Development Of Undergraduate Students Course Recommender System

Abdulrazak Yahya, Saleh and Mark Cornalius, Julian (2023) Development Of Undergraduate Students Course Recommender System. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA), 10-11 October 2023, Taiz, Yemen.

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Abstract

Recommender systems can be utilized to their full potential by suggesting to users the information they need, tailored to their interests and personality. However, researchers in the past have faced a common challenge when developing a recommender system: the "Cold-Start" problem. This issue arises when the system cannot suggest any information to the user due to a lack of data from that user. Individual interests and personalities are strongly influenced by the choices made regarding future careers, including course selection prior to entering university. To address the "Cold-Start" problem, a Course Recommender system has been proposed. The collection of data from users before developing this system is vital to ensure its efficient and effective function. The Agile methodology has been employed in the development of the Course Recommender system, encompassing the discovery of requirements, design to fulfill those requirements, prototyping, and evaluation. Holland's RIASEC personality test is utilized to gather users' interests and personality traits, which then serve as a guide to determine the courses that suit the users best. In a sample of 15 respondents chosen from 105, nine claimed that they were matched with a course of interest, while six did not feel the same way. Hence, the incorporation of Holland's RIASEC personality traits has indeed proven beneficial in selecting courses that best suit the users, thereby enhancing the effectiveness and efficiency of the developed Course Recommender system.

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