Exploring students' backtracking behaviors in digital textbooks and its relationship to learning styles

TL;DR

This study investigates students' backtracking behaviors in digital textbooks, revealing their relationship with learning styles and academic performance, and proposes methods for personalized support and automatic learning style detection.

Contribution

It introduces a detailed analysis of backtracking patterns, links them to learning styles and performance, and suggests automated tools for enhancing digital textbook learning experiences.

Findings

Backtrackers perform better academically.

Higher initial ability leads to more backtracking.

Most backtrackers are reflective and visual learners.

Abstract

The purpose of this study is to explore students' backtracking patterns in using a digital textbook and reveal the relationship between backtracking behaviors and academic performance as well as learning styles. The study was carried out for two semesters on 102 university students and they are required to use a digital textbook system called DITeL to review courseware. Students' backtracking behaviors are characterized by seven backtracking features extracted from interaction log data and their learning styles are measured by Felder-Silverman learning style model. The results of the study reveal that there is a subgroup of students called backtrackers who backtrack more frequently and performed better than the average students. Furthermore, the causal inference analysis reveals that a higher initial ability can directly cause a higher frequency of backtracking, thus affecting the final test score. In addition, most backtrackers are reflective and visual learners, and the seven backtracking features are good predictors in automatically identifying learning styles. Based on the results of qualitative data analysis, recommendations were made on how to provide prompt backtracking assistants and automatically detect learning styles in digital textbooks.