Factors Affecting Student Satisfaction and Utilization of the Learning Management System (LMS): A Case Study at the University of Cape Coast.

Abstract

This study investigates factors affecting students’ satisfaction and utilisation of the Learning Management System (LMS) at the University of Cape Coast (UCC), Ghana, in line with the mandate of the National ICT Policy (2020) to integrate LMS into institutions. Despite LMS platforms being critical to blended learning, student satisfaction remains inconsistent due to infrastructural and pedagogical challenges. The research employs a mixed-methods explanatory sequential design, integrating quantitative analysis of survey data from 635 students and qualitative insights from open-ended responses. Stratified random sampling ensured demographic representation across modes of study (regular, distance, sandwich, and affiliation), level of study, age groups, and gender. The key findings show that technical infrastructure is the primary driver of satisfaction (β = -0.41, p < 0.001), with distance students reporting the most technical issues (40% "Very Often") and the lowest satisfaction levels (3.15). Lecturer interaction emerged as an essential mediator (β = 0.28), helping to reduce technical dissatisfaction, while resource accessibility (β = 0.17) was necessary but insufficient without technical stability. Notable demographic differences were observed: satisfaction increased with age (16–22: 3.12; 51+: 4.35; r = 0.38), and students in the regular mode outperformed those in the distance mode of study (4.28 vs. 3.15; β = 0.22). Thematic analysis identified technical issues (42%), resource gaps (28%), and support services (18%) as key areas for improvement. The study advances the Context-Integrated LMS Satisfaction (C-LMS) model, resolving limitations of existing frameworks (TAM, UTAUT, IQT) by incorporating Ghana-specific barriers (e.g., bandwidth, infrastructure deficits) and demographic moderators. Recommendations include: first, short-term technical triage (offline modules, 24/7 support); second, medium-term infrastructure upgrades (50% bandwidth increase); third, long-term systemic transformation (AI-driven issue prediction, a national LMS consortium). These findings provide a blueprint for enhancing the LMS resilience in resource-constrained contexts, emphasising that technical stability and human factors are interdependent in driving satisfaction.