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Understanding students' backtracking behaviors in digital textbooks: a data-driven perspective.

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Bibliographic Details
Title: Understanding students' backtracking behaviors in digital textbooks: a data-driven perspective.
Authors: Jiang, Bo1,2 (AUTHOR) bjiang@deit.ecnu.edu.cn, Wei, Yuang2 (AUTHOR), Gu, Meijun3 (AUTHOR), Yin, Chengjiu4 (AUTHOR)
Source: Interactive Learning Environments. Dec2024, Vol. 32 Issue 10, p6717-6734. 18p.
Subjects: Electronic textbooks, Cognitive styles, Machine learning, Causal inference, Students
Abstract: The purpose of this study is to explore students' backtracking patterns in using a digital textbook, reveal the relationship between backtracking behaviors and academic performance as well as learning styles. This study was carried out for 2 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 this study reveal that there is a subgroup of students called backtracker 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, the significance analysis reveals no significant correlation between backtracking behavior and learning style. Building upon these experimental findings, we offer several suggestions for the future advancement of digital teaching materials development. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The purpose of this study is to explore students' backtracking patterns in using a digital textbook, reveal the relationship between backtracking behaviors and academic performance as well as learning styles. This study was carried out for 2 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 this study reveal that there is a subgroup of students called backtracker 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, the significance analysis reveals no significant correlation between backtracking behavior and learning style. Building upon these experimental findings, we offer several suggestions for the future advancement of digital teaching materials development. [ABSTRACT FROM AUTHOR]
ISSN:10494820
DOI:10.1080/10494820.2023.2280964