论文标题
探索学生在数字教科书中的回溯行为及其与学习风格的关系
Exploring students' backtracking behaviors in digital textbooks and its relationship to learning styles
论文作者
论文摘要
这项研究的目的是在使用数字教科书中探索学生的回溯模式，并揭示回溯行为与学习表现以及学习风格之间的关系。这项研究是在102名大学生的两个学期进行的，他们必须使用名为Ditel的数字教科书系统来审查课件。学生的回溯行为的特征是从互动日志数据中提取的七个回溯功能，他们的学习方式由Felder-Silverman学习样式模型衡量。该研究的结果表明，有一个名为BackTrackers的学生子组，他们的回调频率更高，并且表现优于普通学生。此外，因果推断分析表明，较高的初始能力可以直接导致更高的回溯频率，从而影响最终的测试评分。此外，大多数回溯器都是反射性和视觉学习者，而七个回溯功能是自动识别学习风格的良好预测指标。根据定性数据分析的结果，提出了有关如何提供及时回溯助手并自动检测数字教科书中的学习风格的建议。
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.
