Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs
Abstract The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors....
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-05-01
|
| Series: | International Journal of Educational Technology in Higher Education |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41239-023-00400-x |
| _version_ | 1827905334495150080 |
|---|---|
| author | Xiaona Xia Wanxue Qi |
| author_facet | Xiaona Xia Wanxue Qi |
| author_sort | Xiaona Xia |
| collection | DOAJ |
| description | Abstract The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs. |
| first_indexed | 2024-03-13T00:41:10Z |
| format | Article |
| id | doaj.art-ef2ae7378ac34bb3961bf31969cf334d |
| institution | Directory Open Access Journal |
| issn | 2365-9440 |
| language | English |
| last_indexed | 2024-03-13T00:41:10Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Educational Technology in Higher Education |
| spelling | doaj.art-ef2ae7378ac34bb3961bf31969cf334d2023-07-09T11:21:09ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402023-05-0120112510.1186/s41239-023-00400-xDropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCsXiaona Xia0Wanxue Qi1Faculty of Education, Qufu Normal UniversityFaculty of Education, Qufu Normal UniversityAbstract The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs.https://doi.org/10.1186/s41239-023-00400-xMOOCsLearning behaviorMulti temporal sequenceDropout trendDropout prediction modelLearning analytics |
| spellingShingle | Xiaona Xia Wanxue Qi Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs International Journal of Educational Technology in Higher Education MOOCs Learning behavior Multi temporal sequence Dropout trend Dropout prediction model Learning analytics |
| title | Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs |
| title_full | Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs |
| title_fullStr | Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs |
| title_full_unstemmed | Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs |
| title_short | Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs |
| title_sort | dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in moocs |
| topic | MOOCs Learning behavior Multi temporal sequence Dropout trend Dropout prediction model Learning analytics |
| url | https://doi.org/10.1186/s41239-023-00400-x |
| work_keys_str_mv | AT xiaonaxia dropoutpredictionanddecisionfeedbacksupportedbymultitemporalsequencesoflearningbehaviorinmoocs AT wanxueqi dropoutpredictionanddecisionfeedbacksupportedbymultitemporalsequencesoflearningbehaviorinmoocs |