MSc. DSA Theses and Dissertations (2024)

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Browsing MSc. DSA Theses and Dissertations (2024) by Author "Ng'eno, B. C."

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    Leveraging learning analytics to optimize virtual learners’ performance
    (Strathmore University, 2024) Ng'eno, B. C.
    Learning analytics has gained traction globally over the years with many institutions acknowledging its potential to optimize learning and the environments in which learning occurs. The study is structured around three primary objectives aiming to provide a key focus on optimization of virtual learners’ academic outcome using learning analytics approaches. Firstly, it aims to identify key indicators that reliably predict students' performance within academic settings. Secondly, it seeks to examine and compare the effectiveness of different algorithms in accurately forecasting students' performance outcomes. Lastly, the research endeavours to develop and deploy a performance prediction and early alert tool utilizing R-Shiny. In this study, the performance of Logistic Regression, Naive Bayes, K-Nearest Neighbors and Support Vector Machine in predicting learners’ performance were evaluated. Utilizing 21,216 records from students at the The Open University UK, the results indicated Logistic Regression as the best performing model with a precision rate of 90% and key features encompassed student demographic information and academic history. The findings of this study give invaluable insights to educational institutions on leveraging learning analytic practices for data-driven interventions to optimize and enhance student performance. In conclusion, this study not only provides a tangible solution of students’ performance optimization but also contributes to the growing body of knowledge on learning analytic practices that provide solutions which can be incorporated in the education sector. Keywords: Learning analytics, machine learning, student performance, R-Shiny.