Exploring the Impact of Multiple Representations in Introductory Programming
A Pilot Study
Naaz Sibia (University of Toronto)
Valeria Ramirez Osorio (University of Toronto)
Angela Zavaleta Bernuy (University of Toronto)
Efthimia Aivaloglou (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Rutwa Engineer (University of Toronto)
Andrew Petersen (University of Toronto)
Michael Liut (TU Delft - Electrical Engineering, Mathematics and Computer Science, University of Toronto)
Carolina Nobre (University of Toronto)
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Abstract
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.
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