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Artificial intelligence-based group decision making to improve knowledge transfer: the case of distance learning in higher education

Author

Listed:
  • Inès Saad

    (ESC Amiens, MIS - Modélisation, Information et Systèmes - UR UPJV 4290 - UPJV - Université de Picardie Jules Verne)

  • Thierno Tounkara

    (LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], IMT-BS - TIM - Département Technologies, Information & Management - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris])

Abstract

In this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers' knowledge and preferences to identify ‘atrisk students', i.e. students who are likely to drop out, and ‘leader students', i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers' preferences, and phase II classifies students into two decision classes: ‘atrisk students' and ‘leader students'. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic.

Suggested Citation

  • Inès Saad & Thierno Tounkara, 2023. "Artificial intelligence-based group decision making to improve knowledge transfer: the case of distance learning in higher education," Post-Print hal-04040348, HAL.
    • Handle: RePEc:hal:journl:hal-04040348
    DOI: 10.1080/12460125.2022.2161734
    as

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