ÖĞRENCİLERİN STEM EĞİTİMİ TERCİHLERİNİN VERİ MADENCİLİĞİ YAKLAŞIMI ile TAHMİN EDİLMESİ

Year 2019, Volume: 9 Issue: 1, 73 - 88, 31.01.2019

Gökhan Akçapınar Erdal Coşgun

https://doi.org/10.17943/etku.429785

Cited By: 3

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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