Visual Feedback in Python Education: A Controlled 
Experiment on Cognitive Load and Skill Acquisition for 
Non-Computer Science Students

Ke-Lin Li*; Xiaorong Zhang; Nannan Lu; Yu Xiong; Xin Xu

doi:10.70711/neet.v3i12.8243

Visual Feedback in Python Education: A Controlled Experiment on Cognitive Load and Skill Acquisition for Non-Computer Science Students

Ke-Lin Li*, Xiaorong Zhang, Nannan Lu, Yu Xiong, Xin Xu

Abstract

This study evaluates visual feedback pedagogy for teaching Python to non-CS students. A controlled experiment (N=252) compared
Layered Visual Feedback (LVF) with traditional instruction. Results show LVF significantly enhanced higher-order skills (loop visualization
d=0.87, recursion d=0.82, debugging d=0.91) and reduced cognitive load (mental effort ?M=-1.24, interface complexity ?M=-1.68). LVF also
improved skill transfer to text-based tasks by 22.7%. The study proposes a Visual Feedback Threshold Effect model and a Turtle-Cognition
Transfer pathway, supporting integrated visual scaffolding in programming curricula.

Keywords

Python education; Visual feedback; Cognitive load theory; Programming pedagogy; Skill transfer

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DOI: http://dx.doi.org/10.70711/neet.v3i12.8243

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