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DATA MINING FOR MACHINE LEARNING AND STATISTICS

Dr. John Martin has been an academic in computing education, research, and consulting for twenty-five years. He has extensive experience in higher education as an educator and administrator in India and the Middle East. Currently, he is working in the School of Computer Science and Information Technology at Jazan University (Ministry of Education), KSA. He has authored a book entitled “EEG Signal Processing: A Machine Learning-Based Framework.” He has published extensively in the fields of machine intelligence and biomedical data analytics and has served as an editor and reviewer for refereed journals. He is a professional member of IEEE and ACM. His research interests include machine intelligence, predictive analytics, and biomedical signal processing.

Dr. V SelvaKumar, Assistant Professor in the Department of Mathematics and Statistics, Bhavan’s Vivekananda College of Science, Humanities & Commerce.  He did his Ph.D from BITS Pilani, Hyderabad Campus.  Dr V Selvakumar has 21 years of experience as an active academician and researcher. He has published 22 papers in different national and international journals, 5  patents, and authored a book to his credit. Also, presented twelve papers at national and international conferences. His areas of interest are Data analytics, Time Series Analysis, Machine Learning and Deep learning.

Rashmi Rani Patro received her Phd degree from Siksha ‘O’ Anusandhan Deemed to be University in the Department of Computer Science and Engineering. She completed her B.tech degree in 2005 and M.tech degree in 2010. Her current area of research is Digital Business, Operations Research and Data mining.

Rojalini Patro received her Phd degree from Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India. She is currently working as an Assistant Professor in the Department of Mathematics, K L Deemed to be University, Vijayawada, India. Her research interests include Fuzzy Optimization Theory and Operations Research.

Description

There has been great advancement in the area of learning analytics as well as in the creation of methods for delivering technology enhanced learning systems that are successful in education. These studies may assist academics in identifying students who are at danger of failing or quitting. It is essential to identify at-risk pupils as soon as possible so that academic staff and institutions can take prompt action.In this thesis, both well-established machine learning approaches and a unique approach are taken into consideration for the prediction of student outcomes and the support of interventions. A range of predictive analyses are presented, along with a live experiment. It examines the current state of technologically enhanced learning systems and the related institutional barriers to their application. The amount of data that is readily accessible for analysis is constrained by institutional privacy policies and the fact that many courses have very small student cohorts. This topic of analysis and prediction seems to have received relatively little scientific interest. I describe an experiment done on a final-year university module with a cohort of 23 students, where the data that could be predicted only included attendance at lectures and tutorials, visits to virtual learning environments, and intermediate evaluations. During the delivery of the lesson, I use a range of machine learning techniques to evaluate and forecast student performance. While there were some conflicting findings, I discovered the possibility for forecasting student performance in small student cohorts with relatively few student variables, with accuracies favorably compared with published results utilizing big cohorts and much more data. I suggest that the analyses will be beneficial in helping module leaders find chances to implement prompt academic interventions.

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