A machine learning approach to identify and track learning styles in MOOCs. - Brahim Hmedna; Ali El Mezouary; Omar Baz; Driss Mammass (ICMCS, 2016)

A predictive model for the identification of learning styles in MOOC environments. - Brahim Hmedna; Ali El Mezouary; Omar Baz (Clust. Comput., 2020)

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS. - Khalid Benabbes; Brahim Hmedna; Khalid Housni; Ahmed Zellou; Ali El Mezouary (Int. J. Interact. Mob. Technol., 2022)

Enhancing Learner Resource Recommendations on LMS through Context and Learning Style Analysis. - Khalid Benabbes; Khalid Housni; Ahmed Zellou; Brahim Hmedna; Ali El Mezouary (CiSt, 2023)

A New Hybrid Approach to Detect and Track Learner's Engagement in e-Learning. - Khalid Benabbes; Khalid Housni; Brahim Hmedna; Ahmed Zellou; Ali El Mezouary (IEEE Access, 2023)

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment. - Khalid Benabbes; Khalid Housni; Ahmed Zellou; Brahim Hmedna; Ali El Mezouary (Int. J. Emerg. Technol. Learn., 2023)

Explore the influence of contextual characteristics on the learning understanding on LMS. - Khalid Benabbes; Khalid Housni; Brahim Hmedna; Ahmed Zellou; Ali El Mezouary (Educ. Inf. Technol., 2023)

Unlocking teachers' potential: MOOCLS, a visualization tool for enhancing MOOC teaching. - Brahim Hmedna; Aïcha Bakki; Ali El Mezouary; Omar Baz (Smart Learn. Environ., 2023)

Transforming Microlearning with Generative AI: Current Advances and Future Challenges. - Kaoutar Boumalek; Ali El Mezouary; Brahim Hmedna; Aïcha Bakki (General Aspects of Applying Generative AI in Higher Education, 2024)