Programlama eğitimi alan öğrencilerin bilgi işlemsel kimlikleri ve yetkilendirilmelerinin incelenmesi: Bir metin madenciliği çalışması

Year 2023, Volume: 5 Issue: 1, 29 - 45, 24.06.2023

Nilüfer Atman Uslu , Aytuğ Onan

Abstract

Bu çalışmada programlama eğitimi alan öğrencilerin açık uçlu sorulara verdikleri cevaplardan elde edilen metinlerin metin madenciliği algoritmaları ile tahmin edilmesi amaçlanmıştır. Böylece bilgi-işlemsel kimlik ve programlamada yetkilendirilmeleri ile ilgili metin tabanlı veriler analiz edilmeye ve farklı algoritmaların performansları karşılaştırılmaya çalışılmıştır. Araştırmanın katılımcılarını yaş aralığı 12-20 arasında değişen programlama eğitimi alan 646 öğrenci oluşturmuştur. Programlama eğitimi alan öğrencilerin görüşlerini toplamak için açık uçlu sorulardan oluşan elektronik bir form hazırlanmıştır. Bilgi-işlemsel kimlik ve (3 soru) ve güçlendirme (3 soru) ile ilgili toplam altı açık uçlu soru hazırlanmıştır. Veri setinin analizinde metin madenciliği süreci izlenmiştir. Analizler Python 3.8 programında yapılmıştır. Çalışmada Word2vec (W2v) ve Terim Frekans-Ters Doküman Frekansı (TF-IDF) kelime temsil yöntemleri kullanılmıştır. Bu çalışmada beş makine öğrenme algoritmasının performansı karşılaştırılmıştır: (a) Lojistik regresyon, (b) Karar ağacı, (c) Destek Vektör Makineleri, (d) Rastgele Orman, (e) Yapay Sinir Ağı. Bilgi işlemsel kimlik ile ilgili olarak, en yüksek tahmin doğruluğunun yapay sinir ağı (tf-idf) ve lojistik regresyon (tf-idf) algoritmasında olduğu görülmüştür. Bu algoritmalar, bilgi işlemsel kimlik ile ilgili olarak % 93'lük bir doğruluk oranına sahiptir. Programlamada yetkilendirme ile ilgili metin-veriler incelendiğinde lojistik regresyon (tf-idf) yönteminin en yüksek doğruluk tahmin oranına (%96) ulaştığı belirlenmiştir. Bu yöntemin ardından rastgele orman (tf-idf), destek vektör makinesi (tf-idf) ve yapay sinir ağı (tf-idf) algoritmaları %94 doğrulukla tahmin edilmiştir. Elde edilen bu puanların %90'ın üzerinde olması tahmin performansının yeterli olduğu şeklinde yorumlanabilir.

Keywords

Bilgi-işlemsel kimlik, Programlama, Yetkilendirme, Metin madenciliği

Supporting Institution

Destekleyen bir kurum ya da kuruluş bulunmamaktadır

Project Number

-

Thanks

Bu çalışma, birinci yazarın, ikinci yazarın danışmanlığında yürütülen yüksek lisans tezinden üretilmiştir.

Investigating computational identity and empowerment of the students studying programming: A text mining study

Abstract

This study aimed to predict the texts obtained from the answers given by the students receiving programming education to open-ended questions, with text mining algorithms. Thus, an attempt was made to analyze text-based data in research on computational identity and programming empowerment and to compare the performances of different algorithms. The participants of the study consisted of 646 students studying programming with age range varies between 12-20. An electronic form consisting of open-ended questions was prepared to collect the opinions of the students who received programming education. There are a total of six open-ended questions about computational identity (3 questions) and empowerment (3 questions). The text mining process was followed in the analysis of the data set. Analyzes were carried out in Python 3.8 program In this study, Word2vec (W2v) and Term Frequency-Inverse Document Frequency (TF-IDF) word representation methods were used. Five machine learning algorithms compared in this study: (a) Logistic regression, (b) Decision tree, (c) Support Vector Machines, (d) Random Forest, (e) Artificial Neural Network. Concerning computational identity, it was found that the highest estimation accuracy was in artificial neural network (tf-idf) and logistic regression (tf-idf) algorithm. These algorithms have an accucary rate of 93% regarding computational identity. When the text-data related to programming empowerment was analyzed, it was determined that the logistic regression (tf-idf) method reached the highest accuracy prediction rate (96%). Following this method, random forest (tf-idf), support vector machine (tf-idf) and artificial neural network (tf-idf) algorithms predicted with 94% accuracy. The fact that these obtained scores are above 90% can be interpreted as sufficient estimation performance.

Keywords

Computational identity, Programming, Empowerment, Text-mining

Project Number

-

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