Next Article in Journal
The Fractional View of Complexity
Next Article in Special Issue
Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids
Previous Article in Journal
A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE
Previous Article in Special Issue
Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme
Open AccessArticle

Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning

by Shaojie Qu 1, Kan Li 2,*, Bo Wu 1, Xuri Zhang 2 and Kaihao Zhu 2
1
Network Information Technology Center, Beijing Institute of Technology, Beijing 100081, China
2
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1216; https://doi.org/10.3390/e21121216
Received: 25 October 2019 / Revised: 8 December 2019 / Accepted: 10 December 2019 / Published: 12 December 2019
(This article belongs to the Special Issue Theory and Applications of Information Theoretic Machine Learning)
Massive open online courses (MOOCs), which have been deemed a revolutionary teaching mode, are increasingly being used in higher education. However, there remain deficiencies in understanding the relationship between online behavior of students and their performance, and in verifying how well a student comprehends learning material. Therefore, we propose a method for predicting student performance and mastery of knowledge points in MOOCs based on assignment-related online behavior; this allows for those providing academic support to intervene and improve learning outcomes of students facing difficulties. The proposed method was developed while using data from 1528 participants in a C Programming course, from which we extracted assignment-related features. We first applied a multi-task multi-layer long short-term memory-based student performance predicting method with cross-entropy as the loss function to predict students’ overall performance and mastery of each knowledge point. Our method incorporates the attention mechanism, which might better reflect students’ learning behavior and performance. Our method achieves an accuracy of 92.52% for predicting students’ performance and a recall rate of 94.68%. Students’ actions, such as submission times and plagiarism, were related to their performance in the MOOC, and the results demonstrate that our method predicts the overall performance and knowledge points that students cannot master well. View Full-Text
Keywords: multi-task; multi-layer LSTM; attention mechanism; MOOCs; educational data mining multi-task; multi-layer LSTM; attention mechanism; MOOCs; educational data mining
Show Figures

Figure 1

MDPI and ACS Style

Qu, S.; Li, K.; Wu, B.; Zhang, X.; Zhu, K. Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning. Entropy 2019, 21, 1216.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop