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Truong Duc Phuong, Do Van Thanh and Nguyen Duc Dung

Abstract

The main objective of this paper is to introduce fuzzy sequential patterns with fuzzy time-intervals in quantitative sequence databases. In the fuzzy sequential pattern with fuzzy time-intervals, both quantitative attributes and time distances are represented by linguistic terms. A new algorithm based on the Apriori algorithm is proposed to find the patterns. The mined patterns can be applied to market basket analysis, stock market analysis, and so on.

Open access

Thi-Oanh Tran, Hai-Trieu Dang, Viet-Thuong Dinh, Thi-Minh-Ngoc Truong, Thi-Phuong-Thao Vuong and Xuan-Hieu Phan

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.