
Student Learning Prediction Using Machine Learning Techniques
Author(s) -
Alt-Epping V,
A. John Martin
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8710.088619
Subject(s) - computer science , attendance , obstacle , machine learning , cluster analysis , class (philosophy) , artificial intelligence , political science , law , economics , economic growth
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.