Loading...
|
Please use this identifier to cite or link to this item:
http://asiair.asia.edu.tw/ir/handle/310904400/115607
|
| Title: | Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence |
| Authors: | 陳興忠;Chen, Hsing-Chung;Prasetyo, Eko;Prasetyo, Eko;曾憲雄;Tseng, Shian-Shyong;Trin, Karisma;Putra, Karisma Trinanda;Prayitno;Prayitno;Sri, Suning K;Kusumawardani, Sri Suning;We, Chien-Erh;Weng, Chien-Erh |
| Contributors: | 資訊電機學院資訊工程學系 |
| Keywords: | imbalanced data distribution;explainable student performance prediction;featureimportance |
| Date: | 2022-02-01 |
| Issue Date: | 2023-03-29 02:53:01 (UTC+0) |
| Publisher: | 亞洲大學 |
| Abstract: | Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students’ activities’ data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students’ activities’ maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models. |
| Appears in Collections: | [經營管理學系 ] 期刊論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| index.html | | 0Kb | HTML | 207 | View/Open |
|
All items in ASIAIR are protected by copyright, with all rights reserved.
|
