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The Indian National Education Policy (NEP) 2020 emphasizes a multidisciplinary and flexible education system, promoting vocational training to enhance employability and skill development among undergraduate students. This research proposes an intelligent recommendation system for vocational course selection using Association Rule Mining (ARM), a machine learning technique that discovers hidden patterns in student data. The study aims to identify relationships between students’ academic backgrounds, interests, and career aspirations to suggest suitable vocational courses. The proposed system preprocesses student datasets, applies frequent pattern mining techniques, and generates association rules based on student preferences and prior course enrollments. Key metrics such as support, confidence, and lift are utilized to evaluate the strength and reliability of the generated rules. By leveraging Apriori algorithm, the system ensures efficient rule generation for personalized course recommendations. This research supports evidence-based curriculum design, guiding educational institutions in offering tailored vocational training programs, thus fostering a skilled workforce aligned with India’s evolving job market.

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Recommendation of Vocation Courses Under Indian National Education Policy 2020 to Undergraduate Students Using Association Rule

  • 会议论文

  • 发表日期：

    2026年

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  • Iram Naim,
  • Sarabjeet Singh Bedi,
  • Pankaj,
  • Ashraf Rahman Idrisi,
  • Anil Kumar Bisht

摘要

The Indian National Education Policy (NEP) 2020 emphasizes a multidisciplinary and flexible education system, promoting vocational training to enhance employability and skill development among undergraduate students. This research proposes an intelligent recommendation system for vocational course selection using Association Rule Mining (ARM), a machine learning technique that discovers hidden patterns in student data. The study aims to identify relationships between students’ academic backgrounds, interests, and career aspirations to suggest suitable vocational courses. The proposed system preprocesses student datasets, applies frequent pattern mining techniques, and generates association rules based on student preferences and prior course enrollments. Key metrics such as support, confidence, and lift are utilized to evaluate the strength and reliability of the generated rules. By leveraging Apriori algorithm, the system ensures efficient rule generation for personalized course recommendations. This research supports evidence-based curriculum design, guiding educational institutions in offering tailored vocational training programs, thus fostering a skilled workforce aligned with India’s evolving job market.