ABSTRACT
Technological progress has led to the integration of technology into the practices of the learning process. The aim of this integration is to overcome the deficiencies of traditional methods to ensure greater efficiency. Despite the technology revolution, the adoption of e-learning has always been a choice in the educational process. The COVID 19 crisis has shown the need for a total transition to e-learning during the period of lockdown. According to several studies that have assessed the impact of COVID 19 on their education systems and the solutions adopted, hybrid learning represents an adequate solution to benefit from the advantages of both modes: face-to-face and distance learning. For this purpose, we propose in this paper a hybrid learning model based on an adaptive collaborative work through an intelligent assignment of the group member's roles by using Naïve Bayes algorithm and Belbin theory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Technological progress has led to the integration of technology into the practices of the learning process. The aim of this integration is to overcome the deficiencies of traditional methods to ensure greater efficiency. Despite the technology revolution, the adoption of e-learning has always been a choice in the educational process. The COVID 19 crisis has shown the need for a total transition to e-learning during the period of lockdown. According to several studies that have assessed the impact of COVID 19 on their education systems and the solutions adopted, hybrid learning represents an adequate solution to benefit from the advantages of both modes: face-to-face and distance learning. For this purpose, we propose in this paper a hybrid learning model based on an adaptive collaborative work through an intelligent assignment of the group member's roles by using Naïve Bayes algorithm and Belbin theory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.