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CoDeRS: a computing degree program recommender system using machine learning algorithms.
Christian Alexandra Rances1, Rassel Aviel Sipe1, Sherwin Baris1
1College of Computer Studies and Systems, University of the East, Manila, Philippines.
Frontiers in Artificial Intelligence
|July 16, 2026
Summary
A new web-based recommender system, CoDeRS, helps students find suitable computing degrees by analyzing interests and personality. The system demonstrated high usability and recommendation acceptance, proving effective for career guidance.
Area of Science:
- Educational Technology
- Computer Science
- Human-Computer Interaction
Background:
- Students often struggle to select appropriate computing degree programs.
- Existing guidance systems may not fully integrate personal and academic factors.
Purpose of the Study:
- To develop and evaluate CoDeRS, a web-based recommender system for computing degree selection.
- To assess the system's usability, reliability, user experience, and overall satisfaction among students.
Main Methods:
- A six-stage development process incorporating user profiles, academic data, and personality types (Big Five Inventory).
- Multinomial Naïve Bayes for user profiles and content-based filtering for academic data.
- Cosine similarity for personality matching, with a recommendation engine trained on 352 computing students.
Main Results:
- High usability ratings: 85.7% found it easy to navigate, 81% easy to use.
- Recommendation acceptance rate of 76.2%, with over 50% intending future use.
- Performance metrics: 81.6% accuracy, 84.2% recall, and 77.9% precision.
Conclusions:
- CoDeRS is an effective technology for aiding students in making informed computing degree choices.
- The system successfully integrates diverse data points to provide tailored recommendations.
- Further development and application of CoDeRS are warranted for educational guidance.
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