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KIDNet: a Knowledge-Aware neural network model for academic performance prediction

Citation

Tang, T and Hou, J and Guo, T and Bai, X and Tian, X and Hoshyar, AN, KIDNet: a Knowledge-Aware neural network model for academic performance prediction, SIGAI: ACM Special Interest Group on Artificial Intelligence, 14-17 December 2021, Melbourne, VIC, Australia, pp. 37-44. ISBN 9781450391870 (2021) [Refereed Conference Paper]

Copyright Statement

Copyright 2021 The Authors

DOI: doi:10.1145/3498851.3498927

Abstract

Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the studentís 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.

Item Details

Item Type:Refereed Conference Paper
Keywords:Educational data, academic prediction, knowledge graph embedding, knowledge Interaction
Research Division:Education
Research Group:Specialist studies in education
Research Field:Learning analytics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Tian, X (Ms Xue Tian)
ID Code:152016
Year Published:2021
Deposited By:Information and Communication Technology
Deposited On:2022-08-10
Last Modified:2022-09-05
Downloads:0

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