ÖĞRENCİLERİN STEM EĞİTİMİ TERCİHLERİNİN VERİ MADENCİLİĞİ YAKLAŞIMI ile TAHMİN EDİLMESİ

Abstract

Bu çalışmada, ortaokul öğrencilerinin ASSISTments isimli zeki öğretim sistemindeki etkileşim verilerinden lisede STEM eğitimini tercih edip etmeyeceklerini tahmin edecek bir model oluşturulması amaçlanmıştır. Çalışmada 2017 yılında düzenlenen ASSISTments Veri Madenciliği Yarışması’nda (ASSISTments Data Mining Competition 2017) katılımcılara sunulan veri seti kullanılmıştır. Düzenlenen yarışmanın amacı; öğrencilerin ortaokul eğitimleri süresince sistemi kullanım verilerinden lisede STEM alanında kariyerlerine devam edip etmeyeceklerini tahmin etmeye yönelik bir tahmin modeli geliştirilmesidir. Bu amaçla 2004-2007 yılları arasında sistemi kullanan 1709 öğrenciye ilişkin yaklaşık 1 milyon satırlık tıklama verisi, öğrenciyi tanımlayan veriler silinerek, katılımcılara sunulmuştur. Katılımcılara aynı zamanda geliştirdikleri tahmin modellerini test edebilmeleri için veri setinde yer alan 514 öğrencinin STEM kariyerine devam edip etmedikleri bilgisini içeren bir eğitim veri seti verilmiştir. Bu çalışma kapsamında farklı ön işlemle yöntemleri ve farklı sınıflama algoritmaları veri setinde karşılaştırmalı olarak test edilmiş ve sonuçları raporlanmıştır. Yapılan analizler sonucunda elde edilen en iyi sınıflama modeli öğrencilerin STEM eğitimi tercihlerini %89,1 oranında doğru olarak tahmin etmiştir. Aynı zamanda öğrencilerin STEM eğitimi tercihlerini belirlemede önemli olan değişkenler de analiz edilmiştir.

Keywords

• Eğitsel veri madenciliği,tahmin,sınıflama,makine öğrenmesi,STEM

PREDICTING STUDENTS’ STEM CAREER INTEREST WITH DATA MINING APPROACH

Abstract

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.

Keywords

• Educational data mining,prediction,classification,machine learning,STEM

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