relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2545/
title: Performance Prediction for Students: A Multi-Strategy Approach
creator: Tran, Thi Oanh
creator: Dang, Hai Trieu
creator: Dinh, Viet Thuong
creator: Truong, Thi Minh Ngoc
creator: Vuong, Thi Phuong Thao
creator: Phan, Xuan Hieu
subject: Information Technology (IT)
subject: ISI-indexed journals
description: This paper presents a study on Predicting Student Performance (PSP) in academic systems. In order to solve the task, we have proposed and investigated different strategies. Specifically, we consider this task as a regression problem and a rating prediction problem in recommender systems. To improve the performance of the former, we proposed the use of additional features based on course-related skills. Moreover, to effectively utilize the outputs of these two strategies, we also proposed a combination of the two methods to enhance the prediction performance. We evaluated the proposed methods on a dataset which was built using the mark data of students in information technology at Vietnam National University, Hanoi (VNU). The experimental results have demonstrated that unlike the PSP in e-Learning systems, the regression-based approach should give better performance than the recommender system-based approach. The integration of the proposed features also helps to enhance the performance of the regression-based systems. Overall, the proposed hybrid method achieved the best RMSE score of 1.668. These promising results are expected to provide students early feedbacks about their (predicted) performance on their future courses, and therefore saving times of students and their tutors in determining which courses are appropriate for students’ ability.
date: 2017-06
type: Article
type: PeerReviewed
identifier:   Tran, Thi Oanh and Dang, Hai Trieu and Dinh, Viet Thuong and Truong, Thi Minh Ngoc and Vuong, Thi Phuong Thao and Phan, Xuan Hieu  (2017) Performance Prediction for Students: A Multi-Strategy Approach.  Cybernetics and Information Technologies, 17  (2).   pp. 164-182.  ISSN 1314-4081     
relation: http://doi.org/10.1515/cait-2017-0024
relation: doi:10.1515/cait-2017-0024