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ISSN:
2377-2891(Print); 2377-2905(Online)
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Editor-in-Chief:
Prof. Eric C. K. Cheng
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10.18178/ijlt
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Editor-in-Chief
Prof. Eric C. K. Cheng
Professor & Vice President (Academic)
Yew Chung College of Early Childhood Education, Hong Kong, China
As the Editor-in-Chief of IJLT, I invite you to contribute your scholarly work to our esteemed publication. IJLT serves as a beacon for original and impactful academic contributions in the realm of education, fostering multidisciplinary research and development to enhance teaching-learning processes globally. We welcome submissions spanning a wide spectrum of topics, from innovative program development to the integration of digital tools in education. Our scope encompasses areas such as student leadership, diversity in education, and collaborative initiatives, reflecting our commitment to a sustainable and inclusive society. [
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Volume 5, No. 3, September 2019
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A Personalized Course Recommender System for Undergraduate Students
Thi-Hai-Yen Vuong
1
, Thi-Thu Trinh
2
, Hong-Duyen Ha
2
, Xuan-Hieu Phan
2
, and Thi-Oanh Tran
3
1. School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan
2. University of Engineering and Technology, Vietnam National University Hanoi, Vietnam
3. International School, Vietnam National University Hanoi, Vietnam
Abstract
—In this paper, we describe a web-based course recommender system which has a variety of functions for supporting students in academic fields. The system’s goal is to help students manage their own study progress in a more effective way. With this system, students would achieve better performance, find it easy to select courses without wondering which will give them high score or which are better for their future job or career path. The core functionalities of the system are partly based on recommendation methods. The first task is the prediction of student performance to help them estimate their performances on selected courses. These predicted results help students choose courses that are appropriate for them to get higher scores. Second, the system will automatically build a study strategy for next two years or even entirely four academic years based on their ability and related information. Our system also provides students a general view of their learning status: profile, grades, taken courses, credits, important school news, time table, etc. By using these functionalities, students will have a guidance and an up-to-date studying path to follow, as well as efficiently complete their school work.
Index Terms
—student performance prediction, recommender system, study strategy
Cite: Thi-Hai-Yen Vuong, Thi-Thu Trinh, Hong-Duyen Ha, Xuan-Hieu Phan, and Thi-Oanh Tran, "A Personalized Course Recommender System for Undergraduate Students," International Journal of Learning and Teaching, Vol. 5, No. 3, pp. 181-190, September 2019. doi: 10.18178/ijlt.5.3.181-190
1-LT017
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