Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section
In recent years, distance learning using learning management and e-book systems has been actively conducted in higher education institutions and various other organizations. It is possible to collect and analyze learning logs even in classes with many learners, including clickstreams and quiz scores...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X22000601 |
| _version_ | 1811315758225948672 |
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| author | Konomu Dobashi Curtis P. Ho Catherine P. Fulford Meng-Fen Grace Lin Christina Higa |
| author_facet | Konomu Dobashi Curtis P. Ho Catherine P. Fulford Meng-Fen Grace Lin Christina Higa |
| author_sort | Konomu Dobashi |
| collection | DOAJ |
| description | In recent years, distance learning using learning management and e-book systems has been actively conducted in higher education institutions and various other organizations. It is possible to collect and analyze learning logs even in classes with many learners, including clickstreams and quiz scores in detail for each individual. This research proposes using Moodle's learning logs to classify learning patterns and outliers in order to identify struggling learners. The proposed method uses the descriptive statistics between the learner's teaching material clickstream and the final test score accumulated in Moodle, and students can be classified into four learning patterns. The frequency of each learning pattern was correlated with the appearance of outliers in the final test score and the teaching material clickstream. Most learners moved through four learning patterns during the weekly lessons, however, some learners scoring at the top and bottom of the weekly quiz scores repeated the same learning patterns. There was a tendency to correspond to an outlier due to the repetition of the same learning pattern. The time-series learning analytics of the teaching material clickstream revealed that learners with low final test scores and abnormal values tended to fall under a learning pattern with a smaller teaching material clickstream and a smaller access outside class hours. |
| first_indexed | 2024-04-13T11:37:03Z |
| format | Article |
| id | doaj.art-155f91c8cad64d1082c841ab7244a8f8 |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-04-13T11:37:03Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-155f91c8cad64d1082c841ab7244a8f82022-12-22T02:48:25ZengElsevierComputers and Education: Artificial Intelligence2666-920X2022-01-013100105Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-sectionKonomu Dobashi0Curtis P. Ho1Catherine P. Fulford2Meng-Fen Grace Lin3Christina Higa4Faculty of Modern Chinese Studies, Aichi University, Japan; Corresponding author.Learning Design and Technology, College of Education, University of Hawaiʻi at Mānoa, USALearning Design and Technology, College of Education, University of Hawaiʻi at Mānoa, USALearning Design and Technology, College of Education, University of Hawaiʻi at Mānoa, USASocial Science Research Institute, University of Hawaiʻi at Mānoa, USAIn recent years, distance learning using learning management and e-book systems has been actively conducted in higher education institutions and various other organizations. It is possible to collect and analyze learning logs even in classes with many learners, including clickstreams and quiz scores in detail for each individual. This research proposes using Moodle's learning logs to classify learning patterns and outliers in order to identify struggling learners. The proposed method uses the descriptive statistics between the learner's teaching material clickstream and the final test score accumulated in Moodle, and students can be classified into four learning patterns. The frequency of each learning pattern was correlated with the appearance of outliers in the final test score and the teaching material clickstream. Most learners moved through four learning patterns during the weekly lessons, however, some learners scoring at the top and bottom of the weekly quiz scores repeated the same learning patterns. There was a tendency to correspond to an outlier due to the repetition of the same learning pattern. The time-series learning analytics of the teaching material clickstream revealed that learners with low final test scores and abnormal values tended to fall under a learning pattern with a smaller teaching material clickstream and a smaller access outside class hours.http://www.sciencedirect.com/science/article/pii/S2666920X22000601ClickstreamEngagementLearning analyticsLearning logLearning patternOutlier |
| spellingShingle | Konomu Dobashi Curtis P. Ho Catherine P. Fulford Meng-Fen Grace Lin Christina Higa Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section Computers and Education: Artificial Intelligence Clickstream Engagement Learning analytics Learning log Learning pattern Outlier |
| title | Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section |
| title_full | Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section |
| title_fullStr | Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section |
| title_full_unstemmed | Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section |
| title_short | Learning pattern classification using moodle logs and the visualization of browsing processes by time-series cross-section |
| title_sort | learning pattern classification using moodle logs and the visualization of browsing processes by time series cross section |
| topic | Clickstream Engagement Learning analytics Learning log Learning pattern Outlier |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X22000601 |
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