Using Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAE

Abstract

Educational Data Mining (EDM) is the process of discovering information and relationships from educational data for better understanding of students’ performance, and characteristics of their education providers. Classification is a Data Mining (DM) technique used for prediction. On the other hand, feature selection is the process of finding the best set of features that has the most impact on a specific target. This dissertation provides an extensive descriptive and predictive analysis on Grade-4 student performance in the Trends in Mathematics and Science Study (TIMSS) in the United Arab Emirates (UAE). The main purpose is to bridge the gap between EDM and International Assessments in the Arab world by applying EDM to predict Grade-4 student levels in TIMSS assessments in the UAE. We examined different feature selection methods and classification algorithms to find the best prediction model with the highest accuracy. The study in this dissertation was expanded to delve deeper into Dubai’s private schools data and discover the important features leading to improvements. In addition to building a prediction model to examine if a school will improve in the future TIMSS assessment cycles. As a result, it was found that the Tree-based feature selection method associated with Decision Tree (DT) classifier built the most accurate prediction models on most TIMSS datasets. The main key factors influencing students’ performance in science is discovered and presented. To the best of our knowledge, this study is the first scientific analysis implementing EDM in the field of international assessments in the UAE. In addition to being the first scientific study that considers all TIMSS questionnaires database in EDM task.