E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior

Feiyue Qiu; Lijia Zhu; Guodao Zhang; Xin Sheng; Mingtao Ye; Qifeng Xiang; Ping-Kuo Chen

doi:10.3390/e24050722

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior

Entropy (Basel). 2022 May 19;24(5):722. doi: 10.3390/e24050722.

Authors

Feiyue Qiu¹, Lijia Zhu¹, Guodao Zhang^{2

3}, Xin Sheng¹, Mingtao Ye³, Qifeng Xiang¹, Ping-Kuo Chen⁴

Affiliations

¹ College of Education, Zhejiang University of Technology, Hangzhou 310023, China.
² Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
³ College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
⁴ Business School, Shantou University, Shantou 515000, China.

Abstract

Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners' academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.

Keywords: E-learning behavior classification; E-learning performance; feature fusion; feature space; machine learning.

Grants and funding

This study was supported by grant from National Natural Science Foundation of China No. 71872131. In addition, is also supported by grant from Shantou University SRIG No. 2007 and Science and technology project of Zhejiang Province No. 2018C01080.