- Title
- KIDNet : a knowledge-aware neural network model for academic performance prediction
- Creator
- Tang, Tao; Hou, Jie; Guo, Teng; Bai, Xiaomei; Tian, Xue; Noori Hoshyar, Azadeh
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189072
- Identifier
- vital:17393
- Identifier
-
https://doi.org/10.1145/3498851.3498927
- Identifier
- ISBN:9781450391870 (ISBN)
- Abstract
- Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student's ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the Knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset. © 2021 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021, Virtual, Online14-17 December 2021, ACM International Conference Proceeding Series p. 37-44
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 ACM
- Subject
- Academic Prediction; Educational Data; Knowledge Graph Embedding; Knowledge Interaction
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