Knowledge-Based Recommendation for Subject Allocation Using Artificial Neural Network in Higher Education

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  • Author(s): Saxena, Nitin Kumar (ORCID Saxena, Nitin Kumar (ORCID 0000-0002-6520-1749); Chauhan, Bhavesh Kumar (ORCID Chauhan, Bhavesh Kumar (ORCID 0000-0003-4137-6234); Gouri, Sonia (ORCID Gouri, Sonia (ORCID 0000-0001-8916-8763); Kumar, Ashwani (ORCID Kumar, Ashwani (ORCID 0000-0002-3751-0800); Gupta, Anmol (ORCID Gupta, Anmol (ORCID 0000-0002-4967-5821)
  • Language:
    English
  • Source:
    IEEE Transactions on Education. 2023 66(5):500-508.
  • Publication Date:
    2023
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=13
    • Peer Reviewed:
      Y
    • Source:
      9
    • Education Level:
      Higher Education
      Postsecondary Education
    • Subject Terms:
      Knowledge Management; Artificial Intelligence; Higher Education; Information Technology; Information Retrieval; Outcome Based Education; Engineering Education; College Faculty; Course Objectives; Teaching Methods; Algorithms
    • Accession Number:
      10.1109/TE.2023.3296315
    • ISSN:
      0018-9359
      1557-9638
    • Abstract:
      Contribution: The proposed work carries out the training and testing of the available data through an artificial neural network and develops a model to allocate the subject for maximum outcome. The system also provides percentagewise correlation among all the possible subjects of best fit to allocate among the faculty members. Background: Data mining and machine learning tools have amazed all professionals with their fast, accurate, precise, and feasible results. While their results cannot be directly superimposed on all education systems, they certainly provide ideas for improving teaching pedagogy based on the requirements and capabilities of the system. Intended Outcomes: The subject allocation among the faculty members in engineering studies plays a crucial role in teaching and training the students in the best possible way from the point of view of outcome-based education. The objective of this article is to present an effective model for subject allocation to faculty members based on various factors. Application Design: Faculty members have their diversified strengths because of their involvement in different institute activities. An appropriate subject allocation mechanism for any faculty accumulating the knowledge of an individual's responsibilities and area of interest can support more significantly in achieving the course outcomes. Findings: 1) Subject allocation based on individuals' involvement in academics, administrative, and research domains; 2) Subject allocation based on qualifications and experiences for engendering the outcome; and 3) A user-friendly model development for applying at an individual, department, or even at the institute level.
    • Abstract:
      As Provided
    • Publication Date:
      2023
    • Accession Number:
      EJ1395335