Version 1
: Received: 26 August 2024 / Approved: 27 August 2024 / Online: 27 August 2024 (12:41:12 CEST)
How to cite:
Mehrabi, A.; Morphew, J. W.; Araabi, B. N.; Memarian, N.; Memarian, H. AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education During the Post-COVID Era. Preprints2024, 2024081952. https://doi.org/10.20944/preprints202408.1952.v1
Mehrabi, A.; Morphew, J. W.; Araabi, B. N.; Memarian, N.; Memarian, H. AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education During the Post-COVID Era. Preprints 2024, 2024081952. https://doi.org/10.20944/preprints202408.1952.v1
Mehrabi, A.; Morphew, J. W.; Araabi, B. N.; Memarian, N.; Memarian, H. AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education During the Post-COVID Era. Preprints2024, 2024081952. https://doi.org/10.20944/preprints202408.1952.v1
APA Style
Mehrabi, A., Morphew, J. W., Araabi, B. N., Memarian, N., & Memarian, H. (2024). AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education During the Post-COVID Era. Preprints. https://doi.org/10.20944/preprints202408.1952.v1
Chicago/Turabian Style
Mehrabi, A., Negar Memarian and Hossein Memarian. 2024 "AI-Enhanced Decision-Making for Course Modality Preferences in Higher Engineering Education During the Post-COVID Era" Preprints. https://doi.org/10.20944/preprints202408.1952.v1
Abstract
This study harnesses the potential of artificial intelligence (AI) focusing on machine learning (ML) to improve decision-making in advanced engineering education. The onset of the COVID-19 pandemic compelled a swift transformation in higher education methodologies, particularly in the domain of course modality. This study aims to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches, particularly learning styles, theoretical, practical, and theoretical-practical, prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and E-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees (DT), support vector machines (SVM), and random forest (RF)—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.
Keywords
online learning; technology-enhanced learning; educational machine learning; educational artificial intelligence; feature selection methods; random forest; SVM; decision tree
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
