Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University

Khloud Alshaikh, Naela Bahurmuz, Ola Torabah, Sara Alzahrani, Zainab Alshingiti, Maram Meccawy


In Saudi Arabia, all high school graduates who want join local universities have to go through a preparatory year before selecting their specific specialization/major. One of the most concerning issues for those fresh undergraduate college students is the selection of their specialization. College specialization selection is critical for them, as their academic and career future will be affected by this decision. An un-suitable specialization selection will have unfortunate consequences, not only on the students' future but also on the university’s resources and budget. This paper sug-gests a solution to this problem by introducing a preliminary study of a recommend-er system (RS), which will recommend the appropriate specialization for the students based on various tests and grades during the preparatory year at King Abdulaziz University (KAU). The proposed system guides students through their specialization selection process based on their abilities. The collaborative filtering technique was used to build the RS and K-fold cross-validation was adopted to evaluate its accura-cy and performance. The results showed the prediction of a specialization for each student with good accuracy ratio. These promising initial results provide a feasible solution to assess this issue further in future studies.


Artificial Intelligence, Education, Machine learning, Recommender Systems.

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Copyright (c) 2021 Khloud Alshaikh, Naela Bahurmuz, Ola Torabah, Sara Alzahrani, Zainab Alshingiti, Maram Meccawy

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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