Abstract
Student performance prediction is an area of concern for educational institutions. At the University level learning system, the method or rule adopted to identify the candidates who pass or fail differs depending on various factors such as the course, the department of study and so on. Predicting the result of a student in a course is an issue that has recently been addressed using machine learning techniques. The focus of this work is to find a way to predict a student’s academic performance in the University using the machine learning approach. This is done by using the previous records of the student rather than applying course dependent formulae to predict the student’s final grade. In this work, meta decision tree classifier techniques based on four representative learning algorithms, namely Adaboost, Bagging, Dagging and Grading are used to construct different decision trees. REPTree is used as the decision tree method for meta learning. These four meta learning methods have been compared separately with respect to the training and test sets. Adaboost is found to be the best meta decision classifier for predicting the student’s result based on the marks obtained in the semester.
Highlights
Universities operate in very energetic and effective viable environments
The main purpose of this work is to compare the performance of various meta decision tree algorithms in predicting the performance of students in both training sets and test sets
The methods analysed for distance learning included decision-tree classification, support vector machine, general unary hypotheses automaton, Bayesian networks and linear and logistic regression
Summary
Universities operate in very energetic and effective viable environments. A massive volume of data with reference to the students is available in digital form. Many research works have focused on data mining techniques in higher education institutions to enhance the method of learning. Developing an automated system for this will help educators to monitor their students’ achievements (Buldu and Ucgun, 2010; Delen, 2010; Marquez‐Vera et al, 2016) and the students to enhance their learning skills. Predicting a student’s performance has been studied previously in educational data mining research in the context of student attrition. Wolff et al (2014) explored the effectiveness of predictive modelling methods for identifying students who will benefit most from tutor interventions in distance learning. A new Moodle’s module was developed for gathering forum indicators Using this Moodle, different experiments were carried out using real data from 114 university students in a first-year course in computer science. The results achieved proved its effectiveness both in terms of final prediction at the end of the course and early prediction before the end of the course
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