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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

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.

Supporting Institution

Destekleyen bir kurum ya da kuruluş bulunmamaktadır

Project Number

-

Thanks

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

References

  • Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954
  • Aninditya, A., Hasibuan, M. A., & Sutoyo, E. (2019, November). Text mining approach using TF-IDF and naive Bayes for classification of exam questions based on cognitive level of bloom's taxonomy. In 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS) (pp. 112-117). IEEE.
  • Antons, D., Grünwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. R&D Management, 50(3), 329-351.
  • Atman-Uslu, N., Mumcu, F., & Eğin, F. (2018). Görsel programlama etkinliklerinin ortaokul öğrencilerinin bilgi-işlemsel düşünme becerilerine etkisi. Ege Eğitim Teknolojileri Dergisi 2(1), 19-31.
  • Atman Uslu, N. (2022). How do computational thinking self-efficacy and performance differ according to secondary school students’ profiles? The role of computational identity, academic resilience, and gender. Education and Information Technologies, 1-25.
  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada (Vol. 1, p. 25).
  • Brousseau, E., & Sherman, M. (2019, October). Position: The Role of Blocks Programming in Forming Computational Identity. In 2019 IEEE Blocks and Beyond Workshop (B&B) (pp. 15-17). IEEE.
  • Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & education, 109, 162-175.
  • Capobianco, B. M., French, B. F., & Diefes-Dux, H. A. (2012). Engineering identity development among pre-adolescent learners. Journal of Engineering Education, 101(4), 698–716. https://doi.org/10.1002/j.2168-9830.2012.tb01125.x
  • Frymier, A. B., Shulman, G. M. and Houser, M. L. 1996. The development of a learner empowerment measure. Communication Education, 45, 181–199.
  • Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60-76.
  • Hotho, A., Nürnberger, A., & Paaß, G. (2005). A brief survey of text mining. Journal for Language Technology and Computational Linguistics, 20(1), 19-62.
  • Houser, M. L., & Frymier, A. B. (2009). The role of student characteristics and teacher behaviors in students’ learner empowerment. Communication Education, 58(1), 35-53.
  • Kazakof, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245–255.
  • Kong, S. C., & Wang, Y. Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 106230.
  • Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students' interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & education, 127, 178-189.
  • Kong, S. C., & Lai, M. (2022). Computational identity and programming empowerment of students in computational thinking development. British Journal of Educational Technology, 53(3), 668-686.
  • Korkmaz, Ö., Balcı, H., Çakır, R., & Erdoğmuş, F. U. (2020). Görsel programlama ortamlarında yapılan oyun geliştirme etkinliklerinin etkililiği. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, (57), 52-73.
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150
  • Lin, P. H., & Chen, S. Y. (2020). Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 8, 45689-45699.
  • Moon, J., Do, J., Lee, D., & Choi, G. W. (2020). A conceptual framework for teaching computational thinking in personalized OERs. Smart Learning Environments, 7(1), 1-19.
  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3).
  • Oluk, A., Korkmaz, Ö., & Oluk, H. A. (2018). Scratch’ın 5. sınıf öğrencilerinin algoritma geliştirme ve bilgi-işlemsel düşünme becerilerine etkisi. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 9(1), 54-71.
  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247.
  • Page, N., & Czuba, C. E. (1999). Empowerment: What is it. Journal of extension, 37(5), 1-5.
  • Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: A case study. International Journal of Mobile Learning and Organisation, 10(3), 187–202
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 1-15.
  • Saritepeci, M. (2020). Developing computational thinking skills of high school students: Design-based learning activities and programming tasks. The Asia-Pacific Education Researcher, 29(1), 35-54.
  • Sfard, A., & Prusak, A. (2005). Telling identities: In search of an analytic tool for investigating learning as a culturally shaped activity. Educational Researcher, 34(4), 14–22. https://doi.org/10.3102/0013189X034004014
  • Sobral, S. R. (2021). Teaching and Learning to Program: Umbrella Review of Introductory Programming in Higher Education. Mathematics, 9(15), 1737.
  • Sun, L., Guo, Z., & Zhou, D. (2022). Developing K-12 students’ programming ability: A systematic literature review. Education and Information Technologies, 27(5), 7059-7097.
  • Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education, 162, 104083.
  • Uday, S. S., Pavani, S. T., Lakshmi, T. J., & Chivukula, R. (2022). COVID-19 literature mining and retrieval using text mining approaches. arXiv preprint arXiv:2205.14781.
  • Yaman, U. C. (2022). Metin madenciliği teknikleri ile Türkçe müşteri yorumlarının analizi (Master's thesis, Eskişehir Teknik Üniversitesi).

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

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

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.

Project Number

-

References

  • Angeli, C., & Valanides, N. (2020). Developing young children’s computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in Human Behavior, 105, 105954
  • Aninditya, A., Hasibuan, M. A., & Sutoyo, E. (2019, November). Text mining approach using TF-IDF and naive Bayes for classification of exam questions based on cognitive level of bloom's taxonomy. In 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS) (pp. 112-117). IEEE.
  • Antons, D., Grünwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. R&D Management, 50(3), 329-351.
  • Atman-Uslu, N., Mumcu, F., & Eğin, F. (2018). Görsel programlama etkinliklerinin ortaokul öğrencilerinin bilgi-işlemsel düşünme becerilerine etkisi. Ege Eğitim Teknolojileri Dergisi 2(1), 19-31.
  • Atman Uslu, N. (2022). How do computational thinking self-efficacy and performance differ according to secondary school students’ profiles? The role of computational identity, academic resilience, and gender. Education and Information Technologies, 1-25.
  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American educational research association, Vancouver, Canada (Vol. 1, p. 25).
  • Brousseau, E., & Sherman, M. (2019, October). Position: The Role of Blocks Programming in Forming Computational Identity. In 2019 IEEE Blocks and Beyond Workshop (B&B) (pp. 15-17). IEEE.
  • Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & education, 109, 162-175.
  • Capobianco, B. M., French, B. F., & Diefes-Dux, H. A. (2012). Engineering identity development among pre-adolescent learners. Journal of Engineering Education, 101(4), 698–716. https://doi.org/10.1002/j.2168-9830.2012.tb01125.x
  • Frymier, A. B., Shulman, G. M. and Houser, M. L. 1996. The development of a learner empowerment measure. Communication Education, 45, 181–199.
  • Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60-76.
  • Hotho, A., Nürnberger, A., & Paaß, G. (2005). A brief survey of text mining. Journal for Language Technology and Computational Linguistics, 20(1), 19-62.
  • Houser, M. L., & Frymier, A. B. (2009). The role of student characteristics and teacher behaviors in students’ learner empowerment. Communication Education, 58(1), 35-53.
  • Kazakof, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245–255.
  • Kong, S. C., & Wang, Y. Q. (2020). Formation of computational identity through computational thinking perspectives development in programming learning: A mediation analysis among primary school students. Computers in Human Behavior, 106, 106230.
  • Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students' interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & education, 127, 178-189.
  • Kong, S. C., & Lai, M. (2022). Computational identity and programming empowerment of students in computational thinking development. British Journal of Educational Technology, 53(3), 668-686.
  • Korkmaz, Ö., Balcı, H., Çakır, R., & Erdoğmuş, F. U. (2020). Görsel programlama ortamlarında yapılan oyun geliştirme etkinliklerinin etkililiği. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, (57), 52-73.
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150
  • Lin, P. H., & Chen, S. Y. (2020). Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 8, 45689-45699.
  • Moon, J., Do, J., Lee, D., & Choi, G. W. (2020). A conceptual framework for teaching computational thinking in personalized OERs. Smart Learning Environments, 7(1), 1-19.
  • Mouza, C., Yang, H., Pan, Y. C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3).
  • Oluk, A., Korkmaz, Ö., & Oluk, H. A. (2018). Scratch’ın 5. sınıf öğrencilerinin algoritma geliştirme ve bilgi-işlemsel düşünme becerilerine etkisi. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 9(1), 54-71.
  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247.
  • Page, N., & Czuba, C. E. (1999). Empowerment: What is it. Journal of extension, 37(5), 1-5.
  • Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: A case study. International Journal of Mobile Learning and Organisation, 10(3), 187–202
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(1), 1-15.
  • Saritepeci, M. (2020). Developing computational thinking skills of high school students: Design-based learning activities and programming tasks. The Asia-Pacific Education Researcher, 29(1), 35-54.
  • Sfard, A., & Prusak, A. (2005). Telling identities: In search of an analytic tool for investigating learning as a culturally shaped activity. Educational Researcher, 34(4), 14–22. https://doi.org/10.3102/0013189X034004014
  • Sobral, S. R. (2021). Teaching and Learning to Program: Umbrella Review of Introductory Programming in Higher Education. Mathematics, 9(15), 1737.
  • Sun, L., Guo, Z., & Zhou, D. (2022). Developing K-12 students’ programming ability: A systematic literature review. Education and Information Technologies, 27(5), 7059-7097.
  • Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature Review. Computers & Education, 162, 104083.
  • Uday, S. S., Pavani, S. T., Lakshmi, T. J., & Chivukula, R. (2022). COVID-19 literature mining and retrieval using text mining approaches. arXiv preprint arXiv:2205.14781.
  • Yaman, U. C. (2022). Metin madenciliği teknikleri ile Türkçe müşteri yorumlarının analizi (Master's thesis, Eskişehir Teknik Üniversitesi).
There are 34 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Nilüfer Atman Uslu 0000-0003-2322-4210

Aytuğ Onan 0000-0002-9434-5880

Project Number -
Early Pub Date June 23, 2023
Publication Date June 24, 2023
Submission Date May 11, 2023
Acceptance Date June 16, 2023
Published in Issue Year 2023 Volume: 5 Issue: 1

Cite

APA Atman Uslu, N., & Onan, A. (2023). Investigating computational identity and empowerment of the students studying programming: A text mining study. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, 5(1), 29-45.