Developing an Integrated Big Data Analytics Framework for Personalized E-Learning in Open and Distance Learning Institutions in Kenya

Abstract: Big Data Analytics (BDA) offers open and distance learning (ODL) institutions a means of converting learner data into personalized support, yet many institutions in Kenya lack structured guidance for implementing it. Learner data remain fragmented across systems and are rarely translated into timely personalized interventions, a situation aggravated by infrastructure, skills and governance gaps. This study aimed to develop a framework for implementing BDA in personalized e-learning environments in ODL institutions in Kenya. A case study design was applied at the Open University of Kenya. Stratified, simple random and purposive sampling produced a sample of 293 students and staff, of whom 279 (95.2%) returned questionnaires. Interviews, collaborative framework meetings and document review supplemented the survey. Data were analysed using descriptive statistics, t-tests, ANOVA, multiple regression, thematic and content analysis, and partial least squares structural equation modelling. BDA (β = 0.42) and learning analytics (β = 0.36) significantly predicted personalized e-learning, and the model explained 62% of its variance. Faculty adoption and proficiency (β = 0.18) and quality of data infrastructure (β = 0.21) significantly moderated these effects. Document review and interviews exposed fragmented systems, weak governance and capacity gaps. The resulting Integrated Big Data Analytics–Enhanced Personalized Electronic Learning (IBDA-EPEL) framework showed acceptable reliability (composite reliability 0.88–0.93; AVE 0.62–0.73) and was validated by eight experts (mean 4.75/5). The study concludes that effective personalization requires integrated data, learning analytics, competent faculty, reliable infrastructure and governance. ODL institutions should adopt the framework through phased implementation supported by data integration, staff development and data governance.

Keywords: Big Data Analytics; Learning Analytics; Personalized E-Learning; Open and Distance Learning; Framework Development; Kenya