Computing Education

Permanent URI for this collectionhttps://hdl.handle.net/10125/112520

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Now showing 1 - 7 of 7

Can AI Recognize Its Own Reflection? Self-Detection Performance of LLMs in Computing Education
(2026-01-06) Burger, Christopher; Talley, Karmece; Trotter, Christina
The rapid advancement of Large Language Models (LLMs) presents a significant challenge to academic integrity within computing education. As educators seek reliable detection methods, this paper evaluates the capacity of three prominent LLMs (GPT-4, Claude, and Gemini) to identify AI-generated text in computing-specific contexts. We test their performance under both standard and `deceptive' prompt conditions, where the models were instructed to evade detection. Our findings reveal a significant instability: while default AI-generated text was easily identified, all models struggled to correctly classify human-written work (with error rates up to 32%). Furthermore, the models were highly susceptible to deceptive prompts, with Gemini's output completely fooling GPT-4. Given that simple prompt alterations significantly degrade detection efficacy, our results demonstrate that these LLMs are currently too unreliable for making high-stakes academic misconduct judgments.
Beyond Screens: A Family-Centered, Unplugged, Gamified Intervention to Support Social Skills in Autistic Children
(2026-01-06) De Souza Oliveira, Lais Aparecida; Oliveira Da Silva Junior, Luiz; Oliveira, Wilk; Melo Do Nascimento, Isabelle
Gamification has recently been used to improve social skills among autistic children. However, barriers related to screen dependence and unequal access to devices and connectivity, particularly in low-resource settings, limit its suitability. We address these barriers by proposing a set of unplugged, gamified flashcards designed to support family engagement in promoting social skills among autistic children. The flashcards were designed through a multimodal study structured into four steps: (i) systematic literature review, (ii) expert brainstorming, (iii) prototype design, and (iv) interview with a family of autistic children. The study delivered a set of flashcards with a trail-shaped board to support families in activities that promote social skills in autistic children. We contribute a practical, low-cost tool and a replicable design path for mental health and gamification research and practice, focusing on everyday, family-led communication. Primary users are families with the autistic child, alongside therapists and teachers in clinical and school contexts.
Understanding Computer Science Students' Career Fair Experiences: Goals, Preparation, and Outcomes
(2026-01-06) Lee, Briana; Limon, Samantha; Chen, Alyssia; Ka‘Aiakamanu-Quibilan, Kenny; Peruma, Anthony
The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate their motivations for attending, preparation strategies, and learning outcomes, including exposure to new career paths and technologies. We envision our findings providing valuable insights for career services professionals, educators, and industry leaders in improving the career development processes of CS students.
Timed vs. Untimed Assessments in Computer Science Education: Mapping Student Engagement and Learning Progression Through Network Analysis
(2026-01-06) Malisetty, Saiteja; Rastegari, Elham; Siu, Ka-Chun; Ali, Hesham
Assessment plays a critical role in shaping learning outcomes in computer science education. This study applies network analysis to examine how different assessment designs—timed and untimed—affect student engagement and learning progression. Using data from an introductory programming course delivered in both online and in-person formats, we construct performance correlation networks to analyze similarities among students over time. Our findings show that untimed assessments, delivered via an interactive textbook platform, enable more individualized engagement patterns and clearer differentiation in learning outcomes. In contrast, timed assessments produce more standardized performance clusters, potentially masking underlying learning variability. Despite differences in modality, we observe consistent patterns across online and in-person settings, underscoring the influence of instructional design over delivery format. This work contributes to computing education by integrating learning analytics and pedagogical analysis, demonstrating how network-based methods can enhance assessment strategy evaluation and support more adaptive educational environments.
From Fear to Opportunity: Unlocking the Potential of Bidirectional Feedback in Higher Education
(2026-01-06) Kasakowskij, Regina; Haake, Joerg M.
Bidirectional feedback has the potential to significantly benefit students by enhancing learning success and goal achievement, while also enabling teachers to better address students’ needs and improve learning material. However, current feedback practices face several challenges, including students’ difficulty in formulating clear and actionable feedback, the effort required for both students and teachers to engage in meaningful feedback dialogues, and the burden on teachers to manage and respond to a high amount of feedback across diverse learning materials. These challenges often lead to concerns about teacher overload, which can hinder the adoption of bidirectional feedback practices. We leverage an existing bidirectional feedback tool, including its process model, computational design, and Moodle implementation for self-assessment tasks. Using data from three consecutive semesters in a B.Sc. Computer Science distance learning course, we show that the tool does not increase feedback overload for teachers. Instead, it enables a balanced flow of constructive and positive feedback, which teachers found actionable and useful for improving assignments and supporting learning. By comparing the three semesters, we demonstrate that the tool successfully mitigates concerns about feedback overload while maintaining the benefits of bidirectional feedback. This finding highlights the usability and sustainability of the approach, offering a practical solution for integrating bidirectional feedback into higher education without overwhelming teachers.
Opening the Computing Education: Introducing Open Practices for Undergraduate Students
(2026-01-06) Deus, William; Barbosa, Ellen
This study explores the integration of open practices into a computing education course in Brazil. These practices aim to enhance collaboration, material reuse, and knowledge sharing. Despite their potential, the literature lacks clear guidelines and empirical data on implementing such practices in computing education. Over a 12-week academic term, students were given greater responsibility while using open tools and open resources to create and share educational content. The final materials were shared online using open licenses and Open Educational Resources (OER). Evaluation through statistical analysis and student feedback revealed skill development in writing and collaboration, along with improved student performance.
Introduction to the Minitrack on Computing Education
(2026-01-06) Saarela, Mirka; Oliveira, Wilk; Dantas, Pasqueline

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