Fiche du document
2022
- handle: 10670/1.a5gutz
- hal: hal-03652270
- doi: 10.3390/su14095205
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.3390/su14095205
info:eu-repo/semantics/OpenAccess
Mots-clés
COVID-19 data mining engineering education generalized linear auto-regression (GLAR) grade prediction hybrid learning -ph]/Mechanical engineering [physics.class-ph]Citer ce document
Zoe Kanetaki et al., « Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education », HAL-SHS : sciences de l'éducation, ID : 10.3390/su14095205
Métriques
Partage / Export
Résumé
Since mid-March 2020, due to the COVID-19 pandemic, higher education has been facing a very uncertain situation, despite the hasty implementation of information and communication technologies for distance and online learning. Hybrid learning, i.e., the mixing of distance and face-to-face learning, seems to be the rule in most universities today. In order to build a post-COVID-19 university education, i.e., one that is increasingly digital and sustainable, it is essential to learn from these years of health crisis. In this context, this paper aims to identify and quantify the main factors affecting mechanical engineering student performance in order to build a generalized linear autoregressive (GLAR) model. This model, which is distinguished by its simplicity and ease of implementation, is responsible for predicting student grades in online learning situations in hybrid environments. The thirty or so variables identified by a previously tested model in 2020–2021, in which distance learning was the exclusive mode of learning, were evaluated in blended learning spaces. Given the low predictive power of the original model, about ten new factors, specific to blended learning, were then identified and tested. The refined version of the GLAR model predicts student grades to within ±1 with a success rate of 63.70%, making it 28.08% more accurate than the model originally created in 2020–2021. Special attention was also given to students whose grade predictions were underestimated and who failed. The methodology presented is applicable to all aspects of the academic process, including students, instructors, and decisionmakers.