Abstract
Learning Management Systems (LMSs) form a critical part of the infrastructure in Higher
Education Institutions (HEIs), for managing, organizing, and delivering learning content and
activities. Their importance has grown significantly, especially since the COVID-19 pandemic,
which accelerated the global shift toward online education. This increasing reliance on LMSs has
revealed a need for more adaptive and personalised approaches that can respond to the diverse
backgrounds, behaviours, and learning goals of students, while also supporting educators more
effectively.
Although personalised learning has shown great promise, most LMSs remain course-centred
rather than student-centred, focusing on predefined learning objectives instead of individual
student characteristics. This limitation is particularly significant in diverse educational contexts
such as South Africa, where students’ varied socio-economic and cultural backgrounds demand
a more tailored learning experience. Moreover, existing personalisation methods rely heavily on
data generated within LMSs, overlooking the broader social dimensions that shape how students
learn and engage.
Research consistently highlights the critical role of social dimensions in learning–students gain
deeper understanding through collaboration, communication, and community. Yet, few systems
leverage students’ social profiles to enhance personalization within LMSs. Addressing this gap,
this study proposes the Social-Profile-Based E-Learning Recommendation (SPER) Model, which
integrates students’ social profile data to generate personalized learning strategies.
Guided by the Design Science Research Methodology (DSRM), this study designs, develops, and
evaluates the SPER Model to demonstrate how social profile integration can enhance
personalisation in LMSs. The findings show that the SPER Model is technically feasible,
educationally beneficial, and socially responsive. Its main advantages include a student-centred
design, adaptive recommendations, continuous feedback, and enriched learning experiences.
The study contributes to theory and practice in two ways: (1) It introduces a structured ontologybased
approach to model students’ social profiles, identifying seven key social aspects
influencing online learning. (2) It develops a hybrid recommendation model that combines
social, contextual, and behavioural data to produce personalized learning strategies...