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Publication Open Access Systematic Review: The Role of Data Analytics in Enhancing Academic Performance Classroom interaction, Learning Analytics in Higher Education(ICSDB 2024 and SLIIT Business School, 2024-12-10) Sithumini, J.H.C.; Sanjuka, A.N.E.; Ranawaka, P. S.; Hasaranga, H.G. D.; Samarakkody, T.; Pathirana, GThe field of data analytics has seen substantial growth, particularly within the education sector. With the recent expansion of e-learning due to the COVID-19 pandemic, the ability to make data-driven decisions in education has become more important than ever. This review synthesizes existing research on the role of data analytics in enhancing academic performance and decisionmaking in higher education. The key objectives are to examine the influence of data analytics on student performance, explore learning analytics’ role in institutional decision-making, and assess the effect of data analytics on e-learning systems, particularly during the COVID-19 pandemic.Publication Embargo Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)(IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. PChronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.Publication Embargo Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD)(IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. PChronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.