Liyanage, S. RKasthuriarachchi, K. T. S2022-05-182022-05-182020Liyanage, S. R., & Kasthuriarachchi, K. T. (2020). Predicting the Academic Performance of Students Using Utility-Based Data Mining. In C. Bhatt, P. Sajja, & S. Liyanage (Ed.), Utilizing Educational Data Mining Techniques for Improved Learning: Emerging Research and Opportunities (pp. 56-85). IGI Global. https://doi.org/10.4018/978-1-7998-0010-1.ch0049781799800101https://rda.sliit.lk/handle/123456789/2348Data mining in education has become an important topic in the sphere of influence of data mining. Mining educational data encompasses developing models, plotting data, and utilizing machine learning algorithms to derive patterns on educational data by attempting to uncover hidden patterns, create information for hidden relationships using educational statistics, and perform many more operations that are unfeasible using traditional computational tools. This research aims to identify the main factors that influence the academic performance of learners in tertiary education system in Sri Lanka. A conceptual framework and an analytical framework on factors affecting the academic performance was constructed with this aim. The analytical framework was then validated with the data collected from technology learners in a tertiary educational institute.enPredictingAcademic PerformanceUtility-BasedData MiningBook chapterDOI: 10.4018/978-1-7998-0010-1.ch004