Motivation: Undergraduate computing students increasingly turn to generative AI (GenAI) tools to understand abstract concepts through analogies. Analogies compare an unfamiliar concept to something familiar, but judging whether the comparison holds requires knowledge of both. GenAI may also embed assumptions about who the learner is. GenAI education research centers on output correctness, leaving students' critical reception of analogies largely unexamined. Method: We investigate how students evaluate the accuracy, appropriateness, and assumptions in GenAI-generated analogies, and their perceptions of interest-personalized versus generic technical explanations. Ten students with CS2 experience participated in a pre-survey, a think-aloud task with linked-list and recursion explanations, and a semi-structured interview grounded in the Paul-Elder framework. They judged accuracy, clarity, engagement, and trust separately. Results: Most participants described interest-personalized analogies as more engaging or memorable than generic technical explanations, while trust was mixed. Some trusted the tailored analogies more; others scrutinized them more closely or distrusted the tailoring. Participants with deep source-domain knowledge identified structural flaws requiring that knowledge to recognize. Because personalization and explanation format differed together, these findings do not isolate an effect of personalization alone. Implications: A familiar source flips the student's role. On the concept they are still learners, but on the familiar source they are the expert, and that is the position from which an analogy can be judged. We call this two-sided analogy auditing. GenAI systems should ask what students know, not just what interests them, and treat a flawed analogy as something to inspect and fix rather than accept.

Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python, N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.

Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) offers cognitive design principles for coordinating views, but less is known about what shapes learners' engagement with available representations. We conducted a within-subjects study with 19 undergraduates who had completed CS1 and CS2. Students completed think-aloud tasks, reflective interviews, and webcam-based gaze tracking while using a multi-representational probe with synchronized code, memory, and metaphor views, and Python Tutor, across scope, while loops, and linked lists. Gaze analysis showed that students spent nearly half their time focused on code despite available visual scaffolds. Students without prior experience anchored even more heavily in code and engaged minimally with metaphor views. Interviews identified three factors shaping selective engagement: agency, as students sought control over cognitive effort rather than simply having it reduced; representational fit, as identical designs differed in whether they felt helpful or overwhelming; and legitimacy, as some students avoided metaphorical scaffolds they perceived as childish or insufficiently rigorous for university-level work. These findings suggest that multi-representational tools in computing education require attention to affective and social factors alongside cognitive design. Practical considerations include positioning visualizations as verification instruments, offering toggleable abstraction levels, and framing tools to signal disciplinary legitimacy. More broadly, the themes help explain why cognitively sound visualization tools may fail to engage the students they are designed to help.

Self-explanation deepens understanding by giving learners an opportunity to reflect on what they are learning in a structured way. However, many students struggle to engage in it effectively. We investigate whether large language models (LLMs) can scaffold selfexplanations in a flipped computer organization course. In an A/B test, one group used a fixed prompt to compare their explanations with an expert's, while another engaged in an interactive dialogue with an LLM to identify gaps. Although the overall quality of the explanation did not differ significantly between conditions, some students (non-native English speakers and women) reported greater comfort and perceived value when using the LLM.

Self-explanation promotes active learning by having students articulate their conceptual understanding in their own words. This study investigates whether the timing of self-explanations (before vs. after solving an exercise) relates to performance in a flipped, second-year computer organization course. Although students who self-explained before the exercises achieved higher course marks, the difference was not significant. Still, these findings suggest that early self-explanation may better prepare students for problemsolving when learning new concepts.

Machine learning (ML) has become increasingly important for students, yet university-level ML courses are often perceived as challenging and time-intensive. This study explores the perceived challenges and motivations of students in a university ML course to inform curricular and teaching strategies. Through 5 surveys conducted in two instances of a 12-week introductory ML course, we examined students' engagement with both theoretical and practical aspects of ML. Results indicate that while students initially express strong interest in applying ML concepts, their reported interests can shift toward theoretical foundations. Challenges in both theory and practice are reported, including difficulties in mathematical notation and vectorization of gradient components, as well as model implementation. Students also discuss the time commitment required in a course with both theoretical and practical content. We recommend aligning course content with student motivations, providing targeted support for mathematical notation and vectorization, and balancing theoretical depth with practical application.

Creating a supportive community in introductory programming courses is vital to student success, yet forums meant to facilitate this can cause stress due to social comparison. According to social identity theory, students are more likely to engage and feel a sense of belonging when they perceive connections with their peers. This study investigates whether peer-modeled posts that simulate students exhibiting desirable engagement behavior can reduce feelings of isolation and foster social connection among students. We introduced curated posts modeling expected student behavior -- covering content, providing emotional support, and offering study tips -- into Q&A forums for two introductory computing courses. These posts were inserted using different student accounts. Surveys and forum data were analyzed to measure the impact on students' feelings of isolation. Students responded positively to the seeded posts, reporting a significant reduction in feelings of isolation. Notably, women reported feeling less isolated after seeing the posts more than men, and many students reported feeling relieved that other students had the same worries and concerns as them. Seeding peer-modeled posts can significantly reduce student isolation and foster a greater sense of belonging in competitive academic contexts. However, future work may explore alternative delivery mechanisms, such as instructor posts framed as ''questions from last year,'' to determine if they can achieve similar effects.

Novice programmers often struggle to understand how code executes and to form the abstract mental models necessary for effective problem-solving, challenges that are amplified in large, diverse introductory courses where students' backgrounds, language proficiencies, and prior experiences vary widely. This study examines whether interactive, multi-representational visualizations, combining synchronized code views, memory diagrams, and conceptual analogies, can help manage cognitive load and foster engagement more effectively than single-visual or text-only approaches. Over a 12-week deployment in a high-enrolment introductory Python course (N = 829), students who relied solely on text-based explanations reported significantly higher immediate mental effort than those using visual aids, although overall cognitive load did not differ significantly among conditions. The multi-representational approach consistently yielded higher engagement than both single-visual and text-only methods. Usage logs indicated that learners' interaction patterns varied with topic complexity, and predictive modelling suggested that early experiences of high cognitive load were associated with lower longer-term perceptions of clarity and helpfulness. Individual differences, including language proficiency and prior programming experience, moderated these patterns. By integrating multiple external representations with scaffolded support adapted to diverse learner profiles, our findings highlight design considerations for creating visualization tools that more effectively support novices learning to program.

Motivation: CS2 students often struggle to connect low-level memory operations with high-level structural understanding, particularly with recursion and linked data structures. Visualizations can aid learning by making underlying relationships more visible and helping students track complex behavior. While such tools exist, most emphasize procedural flow without scaffolding abstraction. We explore whether multiple external representations (MERs), which are a well-established approach in other STEM fields that is underutilized in computing education, can support structural abstraction through synchronized, layered visualizations. Method: We used a tri-pane interface, grounded in Ainsworth's DeFT framework, as a design probe. The system synchronized source code, memory diagrams, and abstract structural views. It was embedded into a 12-week CS2 course (n = 695) focused on recursive, reference-based structures. Students engaged with three MER-aligned activities during the semester. 440 completed an end-of-term survey, and 301 provided open-ended feedback. We analyzed perceived clarity, usability, and mental effort using validated scales and thematic coding. Results: Students described the MER visualizations as clear, intuitive, and helpful, especially for recursion and pointer-heavy logic. Many said the integration of views supported "seeing the bigger picture" and reduced reliance on rote tracing. Usability ratings were strong, mental effort was moderate, and non-native English speakers reported higher clarity than native speakers. Implications: As an initial exploration, these findings offer early empirical grounding for the further use of MERs in CS2. The work surfaces representational affordances, learner strategies, and design tensions that can inform future tools and evaluations of abstraction support in computing education.

One of the most important choices a researcher makes is selecting a research paradigm and methodology, without which they will be hampered in their search for knowledge and answers. Ideally, researchers consider all possible approaches and select the most appropriate one, but several factors constrain this: time, familiarity with certain approaches, and the uncertainty of the benefit of change. Computer Science Education Research draws from many research disciplines, exposing new possibilities that may not be seized due to these limitations. The Research Alternatives Exercise is designed to expand researchers' awareness of different research approaches through a rapid, card-based prototyping exercise. This approach reduces the formality and potentially confrontational aspects of being asked to consider new approaches, allowing participants to examine their current research and plans through different paradigms, methodologies, and constraints, without it being a direct criticism of their current choices. This can increase the level of understanding of research framing and practice, strengthening the arguments for using a given approach and introducing valid arguments to adopt different approaches, with low time investment.

This research evaluates voice-based self-explanations as a pedagogical tool in preparation for lectures, assesses user preferences between voice and text, and derives design insights. We report two studies: Study 1, a quasi-experimental field study, with 247 participants divided into voice-based (N = 83), text-based (N = 81), and choice (N = 83) conditions. Study 2 uses semi-structured interviews (N = 16) to explore perceptions of the interaction paradigms in-depth. Results from the first study revealed a general preference for text, though voice users produced longer responses and more topic-related keywords. Over time, the preference for voice increased among students, from 10% to 46%, when given a choice. Study 2 suggested that factors like social presence contribute to hesitance toward voice-based explanations, with a cognitive load, self-confidence, and performance anxiety also influencing medium preferences. Our findings highlight design recommendations and demonstrate the potential of voice-based self-explanations in educational settings, indicating that mixed interfaces might better meet diverse needs.

This pilot study explores how visualization strategies, grounded in multiple representations theory, impact novice students’ engagement, and cognitive load during program tracing tasks. Students were were shown a visualization of the three-variable swap problem at the start of an introductory programming course (CS1) at a large public North American research-intensive university. We compared three conditions: interactive multiple representations, Python Tutor (a single-representation tool), and text-only methods. Preliminary results indicate that interactive multiple representations increase engagement for students with prior programming experience, while no significant differences were observed for students without prior experience. These findings suggest that while multiple representations may boost engagement, identifying how to effectively support students of all experience levels and reduce cognitive load requires further study.

Self-explanations show promise for engaging students with preparatory materials, yet research into the types of self-explanations submitted in computing is limited. This paper examines student perceptions of self-explanation prompts in a flipped databases course, building on existing research that highlights the advantages of self-explanations in such contexts. We present our findings on students’ perceptions of the utility of self-explanation prompts and analyze the nature of the explanations generated across distinct topics. The results suggest that self-explanations not only facilitate a deeper understanding of the subject matter but also promote the discovery of new connections and examples through rewording explanations. Furthermore, errors within self-explanations offer valuable insights for the early identification of misconceptions.