity to explore the impacts of the programming language on students’ success with programming, perception of different programming languages, and interest in computing. Participants . We conducted a cluster-randomized study that involved the 1083 students who took our introductory computing course in the 2019–2020 academic year. The student populations in both versions of the course were similar with respect to sex, race, standardized test scores, predicted performance, and grade point average (GPA) in courses that satisfy general education requirements. Moreover, our course is a general education requirement, which precludes self-selection bias by students predisposed to study a computing or technical discipline. Study Methods . Our mixed methods research design compares student performance in both versions of the course, including by demographic groups; explores factors that predict student performance; summarizes students’ perceptions of RAPTOR and Python; investigates how the programming language influences students’ interest in computing majors; and examines how the programming language affects students’ performance in subsequent courses. Our data regarding student achievement is archival, collected from our student information system, which we augment with voluntary-provided feedback from questionnaires and course evaluations. Findings . Student achievement is correlated with prior programming experience and standardized test scores, yet students performed similarly overall in both versions of the course despite one racial group performing significantly worse with Python, a difference largely attributable to imbalances in academic preparation among the two cohorts for that racial group. Students’ interest in computing and their performance in subsequent computer science courses were not impacted by the programming language they learned in our introductory computing course. Nevertheless, students overwhelmingly perceived Python to be more valuable. Conclusions . Our work is unique in its context (i.e., taking place at the post-secondary level) and scope. The similar performance of students learning each programming language differs markedly from prior research that found all students perform better when using a block-based modality. Moreover, students’ preference to learn Python, particularly among those who subsequently major in a computing discipline, argues against a one-size-fits-all approach when teaching introductory programming. Our results raise important questions about the role of an introductory computing course in promoting equity and engaging students from historically marginalized or underrepresented groups in computing fields.
Objectives : The rapid spread of AI—especially Large Language Models (LLMs)—is reshaping software engineering and raising questions for education. We examine the effectiveness of integrating AI into software engineering education. Participants : This study involved 48 undergraduate students enrolled in a junior-level software engineering course. Study Methods : We implemented an intervention, aligned with ACM/IEEE curriculum guidelines, that integrates an LLM-based prompt session with a conventional lecture. The lecture introduces the topic and explicit strategies for prompt design; the prompt session engages students in constructing, running, and iteratively refining prompts, analyzing LLM outputs, and reflecting on outcomes. A structured prompt-design framework is adopted to standardize context, inputs, tasks, and expected outputs to ensure clarity and consistent engagement. Findings : A pilot in a junior-level course showed modest gains over lecture alone: on survey items repeated immediately after the lecture (before the prompt session) and again after the prompt session, scores improved by an average of 4.19%. Students rated the approach favorably, with scores ranging from 3.78 to 4.16 and an overall average of 4.02 out of 5, with the largest gains in prompt-construction skills, rising by 7.01%. By contrast, gains in identifying incorrect LLM outputs were only 3.67%, and improvements in understanding LLM limitations were just 2.22%. Conclusions : Embedding LLM prompt sessions can supplement software engineering instruction but, in the study setting, produced limited benefits. The findings suggest that LLMs should be positioned as supportive tools, with emphasis on prompt design, critical evaluation of outputs, and expanded time for hands-on practice and assessment. Future work should include controlled, longer-term studies and alignment with industry needs to determine when and how LLM integration measurably improves learning outcomes.
Generative AI (GenAI) systems, particularly large language models (LLMs), are rapidly transforming the technological environment in which higher education operates. Universities have responded with a wide range of policies, from strict prohibition to unrestricted adoption, often without considering how learning processes differ across academic disciplines. In this opinion piece, I argue that computing education represents a special case within the broader debate on AI in education. In my view, programming learning occurs within artifact-centered environments where students routinely interact with code repositories, libraries, and documentation. As a result, students develop expertise through the study, adaptation, and integration of existing computational artifacts. I, therefore, contend that GenAI expands an existing ecosystem of learning artifacts rather than introducing an entirely new form of assistance. Building on this observation, I propose a discipline-aware policy framework for AI use in computing education. This framework advances five principles for AI governance in programming courses: learning-centered policy design, transparency and disclosure of AI use, tiered assignment-level AI permissions, evidence-based integrity evaluation, and equitable access to AI tools. I further argue that effective policy cannot rely solely on institutional rules or AI-detection mechanisms. Instead, scalable educational infrastructure—including code provenance tracking, conversational code explanations, and AI-assisted oral checks—is necessary to make policy implementation operational in large and online computing courses. By bringing together insights from learning theory, computing education research, and institutional governance, I offer a forward-looking view of AI policy that aligns technological capabilities with pedagogical goals. Rather than treating GenAI solely as a threat to academic integrity, I frame AI governance as a design problem: ensuring that AI-assisted tools support student reasoning and conceptual engagement within modern programming learning environments.
Increasingly, students with no prior computer science (CS) training are introduced to programming in non-CS courses, reflecting the rising importance of computational methods across disciplines. Non-CS faculty teaching such courses with a computational component are often unsure how to best teach the computing concepts their students need, and their students struggle to apply to new problems the computational solutions they have seen before. This paper describes surveys and an exploration of teaching materials undertaken at three liberal arts colleges to better understand what computing tasks students in non-CS courses are expected to master and how computing is taught in these courses. We identified a set of computing tasks that faculty across disciplines indicate to be relevant for their courses: collecting data, preparing data, translating data, data visualization, and navigating the file system. Our exploration of teaching material revealed, however, that faculty devote relatively little time to teaching the foundational computing concepts that underlie these tasks, such as variable naming and assignment, basic control flow, working memory vs. persistent storage. We propose that students’ computational problem solving skills can be strengthened by integrating more robust teaching of foundational computing concepts into non-CS courses. To support non-CS faculty in doing so, we developed a library of short videos on foundational computing concepts that can be flexibly integrated into existing course structures. In an initial evaluation, faculty piloted the library in courses from across the sciences, social sciences, and math and found the video library helpful for teaching computing skills.
Teaching software engineering (SE) to diverse undergraduates is challenging due to the abstract nature of the concepts and the varied backgrounds of the learners. Objectives . This study investigates the impact of relevance, clarity, detail, examples, and preferences on the perceived usefulness of supplementary materials, with a focus on learning styles and gender. Participants . The study included 96 undergraduates enrolled in an Introduction to SE course at a large public university in the southeast United States. Study Methods . We conducted an adaptive experiment using contextual Thompson Sampling to select supplementary materials for nine course topics based on student ratings, gender, and topic. We used surveys to assess learning styles, perceived usefulness of supplementary materials, and the factors (relevance, clarity, detail, examples, and preferences) that impacted perceived usefulness. Findings . The results show that all five factors have a positive influence on usefulness, with detail and preferences being the most impactful. Visual learners rated materials higher when aligned with their preferences, while verbal learners were less influenced. The results showed no differences related to gender. Conclusions . Matching the delivery mode of supplementary materials to the learning styles of the students had minimal effect, emphasizing preferences over rigid categorizations. The adaptive algorithm showed potential but varied across topics, indicating a need for refinement. The findings suggest the need for preference-driven personalization in SE education.
Educational programming languages (EPL), for all their success in enabling computing education at scale, regularly exclude learners by embedding assumptions about ability, class, culture, language fluency, and identity. Further, most EPL designs are not governed in ways that are responsive to the needs of learners and their communities on the margins of computing, raising questions about how the design processes behind EPL could be organized to ensure they serve everyone equitably. Building upon discourse on diversity, educational justice, and design justice, we propose seven justice-centered design requirements for EPL, arguing that they should be accessible , liberatory , transparent , cultural , obtainable , democratic , and enduring . For each, we examine why these requirements are necessary and offer examples of languages that do and do not meet them. Throughout, we surface constraints that EPL impose on being justice-centered and grand challenges for research to be able to overcome them.
Background : Motivated by the need for a diverse technological workforce, broadening participation in computing (BPC) efforts aim to increase the representation of people who identify as women, African American or Black, Hispanic or Latinx/a/o/e, Native American, Indigenous, persons from economically disadvantaged backgrounds, and persons with disabilities. Research on BPC efforts has highlighted exemplar institutions and activities, but tends to focus on what initiatives computing departments have undertaken and the outcomes of these initiatives. Purpose : Given this prior focus on what initiatives computing departments are undertaking, we propose refocusing on how change happens to increase our collective capacity for impactful change efforts. We apply a well-known organizational change framework, John Kotter’s eight-stage process of leading change, to examine catalysts for change in computing departments, who contributes to this work, and what motivates the work. Doing so can deepen our understanding of BPC efforts and how to enhance them. Theoretical Framework : Kotter’s framework for leading change includes the following eight stages: (1) establishing a sense of urgency, (2) creating the guiding coalition, (3) developing a vision and strategy, (4) communicating the change vision, (5) empowering broad-based action, (6) generating short-term wins, (7) consolidating gains and producing more change, and (8) anchoring new approaches in the culture. Methods : Using a practitioner research approach, we conducted interviews with 13 faculty and staff members across R1 and R2 U.S. institutions via Zoom. Participants were recruited based on their involvement in BPC efforts at their respective institutions. We used inductive and deductive analytic coding approaches to capture how the Kotter framework illuminated participants’ experiences leading BPC efforts. Findings : Kotter’s stages provide a useful breakdown of processes with which to understand and illuminate catalysts for change in computing departments. Across our participants, we see examples of how each stage not only takes shape in different departments, but also how subsequent stages build upon the one(s) before it. Findings reveal a variety of factors that motivate the urgency for computer science (CS) departments to engage in BPC work, along with the importance of top-down leadership and institutional resources. Implications : Kotter’s organizational change model provides an appropriate frame to guide BPC efforts and may be useful to practitioners. Findings from this study illuminate several areas for CS departments to address in order to build capacity for organizational change efforts that support BPC goals. These include examining a variety of departmental data, effectively using meetings and other communications mechanisms, and revising hiring and promotion policies.
Hybrid programs (and courses) that combine computing science with other disciplines, such as Bioinformatics and Digital Humanities, have gained prominence. They attract a diverse student demographic, including underrepresented groups in STEM subjects, and equip graduates with complementary skills that have a significant social impact and are highly valued by employers. However, the conceptual foundation of such offers remains ambiguous in Computer Science Education (CSEd) research and practice, creating a gap that necessitates research into the ways disciplinary integration manifests. This article presents a theoretical framework for the study of disciplinary integration and offers empirical and conceptual insights into the ways in which undergraduate computing science students engage at the crossroads of disciplines. Being in such a position influences their academic trajectories and has potential implications for their academic identities. Employing a qualitative case study methodology, we conducted semi-structured interviews with six undergraduate students enrolled in a computing science while concurrently enrolled in non-CS degree programs. We analyzed the data using deductive thematic analysis. The participants were purposefully sampled to ensure the diversity of perspectives. A typology of interdisciplinarity was used to map the engagement of students across disciplinary boundaries and characterize their modes of integration. Our analysis reveals a broad spectrum of disciplinary engagement, ranging from multidisciplinary to transdisciplinary. Participants respond to (inter)disciplinary participation in two ways: assimilation or integration. They negotiate interactions with multiple disciplinary cultures through these responses. This study promotes Disciplinary Integration (DI) as a subject of research and a consideration in the curriculum in computing education and beyond. It does so by highlighting the complexity of crossing disciplines as well as the implications of students’ different responses when navigating multiple fields of study. Also, it challenges the common implicit assumption of computing being purely instrumental for other disciplines as uni-directional utilitarian transfer. From a theoretical perspective, this study empirically substantiates the descriptive utility of the theoretical framework while suggesting an extension of a taxonomy of interdisciplinarity with three additional dimensions that are particularly salient in computing education: epistemic proximity of the disciplines, directionality of transfer, and temporal fluidity as a characteristic of student positioning. This work lays a foundation for future research on disciplinary integration and its implications for the evolving role of computing in academia and society.
As digital technologies increasingly shape young people’s social, academic, and civic lives, cybersecurity has become a foundational concern in K–12 computing education. While prior research offers valuable insights into curricula, tools, and pedagogy, less attention has been paid to how these efforts operate within broader educational systems. This systematic literature review examines K–12 cybersecurity education as an interconnected learning ecology embedded in sociotechnical and institutional infrastructures. Drawing on learning ecology and infrastructure perspectives, we synthesize international research published in ACM computing education venues across four dimensions: conceptual rationales, disciplinary contexts and topics, pedagogical enactments and mediational tools, and systemic conditions. Our synthesis identifies five infrastructural and ecological inner contradictions: Top-Down Origins, Fragile Learning Progressions; Disciplinary Contexts, Uneven Reach; Inclusive Aims, Exclusive Infrastructures; Technical Emphasis, Marginal Socio-Ethical Integration; and Local Successes, Fragile Educational Ecologies. By theorizing these systemic tensions, the review argues for integrative approaches to designing coherent K–12 cybersecurity learning ecologies. Building on these insights, we provide a set of guiding questions for future empirical research to explore, test, and refine design principles and policy strategies.
Objectives . This work investigates students’ mid- to long-term takeaways from the course “Communication and Soft Skills” conducted at a European university and relates insights to soft skills interventions in higher computing education described in the literature. A major objective was to situate findings from researching a student-centered course into a broad, international context of promoting soft skills. Research questions address the students’ view on the course’s lasting effects, students’ memories of the course, and the identification of success factors for promoting soft skills in higher computing education based on the current study. Participants and Method . Research encompassed an online survey with open and closed questions that had been emailed to 1,036 course graduates reaching back up to 21 semesters and receiving 101 responses. Following a parallel mixed methods design, quantitative findings were obtained using descriptive statistics while qualitative data were analyzed via a qualitative content analysis. Findings . The study sheds light on which features of the course tend to stay on students’ minds and impact their way of being and acting in their jobs and in society. Our findings indicate that the experiential, student-centered nature of the course and the project-based didactics provided sustained whole-person learning as illustrated by versatile memories and self-reports about constructive changes in attitudes, behavior, skills, and knowledge of respondents. Based on our mixed methods study, we suggest implications for effective soft skills interventions. Conclusions . Only limited literature exists on long-term effects of promoting soft skills in computing-related higher education. This study is among the first to address this literature gap. Student-centered characteristics such as creating an equal environment, being listened to and included in decisions, working in teams on real projects, and getting immediate feedback turned out to leave lasting traces on students’ minds and behavior. The findings from our research contribute an evidence-based source of motivation for staff who are devoted to nourishing students’ soft or professional competencies aside from knowledge and technical skills.
Peer review is an underutilized yet potentially powerful strategy for fostering evaluative judgment and design literacy in visualization education. Critiquing visual work requires domain-specific competencies that extend beyond generic peer feedback; however, widely adopted platforms such as Canvas provide limited support for the cognitive and visual demands of visualization critique. We introduce VisPeerReview, a visualization-specific learning analytics dashboard (LAD) designed to scaffold peer feedback through an integrated visualization display, rubric-guided prompts, and inline annotation tools. We evaluated VisPeerReview through a three-phase mixed-methods study conducted in an undergraduate data visualization course, comparing it with Canvas’s default peer-review workflow. Drawing on interaction logs, peer-review text, and survey responses, we found that VisPeerReview elicited significantly longer and more linguistically rich feedback and was consistently preferred by students. Sentiment analysis further indicated more positive evaluative language and clearer reviewer intent under the dashboard-supported condition. Beyond tool evaluation, this study offers the first systematic comparison between Canvas and a visualization-specific LAD, demonstrating how theory-aligned instructional interface design—grounded in representational competence and learningsciences frameworks—can meaningfully improve the quality of peer feedback in computing education.
Motivation and Objectives . Computational Thinking (CT) has become a central theme in K–12 Computer Science education. Over the past 20 years, multiple conceptualizations of CT have emerged, many forming the basis for assessment instruments. One such conceptualization was developed for the large-scale International Computer and Information Literacy Study (ICILS), which assessed CT across 24 countries using representative sampling. The size and sampling quality of the ICILS dataset allow for robust statistical analyses which in turn will be of interest to researchers and policy-makers alike. This study situates the ICILS 2023 conceptualization of CT within other established frameworks and conducts a secondary analysis of the ICILS 2023 CT data on non-cognitive antecedents and processes. Methods . Structured deductive content analyses compare the ICILS 2023 items with those from the Bebras Challenge on Informatics and Computational Thinking ( Bebras ) and the Computational Thinking Test ) ( CTt ), mapped across three CT frameworks—ICILS, Shute et al. and Weintrop et al.—and aligned with Bloom’s revised taxonomy. Linear regression analyses on the data of the 20 educational contexts that provided not only CT performance data but also a complete coverage of student data relative to the predictors of CT performance studied in prior work examine the predictive effect of non-cognitive factors on CT performance. Results . The qualitative analyses showed that the ICILS 2023 CT items can be mapped to existing frameworks. Conversely, items from both Bebras and CTt can be mapped to the ICILS framework. The distinct, partially overlapping profiles of the instruments across the frameworks as well as Bloom’s taxonomy indicate that they are complementary in assessing CT, confirming and expanding prior comparisons of Bebras and CTt . The regression analyses indicate no single dominant predictor of CT performance. The association of socio-economic status, gender, or the home language was consistent with prior findings, and predictors related to learning processes, however, vary across educational contexts. Discussion . Our results demonstrate that ICILS 2023 items can be mapped onto multiple established CT frameworks, supporting their broader validity and utility for comparative research. The findings of the regression analysis underscore the complex interplay of non-cognitive factors affecting CT and illustrate the significance of contextual interpretation within educational systems.
We present an experiment designed to evaluate the teaching assistant (TA) ecosystem in computing programs, particularly in relation to whether or not in-person, pedagogically focused TA training, based on social constructivist learning theory, impacts TA and student experiences in office hours. We collect data from computing programs at 5 universities over 2 years, amounting to more than 300 TA responses and 4,600 student responses. While previous studies in computing education have identified TAs’ potential to increase student sense of belonging as well as which stances TAs adopt to demonstrate care towards students, to our knowledge, this work represents the widest spanning collection of student and TA data to date, both specifically within computing and in general. We make three important contributions to computing education research from quantitative analysis of our data. First, we identify race, course, university, class standing, and whether or not a TA has been trained as significant factors for TA experiences. Specifically, for the effects of training, we find that TAs who receive any training are more confident in encouraging questions and engagement, creating a welcoming environment, telling students when they do not know an answer, and conducting office hours. Our second contribution characterizes student experiences in office hours. We show that student disability status is associated with significantly different office hours experiences across universities, followed by race, course, and term. Our final contribution focuses on the students who do not attend office hours. To our knowledge, this is the first study that analyzes students in computing programs who do not attend office hours as well as those who do attend office hours. We find that students with historically marginalized identities are more likely to report avoiding office hours due to being externally -avoidant, being pushed away by external factors, such as not feeling comfortable with any TAs for the course, than their peers. In contrast, students with historically privileged identities are more likely to report not attending office hours because they are internally -avoidant, reporting that they do not need help. Finally, we show that students indicating they have a disability report both worse experiences in office hours and more reticence to attend office hours, leaving them in a particularly challenging situation. These findings specifically speak to student and TA experiences within computing; this is a field that not only has a well-documented and unique history of marginalization based on identity characteristics such as race and gender but it has also seen a boom in enrollment since the early 2000s that has been answered, in part, by computing programs relying on TAs to provide instructional support. Given this unique setting, we create a detailed portrait of the student–TA ecosystem in computing across several universities, characterizing TA experiences, student experiences, and differences that emerge along demographic lines.
As scholars continue to investigate important factors that contribute to Broadening Participation in Computing (BPC) among women, Black, Latinx, and other persistently marginalized groups, there remains a limited understanding of how BPC-related research is conducted. Given that research methodologies evolve over time and the application of empirical evidence to practice and policy can be contingent on the methodologies used, it is critical to review how recent postsecondary BPC research has been conducted. In this quantitative methodological review, we expand upon our prior paper where we conducted a systematic methodological review of postsecondary BPC-focused journal articles and conference proceedings (129 articles for original paper, 153 in current paper) using quantitative methodologies that were published between 2005 and 2023. To enhance transparency in our approaches, this study was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement by Moher et al. as an analytic framework for reporting methodological review processes and findings. Results from our content analysis point to a range of important directions for future BPC research; in particular, studies should (a) incorporate more longitudinal and multi-institutional approaches; (b) investigate outcomes that are currently understudied in BPC research, especially those beyond postsecondary experiences (e.g., career trajectories and experiences); (c) build in more sophisticated and multivariate techniques and models; (d) yield insights on smaller racial/ethnic groups, community college students, and students with disabilities, while also accounting for students’ intersecting identities. Doing so will enable scholars to generate nuanced evidence that can be used to better leverage resources for diversifying and equitizing computing education and tech careers.
Studying the factors that can lead to student success and persistence has been the subject of a large body of CS education research. Our earlier work on tools to engage students in Algorithms and Data Structures courses demonstrated uneven benefits, especially among underrepresented students. Using a national pool of students from 13 colleges/universities in Algorithms and Data Structures courses, we used pre and post online surveys to assess the psychometric characteristics of a revised measure of prerequisite proficiency together with 4 others (confidence in computing, precollege computing experiences, skill with computer applications and course engagement) to test for gender and ethnic differences and to investigate the factors that would be useful in predicting final course grade. We were able to confirm differences in prerequisite proficiency; however, differences were a characteristic of additional factors such as precollege experiences, self-rated skill levels and confidence in computing. Regression analysis showed these differences can be mitigated by directing efforts at multiple components, rather than just prerequisite proficiency. Measures taken in the post-survey at the end of the semester showed a reduced number of demographic differences, when compared to the pre-survey. Prerequisite proficiency and performance scores were found to be significant predictors of course grades.
Cultivating critical thinking ability is an essential goal of higher education. Peer review is an effective means of critical thinking training widely used in open-ended assignment. However, there is a challenge in the teaching practice due to inexperience, subjectivity, and randomness of students’ grading, resulting in low accuracy. To address this issue, we introduce PAStudio, a peer assessment pedagogical tool. It utilizes a pairwise comparison method based on a binary system to significantly reduce the grading difficulty for inexperienced students. It requires students to evaluate projects across multiple dimensions using predefined grading criteria, thereby reducing the subjectivity and complexity of the grading process. To better assist students in iterative project improvement and manage the workload of reviews in PAStudio, we have designed an assessment workflow based on the Swiss system for multiple rounds of pairwise comparison. In this workflow, as reviewers, the student’s assessment rounds are acceptable, and the assessment workload for each round is limited. As the submitter, the student receives two constructive comments from different peers. The process forms a formative feedback loop. In PAStudio, we have also developed functionality to avoid superficial comments using the Bidirectional Encoder Representations from Transformers (BERT) model. As the reviewer writes comments, the system rates them on a five-level scale. A comment rated “Fail” cannot be submitted to the system. To calculate a project’s score accurately, we propose an algorithm based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). This algorithm considers the results of all rounds and the opponent’s strength to calculate the assignment’s overall performance comprehensively. PAStudio incorporated gamification elements such as dynamic leaderboards and badges to boost competition and engagement and enhance assignment quality. We analyzed learning data from the Swift Language Programming course from 2023 to 2024. When measured using Krippendorff’s Alpha, the inter-rater reliability between instructor and peer grading shows a good level of agreement, demonstrating the reliability of the peer grading mechanism. Furthermore, peer grading is significantly associated with enhanced students’ assignment performance and learning engagement, as measured by Kruskal-Wallis test. The results of the course questionnaires indicate that most students perceived the peer assessment approach positively and have greatly benefited from this method.
Objectives . During their studies, software engineering students develop various skills that are crucial for their professional careers. Surprisingly, there is limited research on how these graduates perceive these skills in the workplace. This article details a study that specifically examines the experiences of graduates from Software Engineering and Mobile Solutions programs that were implemented as project-based curricula. The study sheds light on how they have utilized their skills and identified any gaps in their capabilities. Participants. Project-based curriculum graduates (N = 10) who had a few years of work experience after their graduation. Complementary data were collected from three participants who graduated earlier from the traditional curriculum. Study Method. This is a qualitative interview study on graduates’ perceptions of the working life competences they gained during their studies. Data include graduate interviews that were recorded and transcribed verbatim, complemented by field notes. Transcriptions were analyzed using content analysis with both descriptive a priori codes and data-driven coding. Findings. Graduates of the project-based curriculum felt that the skills they acquired during their studies were adequate for their professional work. However, they often noted that communication presented some challenges. They specifically mentioned gaps in communication skills with different audiences, such as customers. Other gaps mentioned included written communication, code maintenance, and test automation. Several interviewees expressed a view that their reluctance to ask questions had hindered their integration into the work community. Conclusions. Project-based education facilitates competence acquisition during studies: Graduates have reported acquiring vital professional non-technical competences such as teamwork, communication, and project management. Moreover, they indicated that they had gained proficient technical skills throughout their academic pursuits. The results of this study could be used to develop curricula and design a survey to collect current perceptions, identify gaps, and generate improvement ideas from a larger number of graduates.
