Predicting ICT Students’ Profile Using AI and Social Network for a Post Pandemic Classroom

Authors

  • Cherifa Boudia Exact Sciences Faculty, University Mustapha Stambouli of Mascara, Algeria.
  • Asmaa Bengueddach LIO Laboratory, University Oran 1 Ahmed Ben Bella, Oran, Algerie

DOI:

https://doi.org/10.15379/ijmst.v10i3.3307

Keywords:

ICT Skills, Covid’19, University Curricula, Education Strategies, Questionnaire, Data Mining, Social Network, Post-Crisis.

Abstract

The study investigates the impact of the COVID-19 crisis on education, focusing on computer science graduates and the importance of Information and Communication Technology (ICT) skills aligned with job expectations. Employing a questionnaire-based approach and data mining algorithms, the research assesses students' self-reported ICT skills post-pandemic. Findings indicate students generally possess fundamental ICT skills but exhibit areas for improvement. The study emphasizes addressing gaps in graduates' profiles to enhance skillsets and adapt university curricula. Practical implications highlight the need to prepare students for the evolving workforce, and the research contributes to literature by enriching understanding of post-COVID-19 education. The approach, combining questionnaires and data mining, offers valuable insights for assessing and predicting students' skills in a post-pandemic context. This research serves as a foundation for adapting teaching strategies and curricula to meet evolving learner requirements.

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Published

2023-08-18

How to Cite

[1]
C. . Boudia and A. . Bengueddach, “Predicting ICT Students’ Profile Using AI and Social Network for a Post Pandemic Classroom”, ijmst, vol. 10, no. 3, pp. 3339-3353, Aug. 2023.