Design of a Smart MOOC Trust Model: Towards a Dynamic Peer Recommendation to Foster Collaboration and Learner’s Engagement


  • Khadija Elghomary Mohammed V University
  • Driss Bouzidi Mohammed V University
  • Najima Daoudi School of Information Sciences



Trust Management System (TMS), Social Internet of Things (SIoT), Machine Learning (ML), Smart Education, Massive Open Online Course (MOOC), Peer Recommendation.


Recent evolutions in the Internet of Things (IoT) and Social IoT (SIoT) are facilitating collaboration as well as social interactions between entities in various environments, especially Smart Learning Ecosystems (SLEs). However, in these contexts, trust issues become more intense, learners feel suspicious and avoid collaborating with their peers, leading to their demotivation and disengagement. Hence, a trust management system (TMS) has become a crucial challenge to promote qualified collaboration and stimulate learners' engagement. In the literature, several trust models were proposed in various domains, but rarely those that address trust issues in SLEs, especially in MOOCs. While these models exclusively rank the best nodes and fail to detect the untrustworthy ones. Therefore, in this paper, we propose Machine Learning-based trust evaluation model that considers social and dynamic trust parameters to quantify entities' behaviors. It can distinguish trustworthy and untrustworthy behaviors in MOOCs to recommend benign peers while blocking malicious ones to build a dynamic trust-based peer recommendation in the future phase. Our model prevents learners from wasting their time in unprofitable interactions, protects them from malicious actions, and boosts their engagement. A simulation experiment using real-world SIoT datasets and encouraging results show the performance of our trust model.

Author Biographies

Khadija Elghomary, Mohammed V University

is a Khadija Elghomary received her master’s degree in information sciences in Information Sciences School (ESI) and is currently a Ph.D. student from the National Superior School of Computer Science and Systems Analysis (ENSIAS). She began her Ph.D. studies since 2016 and is working on many research areas related to technology-enhanced learning such as MOOC platforms, tutoring in Virtual Learning Communities, machine learning, recommender systems, Social Networks Analysis and Trust Models development. (Email:

Driss Bouzidi, Mohammed V University

is an associate professor in Computer Sciences at ENSIAS, University Mohammed V, Rabat, Morocco. His research interests are mainly in the areas of distributed systems and security services. He has made many contributions to several chapters in some international books related to e-learning. He was vice-chair of the international conference NGNS'09, TCP chair of NGNS10, NGNS12, and ICEER13 and chair of JDSIRT’2017. He is a founding member of two research associations APRIMT and e-NGN. (Email:

Najima Daoudi, School of Information Sciences

is a Professor at the School of Information Sciences, Rabat, Morocco. She is an Engineer of the National Institute of Statistics and Applied Economics and has a Ph.D. in Computer Science from ENSIAS. She has produced several articles in E-learning, M-learning and Ontology development since 2005. She was chair of the international conference ICSSD’19. (Email:




How to Cite

Elghomary, K., Bouzidi, D., & Daoudi, N. (2022). Design of a Smart MOOC Trust Model: Towards a Dynamic Peer Recommendation to Foster Collaboration and Learner’s Engagement. International Journal of Emerging Technologies in Learning (iJET), 17(05), pp. 36–56.