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Performance Prediction for Students: A Multi-Strategy Approach

Thi Oanh Tran and Hai Trieu Dang and Viet Thuong Dinh and Thi Minh Ngoc Truong and Thi Phuong Thao Vuong and Xuan Hieu Phan (2017) Performance Prediction for Students: A Multi-Strategy Approach. Cybernetics and Information Technologies, 17 (2). pp. 164-182. ISSN 1314-4081

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Official URL: http://doi.org/10.1515/cait-2017-0024

Abstract

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.

Item Type:Article
Subjects:Information Technology (IT)
ISI/Scopus-indexed journals
Divisions:Faculty of Information Technology (FIT)
ID Code:2545
Deposited By: Prof. Xuan Hieu Phan
Deposited On:02 Jul 2017 14:43
Last Modified:02 Jul 2017 14:43

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