ABSTRACT
Technology integration has been crucial in the practice of the learning process. The use of technology aims to find effective solutions to traditional learning problems. Despite the enormous efforts adopted, using e-learning systems was optional in many education systems. However, the COVID-19 health crisis has shown the importance of the transition to e-learning to ensure pedagogical continuity. According to several studies that have measured the impact of COVID-19 on education systems and the adopted solutions, blended learning represents an effective solution for combining the advantages of face-to-face and distance learning. But the implementation strategies regarding this mode of learning are still limited. For this purpose, we propose a hybrid learning model based on collaborative work through an intelligent assignment of learner roles. This approach aims to support adaptive learning via a hybrid learning environment. The proposed solution is based mainly on collaborative work as an active learning method, using the Naïve Bayes algorithm and Belbin theory. The usefulness of collaborative work is to keep the learning rhythm between face-to-face and distance learning and to encourage learners' engagement and motivation through this mode of learning. According to Belbin's theory, the results of this work propose an adequate role for each learner. This intelligent assignment leads the learner to live the learning situation and not undergo it. © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
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.