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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2403.13822 |
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| _version_ | 1866910377073180672 |
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| author | Zhang, Xinyu Lee, Vincent CS Xu, Duo Chen, Jun Obaidat, Mohammad S. |
| author_facet | Zhang, Xinyu Lee, Vincent CS Xu, Duo Chen, Jun Obaidat, Mohammad S. |
| contents | A learning management system streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an e-learning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Therefore, an LMS incorporated with an advanced educational mining module is proposed in this study, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowledge reveals a set of attributes that significantly impact student academic performance, and further classification evaluation validates the identified attributes. The design and application of such an effective LMS can enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13822 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Effective Learning Management System for Revealing Student Performance Attributes Zhang, Xinyu Lee, Vincent CS Xu, Duo Chen, Jun Obaidat, Mohammad S. Computers and Society A learning management system streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an e-learning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Therefore, an LMS incorporated with an advanced educational mining module is proposed in this study, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowledge reveals a set of attributes that significantly impact student academic performance, and further classification evaluation validates the identified attributes. The design and application of such an effective LMS can enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. |
| title | An Effective Learning Management System for Revealing Student Performance Attributes |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2403.13822 |