User Behavior Data-Driven Interface Optimization Design Research for Computer Science Learning Platforms

Abstract

This work presents a new data-driven methodology to promote the effectiveness of computer science education platform user interfaces through behavioral analysis. Through analysis of the interactive behaviors of a sample of 2,847 students on three of the most popular platforms, we uncovered key behavioral indicators of learning success. The mixed-methods methodology used machine learning algorithms to process click-stream data, navigation patterns, and engagement measurements to identify meaningful correlations between interface design elements and educational measures. The optimization framework proposed by us translated to a 34% increase in task completion and a 27% increase in retention of the learned material. Interfaces that dynamically adjust to students’ behavioral patterns outperformed static interfaces, especially among novice programmers. The results contribute to the theoretical discourse in human-computer interaction and to practical design advice for developers of education technology.