This study aims to investigate the relationship between students' assignment submission behavior and final grades in information literacy education using a large volume of learning logs stored on the LMS. A total of 12,516 freshman students participated in this study from the year 2018 to 2022, across the COVID-19 pandemic. The students were divided into high, medium, and low performance groups using k-means clustering. The results of the characteristics analysis show a significant early submission behavioral trend and a late submission behavioral trend in high and low performance groups, respectively. The on-demand class format during the COVID-19 pandemic resulted in more consistent early submission behavior for high performance students and late submission behavior for low performance students, respectively. The findings suggest that time management skill is a critical factor in both blended and online learning environments, affecting weekly submission behavior and final grades.

As a kind of data that can reflect learning status, e-book logs have been widely used in learning analytics, especially for the prediction of academic performance. However, the best prediction model cannot be found without determining the contribution of e-book logs to the prediction performance of the model and its creation process. To this end, this study used the scikit-learn, a free software machine learning library, to analyze learning performance of 234 participants by learning behavior logs, which were collected by an e-book system. Finally, six prediction models containing Decision Tree, Random Forests, XGBoost, Logistic Regression, Support Vector Machines, and K-nearest Neighbors were created. Also, the contribution of e-book logs on the establishment of different prediction models was obtained by three feature importance calculation methods, i.e., the impurity-based feature importance, coefficients feature importance, and permutation feature importance. Based on statistical results, it was concluded that the Decision Tree and Random Forests had the best prediction performance, which was compared to the other four models, with prediction performance scores ranging from 0.7 to 0.8. Besides, the four data features of Prev, Highlight, Maker, and Next were found to have the greatest impact on model prediction creation.

We currently utilize the Moodle learning management system for teachers and students who participate in the course ‘College of Liberal Arts and Sciences’ at Kobe University in Japan. Digital textbooks, reports, quizzes and questionnaires in this course were administered using Moodle. In this paper, we proposed to use quizzes to measure and evaluate those digital textbooks recorded on Moodle. At the beginning of our study, we examined the questions that students got lower scores, and then we found the related teaching materials of digital textbooks and feedback to the teachers in order to improve the content of these digital textbooks.