Performance Prediction for Students: A Multi-Strategy Approach
| Autor: | Thi-Phuong-Thao Vuong, Hai-Trieu Dang, Viet-Thuong Dinh, Thi-Oanh Tran, Xuan-Hieu Phan, Thi-Minh-Ngoc Truong |
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| Rok vydání: | 2017 |
| Předmět: |
General Computer Science
business.industry Computer science 05 social sciences 050301 education Information technology 02 engineering and technology Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering Performance prediction 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer |
| Zdroj: | Cybernetics and Information Technologies. 17:164-182 |
| ISSN: | 1314-4081 |
| DOI: | 10.1515/cait-2017-0024 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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