Predicting Student’s Performance Based on Cloud Computing

Jedidi, Y.; Ibriz, A.; Benslimane, M.; Tmimi, M.; Rahhali, M.

COVID-19 Coronavirus epidemic has created a calamitous worldwide situation. The Moroccan University Sidi Mohammed Ben Abdellah of Fez mobilized to develop platforms for distance learning. New tools for e-learning (online learning) were developed, and advanced learning management systems (LMSs) were deployed. Predicting student's performance is more difficult because of the large amount of data including the huge number of learners and the educational content variety. Currently, in the University of Fez, the lack of current mechanisms to assess and control the development and performance of the students is not discussed. In this context, Cloud computing is becoming a hot research subject when faced with large-scale data and is commonly used to solve this issue. Big Data is a leading concept because of its permanent optimization and opportunities offering data collection, analysis, storage, optimization, processing, and data representation to e-learning professionals. The objective of this paper is to develop a model of predicting student’s performance based on cloud computing as part of the normal enhancement of online learning by incorporating new information and communication technologies. © 2022, Springer Nature Singapore Pte Ltd.