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

2026-06-17

AI Summary

This study investigates why novice learners often overlook well-designed visual scaffolds in multi-view programming visualization tools. Employing eye-tracking, think-aloud protocols, and reflective interviews alongside Python Tutor and multi-representational probe instruments, the research examines cognitive and affective engagement among undergraduate students as they simultaneously interact with code, memory, and metaphorical views. Findings reveal three key factors shaping selective engagement: agency, representational congruence, and disciplinary legitimacy, underscoring the critical role of affective and social dimensions in tool adoption. Results indicate that nearly half of participants’ time was spent exclusively on code, with less-experienced learners engaging even less with metaphorical representations. The study advocates positioning visualizations primarily as validation aids, supporting switchable levels of abstraction, and enhancing their academic legitimacy to foster effective use.

Abstract

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.

Problem

• program visualization
• multiple external representations
• learner engagement
• novice programmers
• cognitive scaffolding

Innovation

• multi-representational visualization
• learner agency
• representational fit
• disciplinary legitimacy
• gaze tracking