A Recommendation Method to Adapt Curriculum and Improve Learners Performance
A Recommendation Method to Adapt Curriculum and Improve Learners Performance

Summary/Abstract: Higher education curricula have evolved to incorporate technologies, but adaptive models continue to yield limited results. This study proposes a recommendation method for an adaptive curriculum that improves student performance. The research followed a mixed-method approach to programming courses at the Salesian Polytechnic University. The average population is 320 students and between 7 and 10 tutors each academic period. Survey techniques were applied to teachers and students between October 2024 and March 2025. The results show that 81% of students on average significantly improved their performance over seven academic cycles. Similarly, four of the seven teachers (57%) expressed a favorable perception of the model's implementation. Despite these results, the sample size and KMO value suggest caution. A control group was omitted to ensure fairness in learning, which limits generalizability. However, the results suggest that the model is transferable to other courses, potentially incorporating artificial intelligence to enhance its socio-professional relevance.

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