Implementing a Recommendation System to Predict Course Selection for Higher Education
Saved in:
| Title: | Implementing a Recommendation System to Predict Course Selection for Higher Education |
|---|---|
| Language: | English |
| Authors: | Nehal Adhvaryu (ORCID |
| Source: | Digital Education Review. 2026 (48):224-237. |
| Availability: | Universitat de Barcelona. Passeig de la Vall d'Hebron 171, Edifici Llevant P3, Barcelona, 08035 Spain. e-mail: der@greav.net; Web site: http://revistes.ub.edu/index.php/der |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Higher Education, Course Selection (Students), Decision Making, Elective Courses, Program Implementation, Automation, Prediction, Artificial Intelligence, Technology Uses in Education |
| Geographic Terms: | India |
| ISSN: | 2013-9144 |
| Abstract: | In order to encourage the students to follow an inter-disciplinary education system where they can study multiple academic disciplines, the New Education Policy offers them a variety of elective courses. Greater flexibility promotes all-around development and can cater to a diverse set of interests, but it poses a serious issue as well: students do not know how to choose electives that align with their academic abilities, career aspirations, and personal interests. A content-based filtering and collaborative filtering technique-based recommendation system is recommended in this research to solve this issue and assist students in making informed elective choices. Personalized course suggestions are provided by the strategy on the basis of data like students' past performances, specific areas of interest, and courses available. All this data is examined by the algorithm and recommends electives that best fit the student's goals and ability level. It seeks to simplify the voluntary decision-making process, reduce decision anxiety, and improve academic performance and engagement. The efficacy of the concept is determined by a sequence of pre- and post-tests of educational achievements and student satisfaction after the implementation of the recommendation system. Moreover, the study provides an in-situ and scalable solution to one of the biggest problems of modern multidisciplinary education. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1500383 |
| Database: | ERIC |
| Abstract: | In order to encourage the students to follow an inter-disciplinary education system where they can study multiple academic disciplines, the New Education Policy offers them a variety of elective courses. Greater flexibility promotes all-around development and can cater to a diverse set of interests, but it poses a serious issue as well: students do not know how to choose electives that align with their academic abilities, career aspirations, and personal interests. A content-based filtering and collaborative filtering technique-based recommendation system is recommended in this research to solve this issue and assist students in making informed elective choices. Personalized course suggestions are provided by the strategy on the basis of data like students' past performances, specific areas of interest, and courses available. All this data is examined by the algorithm and recommends electives that best fit the student's goals and ability level. It seeks to simplify the voluntary decision-making process, reduce decision anxiety, and improve academic performance and engagement. The efficacy of the concept is determined by a sequence of pre- and post-tests of educational achievements and student satisfaction after the implementation of the recommendation system. Moreover, the study provides an in-situ and scalable solution to one of the biggest problems of modern multidisciplinary education. |
|---|---|
| ISSN: | 2013-9144 |
