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

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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]
Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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PubType: Academic Journal
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  Data: Understanding students' backtracking behaviors in digital textbooks: a data-driven perspective.
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  Data: <searchLink fieldCode="JN" term="%22Interactive+Learning+Environments%22">Interactive Learning Environments</searchLink>. Dec2024, Vol. 32 Issue 10, p6717-6734. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Electronic+textbooks%22">Electronic textbooks</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+styles%22">Cognitive styles</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br /><searchLink fieldCode="DE" term="%22Students%22">Students</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/10494820.2023.2280964
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 6717
    Subjects:
      – SubjectFull: Electronic textbooks
        Type: general
      – SubjectFull: Cognitive styles
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Causal inference
        Type: general
      – SubjectFull: Students
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            – D: 01
              M: 12
              Text: Dec2024
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              Y: 2024
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