Identifying the Classification Performances of Educational Data Mining Methods: A Case Study for TIMSS

Abstract

Educational data mining (EDM) is a rapidly growing research area, and the outputs obtained from EDM shed light on educators' and education planners' efforts to make efficient decisions concerning educational strategies. However, a lack of work still exists on using EDM methods for international assessment studies such as the International Association for the Evaluation of Educational Achievement's Trends in International Mathematics and Science Study (IEA's TIMSS). This study aims to fill the gap in the current literature on the latest-released TIMSS 2011 data by applying a decision tree, a Bayesian network, a logistic regression, and neural networks. The best performing algorithm in classification based on several performance measures has been found for eighth-grade Turkish students' mathematics data. During the construction of models, 11 student-based factors have been taken into account. The results show that logistic regression outperforms other algorithms in terms of measuring classification performance. The factor of student confidence has also been found as the most effective factor on eighth-grade students' mathematics achievement.