Location
Harare, Zimbabwe and Virtual
Start Date
14-9-2023 4:30 PM
End Date
14-9-2023 5:00 PM
Description
Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of such systems in creating effective personalized learning paths. Results revealed limitations that prevent students from benefiting from effective personalized learning paths. This research applies Design Science Research Methodology (DSRM) to propose a new model that leverages further insight provided by students' social data to enhance personalized learning paths.
Included in
Artificial Intelligence and Robotics Commons, Computer and Systems Architecture Commons, Software Engineering Commons
A social profile-based e-learning model
Harare, Zimbabwe and Virtual
Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of such systems in creating effective personalized learning paths. Results revealed limitations that prevent students from benefiting from effective personalized learning paths. This research applies Design Science Research Methodology (DSRM) to propose a new model that leverages further insight provided by students' social data to enhance personalized learning paths.
