arXiv subjects

Jessica Wen

Publications and source records attributed to Jessica Wen.

4 recordsLinked to original sources

arXiv2026

Code as Anchor, Memory and Metaphor as Support: Learner Experiences with Multi-View Visualizations

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.

cs.HC↗

arXiv2026

Transforming GenAI Policy to Prompting Instruction: An RCT of Scalable Prompting Interventions in a CS1 Course

Despite universal GenAI adoption, students cannot distinguish task performance from actual learning and lack skills to leverage AI for learning, leading to worse exam performance when AI use remains unreflective. Yet few interventions teaching students to prompt AI as a tutor rather than solution provider have been validated at scale through randomized controlled trials (RCTs). To bridge this gap, we conducted a semester-long RCT (N=979) with four ICAP framework-based instructional conditions varying in engagement intensity with a pre-test, immediate and delayed post-test and surveys. Mixed methods analysis results showed: (1) All conditions significantly improved prompting skills, with gains increasing progressively from Condition 1 to Condition 4, validating ICAP's cognitive engagement hierarchy; (2) for students with similar pre-test scores, higher learning gain in immediate post-test predict higher final exam score, though no direct between-group differences emerged; (3) Our interventions are suitable and scalable solutions for diverse educational contexts, resources and learners. Together, this study makes empirical and theoretical contributions: (1) theoretically, we provided one of the first large-scale RCTs examining how cognitive engagement shapes learning in prompting literacy and clarifying the relationship between learning-oriented prompting skills and broader academic performance; (2) empirically, we offered timely design guidance for transforming GenAI classroom policies into scalable, actionable prompting literacy instruction to advance learning in the era of Generative AI.

cs.HC↗

arXiv2025

Multi-agent Self-triage System with Medical Flowcharts

Online health resources and large language models (LLMs) are increasingly used as a first point of contact for medical decision-making, yet their reliability in healthcare remains limited by low accuracy, lack of transparency, and susceptibility to unverified information. We introduce a proof-of-concept conversational self-triage system that guides LLMs with 100 clinically validated flowcharts from the American Medical Association, providing a structured and auditable framework for patient decision support. The system leverages a multi-agent framework consisting of a retrieval agent, a decision agent, and a chat agent to identify the most relevant flowchart, interpret patient responses, and deliver personalized, patient-friendly recommendations, respectively. Performance was evaluated at scale using synthetic datasets of simulated conversations. The system achieved 95.29% top-3 accuracy in flowchart retrieval (N=2,000) and 99.10% accuracy in flowchart navigation across varied conversational styles and conditions (N=37,200). By combining the flexibility of free-text interaction with the rigor of standardized clinical protocols, this approach demonstrates the feasibility of transparent, accurate, and generalizable AI-assisted self-triage, with potential to support informed patient decision-making while improving healthcare resource utilization.

cs.AI↗

arXiv2025

From Code to Concept: Evaluating Multiple Coordinated Views in Introductory Programming

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.

cs.HC↗