Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction
| Autor: | Shimin Hu, Qichao Wang, Simon Fong, Jie Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
attribute selection
educational data mining Process (engineering) Computer science Science QC1-999 education General Physics and Astronomy Feature selection Machine learning computer.software_genre Astrophysics Educational data mining Article Feature (machine learning) Redundancy (engineering) Performance prediction ComputingMilieux_COMPUTERSANDEDUCATION Curriculum student performance prediction business.industry Physics academic early warning system QB460-466 Early warning system Artificial intelligence multi-label learning business computer |
| Zdroj: | Entropy, Vol 23, Iss 1252, p 1252 (2021) Entropy Volume 23 Issue 10 |
| ISSN: | 1099-4300 |
| Popis: | The university curriculum is a systematic and organic study complex with some immediate associated steps the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics. |
| Databáze: | OpenAIRE |
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