Eğitim Teknolojisi Kuram ve Uygulama

Yazarlar: Gökhan AKÇAPINAR, Erdal COŞGUN

Konular:Sosyal

DOI:10.17943/etku.429785

Anahtar Kelimeler:Educational data mining,Prediction,Classification,Machine learning,STEM

Özet: In this study, it was aimed to create a model to predict whether secondary school students would prefer STEM education in high school by analyzing the use data in the intelligent tutoring system named ASSISTments with data mining methods. In the study, the data set given to the participants in the ASSISTments Data Mining Competition 2017 was used. Purpose of the competition is to develop a predictive model for predicting whether students will continue to STEM education in high school or not. For this purpose, approximately 1 million lines of click-stream data of 1709 students using the system between 2004-2007 were given to the participant while deleting the personal data defining the students. Participants were also given a training data containing information of 514 students along with their STEM field choices. So they could test the predictive models they developed on this data set. In this study, different preprocessing methods and different classification algorithms have been tested comparatively in the data set and their results are reported. As a result of the analysis, the best classification model correctly predicted STEM field choices of students by 89.1%. Important variables to predict students’ STEM preferences are also investigated.

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