Student Learning Prediction Using Machine Learning Techniques
Anbukarasi V1, A. John Martin2

1Anbukarasi V, Research Scholar, Department of Computer Applications, Sacred Heart College, Tirupattur, India
2A. John Martin, Department of Computer Applications, Sacred Heart College, Tirupattur, India.
Manuscript received on July 30, 2019. | Revised Manuscript received on August 25, 2019. | Manuscript published on August 30, 2019. | PP: 4179-4183 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8710088619/2019©BEIESP | DOI: 10.35940/ijeat.F8710.088619
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Abstract: Now a day’s e-learning is smartly growing technology. This technology is more helpful for students to communicate with their professors through chats or emails. E-Learning also removes the obstacle of physical presence of an E-learner. The main aim of this paper is to predict student performance in their final exams using different machine learning techniques. Information like attendance, marks, assignments, class participation, seminar, CA, projects and semester are collected to predict student performance. This prediction helps the instructors to analyze their students based on their performance. For that we have used WEKA tool for the prediction of the student performance. WEKA (Waikato Environment for Knowledge Analysis) is one of the data mining too which is used for the classification and clustering using data mining algorithms. This prediction helps the students and the staffs to know how much effort their students need to be put in their final exams to get good marks. 
Keywords: E-learning, Data Mining, Machine Learning algorithms, Data Preprocessing.