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Implementing a Recommendation System to Predict Course Selection for Higher Education

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Title: Implementing a Recommendation System to Predict Course Selection for Higher Education
Language: English
Authors: Nehal Adhvaryu (ORCID 0009-0002-4370-3796), Akshara Dave (ORCID 0009-0002-6131-2158)
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
FullText Text:
  Availability: 0
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  Data: Implementing a Recommendation System to Predict Course Selection for Higher Education
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  Data: <searchLink fieldCode="AR" term="%22Nehal+Adhvaryu%22">Nehal Adhvaryu</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-4370-3796">0009-0002-4370-3796</externalLink>)<br /><searchLink fieldCode="AR" term="%22Akshara+Dave%22">Akshara Dave</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-6131-2158">0009-0002-6131-2158</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Digital+Education+Review%22"><i>Digital Education Review</i></searchLink>. 2026 (48):224-237.
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  Data: 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
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  Data: 14
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  Data: 2026
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Selection+%28Students%29%22">Course Selection (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Elective+Courses%22">Elective Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Implementation%22">Program Implementation</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink>
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  Data: 2013-9144
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  Label: Abstract
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  Data: 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.
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 224
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Course Selection (Students)
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Elective Courses
        Type: general
      – SubjectFull: Program Implementation
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: India
        Type: general
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      – TitleFull: Implementing a Recommendation System to Predict Course Selection for Higher Education
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            NameFull: Akshara Dave
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              Y: 2026
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