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dc.SupervisorProfessor Sherief Abdallah
dc.contributor.authorSHWEDEH, FATEN
dc.date.accessioned2022-05-19T07:10:13Z
dc.date.available2022-05-19T07:10:13Z
dc.date.issued2018-04
dc.identifier.other2015128
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/2006
dc.description.abstractEducational 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.en_US
dc.language.isoenen_US
dc.publisherThe British University in Dubai (BUiD)en_US
dc.subjectprediction modelen_US
dc.subjectDubai private schools dataen_US
dc.subjectEducational Data Mining (EDM)en_US
dc.subjectclassificationen_US
dc.subjectdata miningen_US
dc.subjectinternational assessmentsen_US
dc.subjecteducational dataen_US
dc.subjectUnited Arab Emirates (UAE)en_US
dc.subjectTrends in Mathematics and Science Study (TIMSS)en_US
dc.titleUsing Educational Data Mining Techniques in Predicting Grade-4 students’ performance in TIMSS International Assessments in the UAEen_US
dc.typeDissertationen_US
dc.Location2018 T 58.6 S59


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