| Autor: |
Oluwasegun Osemudiame Ige, Joseph Bamidele Awotunde, Idowu Dauda Oladipo, Ghaniyyat Bolanle Balogun, Adekola Rasheed Tomori, Muyideen Abdulraheem, Fatimoh Abidemi Taofeek-Ibrahim |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Communications in Computer and Information Science ISBN: 9783030896539 |
| DOI: |
10.1007/978-3-030-89654-6_15 |
| Popis: |
Elective course selection is very important to undergraduate students as the right courses could provide a boost to a student’s Cumulative Grade Point Average (CGPA) while the wrong courses could cause a drop in CGPA. As a result, institutions of higher learning usually have paid advisers and counsellors to guide students in their choice of courses but this method is limited due to factors such as a high number of students and insufficient time on the part of advisers/counsellors. Another factor that limits advisers/counsellors is the fact that no matter how hard we try, there are patterns in data that are simply impossible to detect by human knowledge alone. While many different methods have been used in an attempt to solve the problem of elective course recommendation, these methods generally ignore student performance in previous courses when recommending courses. Therefore, this paper, proposes an effective course recommendation system for undergraduate students using Python programming language, to solve this problem based on grade data from past students. The logistic regression model alongside a wide and deep recommender were used to classify students based on whether a particular course would be good for them or not and to recommend possible electives to them. The data used for this study was gotten from records of the Department of Computer Science, University of Ilorin only and the courses to be predicted were electives in the department. These models proved to be effective with accuracy scores of 0.84 and 0.76 and a mean-squared error of 0.48. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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