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Can Machine Learning Be Taught to Pre-service Teachers in the STEM Fields?

Year 2024, Volume: 5 Issue: 2, 214 - 236
https://doi.org/10.52911/itall.1458322

Abstract

Machine Learning (ML) trainings provide students with 21st century skills and enable students to find solutions to their own problems. The purpose of this study is to design, implement, and evaluate ML training for pre-service teachers in the STEM field in order to contribute to the future workforce in the field of computer science. The participants of the study were 74 pre-service teachers who are studying in the departments of Computer Education and Instructional Technology (CEIT), science education, and mathematics education (STEM fields) at a state university in Istanbul. Convenience sampling method was used in the study. In the research, a single-group pre-test-post-test weak quasi-experimental design was used by using the quantitative method in order to evaluate the training by giving ML training. The training was implemented on the online platform for 3 hours for 8 weeks. "Pretest - Posttest Achievement Test," "Online Student Engagement Scale," "Moodle Activity Data," “Demographic Form,” and "Attendance Forms" were used to collect data. There is a significant difference between the pre-test and post-test averages in favor of the post-test. There is a significant difference between the pretest and posttest scores according to the departments. It has been concluded that the provided training is effective in the success of pre-service teachers. It can be suggested to offer training to different branches and to select participants from elementary and middle school students.

References

  • Ahmad, S. A., Hussain, I., Ahmad, R., & Din, M. N. U. (2020). Performance based prediction of the students in the physics subject using traditional and machine learning approach at higher education level. International Journal of Innovation in Teaching and Learning (IJITL), 6(1), 174-190. https://doi.org/10.35993/ijitl.v6i1.997
  • Buyruk, B., & Korkmaz, O. (2016). Teacher candidates STEM awareness levels. Online Submission, 4(1), 272-279. https://dergipark.org.tr/en/download/article-file/777013
  • Buyukozturk, S., Cakmak, E. K., Akgun, O. E., Karadeniz, S., & Demirel, F. (2008). Scientific research methods. Pegem Academy. https://avesis.medeniyet.edu.tr/yayin/d93be4a3-e3bb-42ae-8635-96946af53ee9/bilimsel-arastirma-yontemleri
  • Bybee, R. W. (2010). What is STEM education?. Science, 329(5995), 996. https://www.science.org/doi/10.1126/science.1194998
  • Cevik, K. K., & Kayakus, M. (2020). Prediction of solution time of user requests delivered to the information technologies department via machine learning. Journal of Engineering Sciences and Design, 8(3), 728-739. https://doi.org/10.21923/jesd.722323
  • Chklovski, T., Jung, R., Fofang, J. B., Gonzales, P., Hub, B. T., & La Paz, B. (2019). Implementing a 15-week AI-education program with under-resourced families across 13 global communities. In International joint conference on artificial intelligence.
  • Chung, C. J., & Shamir, L. (2020). Introducing machine learning with scratch and robots as a pilot program for k-12 computer science education. Science Education, 6, 7. https://people.cs.ksu.edu/~lshamir/publications/ICFL2020.pdf
  • Demirkaya, H., & Sarpel, E. (2018). In the training and development applications virtual reality from the new generation computer technologies, cluster computing and artificial intelligence. Karadeniz Uluslararasi Bilimsel Dergi, (40), 231-245. https://doi.org/10.17498/kdeniz.460145
  • Dixson, M. D. (2015). Measuring student engagement in the online course: The online student engagement scale (OSE). Online Learning, 19(4), n4. https://files.eric.ed.gov/fulltext/EJ1079585.pdf
  • Gok, M. (2017). Predicting academic achievement with machine learning methods. Gazi University Journal of Science and Technology Part C: Design and Technology, 5(3), 139-148. https://dergipark.org.tr/en/download/article-file/341510
  • Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (2019, May). Can children understand machine learning concepts? the effect of uncovering black boxes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. England. https://doi.org/10.1145/3290605.3300645
  • Karasar, N. (2017). Preparing report in research. Nobel Publication Distribution.
  • Kayahan, S. (2018). Artificial intelligence in educational applications: A review. University of Trakya, 201, 20.
  • Kim, K., & Kwon, K. (2024). A systematic review of the evaluation in K-12 artificial intelligence education from 2013 to 2022. Interactive Learning Environments, 1-29. https://doi.org/10.1080/10494820.2024.2335499
  • Lane, D. (2018). Explaining artificial intelligence. Hello World, 4, 44-46.
  • Martin, F., Vahedian Movahed, S., Dimino, J., Farrell, A., Irankhah, E., Ghosh, S., ... & Narain, S. (2024). Perception, Trust, Attitudes, and Models: Introducing Children to AI and Machine Learning with Five Software Exhibits. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1740-1741). https://doi.org/10.1145/3626253.3635512
  • Mduma, N., Kalegele, K., & Machuve, D. (2019). A survey of machine learning approaches and techniques for student dropout prediction. Data Science Journal, 18, 1–10. https://doi.org/10.5334/dsj-2019-014
  • Murphy, R. F. (2019). Artificial intelligence applications to support k–12 teachers and teaching. RAND Corp., USA. https://www.rand.org/pubs/perspectives/PE315.html
  • Nabiyev, V. V. (2012). Artificial intelligence: human-computer interaction. Seckin Publishing.
  • Nafea, I. T. (2018). Machine learning in educational technology, H. Farhadi (Ed.), Machine learning - advanced techniques and emerging applications (s. 175-183). Intech Open. https://www.jstor.org/stable/pdf/resrep19907.pdf
  • Park, D., Ahn, J., Jang, J., Yu, W., Kim, W., Bae, Y., & Yoo, I. (2020). The development of software teaching-learning model based on machine learning platform. Journal of The Korean Association of Information Education, 24(1), 49-57. https://doi.org/10.14352/jkaie.2020.24.1.49
  • Peters, L. (2019). An educational programming environment for teaching the principles of machine learning using lego mindstorms [Unpublished doctoral dissertation]. University of Applied Sciences Leipzig.
  • Polat, E., Hopcan, S., & Arslantaş, T. K. (2022). Çevrimiçi öğrenci bağlılık ölçeğinin Türkçe’ye uyarlanması: geçerlik ve güvenirlik çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 12(1), 41-56.
  • Priya, S., Bhadra, S., Chimalakonda, S., & Venigalla, A. S. M. (2024). ML-Quest: a game for introducing machine learning concepts to K-12 students. Interactive Learning Environments, 32(1), 229-244. https://doi.org/10.1080/10494820.2022.2084115
  • Quiroz, P., & Gutierrez, F. J. (2024). Scratch-NB: A Scratch Extension for Introducing K-12 Learners to Supervised Machine Learning. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education, V. 1 (pp. 1077-1083). https://doi.org/10.1145/3626252.3630920
  • Reyes, A. A., Elkin, C., Niyaz, Q., Yang, X., Paheding, S., & Devabhaktuni, V. K. (2020, August). A preliminary work on visualization-based education tool for high school machine learning education. In 2020 IEEE Integrated STEM Education Conference (ISEC), IEEE. https://ieeexplore.ieee.org/abstract/document/9280629
  • Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., & Subsoontorn, P. (2018). Kids making AI: integrating machine learning, gamification, and social context in STEM education. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering. https://ieeexplore.ieee.org/abstract/document/8615249
  • Senocak, D. (2020). Artificial intelligence in open and distance learning environments: Its opportunities and the concerns raised. Journal of Open Educational Practices and Research, 6(3), 56-78. https://dergipark.org.tr/en/download/article-file/1191727
  • Tataj, X., & Kola, M. (2021). Education policies in line with the latest developments in the field of artificial intelligence: case of Albania. Human & Human, 8(27), 101-116. https://doi.org/10.29224/insanveinsan.818263
  • Tektas, M., Tektas, N., Onat, N., Gokmen, G., Kocyigit, G., & Akinci, T. Ç. (2010). Preparation of web based artificial intelligence techniques training simulators. Marmara University BAP Commission Presidency Project Report, 76-90. https://docplayer.biz.tr/631443-Web￾tabanli-yapay-zeka-tekn-kler-proje-no-fen-e-050608-138.html
  • Tseng, T., Davidson, M. J., Morales-Navarro, L., Chen, J. K., Delaney, V., Leibowitz, M., ... & Shapiro, R. B. (2024). Co-ML: Collaborative machine learning model building for developing dataset design practices. ACM Transactions on Computing Education, 24 (2), 1-37. https://doi.org/10.1145/3641552
  • Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research (JPEER), 1(2), 2. https://doi.org/10.5703/1288284314636
  • Yildiz, O. (2014). Evaluation of the performance of distance education students with machine learning. [Unpublished doctoral dissertation]. Istanbul University.
  • Zhu, K. (2019). An educational approach to machine learning with mobile applications [Unpublished doctoral dissertation]. Massachusetts Institute of Technology.

STEM Alanındaki Öğretmen Adaylarına Makine Öğrenmesi Öğretilebilir mi?

Year 2024, Volume: 5 Issue: 2, 214 - 236
https://doi.org/10.52911/itall.1458322

Abstract

Makine öğrenmesi eğitimleri, öğrencilere 21. yüzyıl becerileri kazandırır ve kendi problemlerine çözüm bulmalarını sağlar. Bu çalışmanın amacı, bilgisayar bilimi alanında gelecekteki iş gücünün oluşturulmasına katkıda bulunmak amacıyla STEM alanındaki öğretmen adaylarına yönelik makine öğrenmesi öğretimini planlamak, uygulamak ve değerlendirmektir. Çalışmanın katılımcıları, İstanbul'da bir devlet üniversitesinde 2020-2021 akademik yılında bilgisayar ve öğretim teknolojileri eğitimi, fen eğitimi ve matematik eğitimi (STEM alanları) bölümlerinde öğrenim gören 74 öğretmen adayıdır. Çalışmada elverişli örnekleme yöntemi kullanılmıştır. Araştırmada, makine öğrenmesi eğitimi verilerek eğitimin değerlendirilmesi amacıyla nicel yöntem kullanılarak tek gruplu ön-test-son-test zayıf yarı deneysel tasarımı kullanılmıştır. Eğitim, 8 hafta boyunca çevrimiçi platformda haftada 3 saat olacak şekilde uygulanmıştır. Veri toplama araçları olarak "Ön Test - Son Test Başarı Testi", " Çevrimiçi Öğrenci Bağlılık Ölçeği", "Moodle Etkinlik Verileri", "Demografik Form" ve "Katılım Formları" kullanılmıştır. Ön-test ve son-test ortalamaları arasında son-test lehine anlamlı bir fark vardır. Bölümlere göre ön test ve son test puanları arasında anlamlı bir fark bulunmaktadır. Verilen eğitimin öğretmen adaylarının başarısında etkili olduğu sonucuna ulaşılmıştır. Farklı branşlara eğitim vermek ve katılımcıların ilkokul ve ortaokul öğrencilerinden seçilmesi önerilebilir.

References

  • Ahmad, S. A., Hussain, I., Ahmad, R., & Din, M. N. U. (2020). Performance based prediction of the students in the physics subject using traditional and machine learning approach at higher education level. International Journal of Innovation in Teaching and Learning (IJITL), 6(1), 174-190. https://doi.org/10.35993/ijitl.v6i1.997
  • Buyruk, B., & Korkmaz, O. (2016). Teacher candidates STEM awareness levels. Online Submission, 4(1), 272-279. https://dergipark.org.tr/en/download/article-file/777013
  • Buyukozturk, S., Cakmak, E. K., Akgun, O. E., Karadeniz, S., & Demirel, F. (2008). Scientific research methods. Pegem Academy. https://avesis.medeniyet.edu.tr/yayin/d93be4a3-e3bb-42ae-8635-96946af53ee9/bilimsel-arastirma-yontemleri
  • Bybee, R. W. (2010). What is STEM education?. Science, 329(5995), 996. https://www.science.org/doi/10.1126/science.1194998
  • Cevik, K. K., & Kayakus, M. (2020). Prediction of solution time of user requests delivered to the information technologies department via machine learning. Journal of Engineering Sciences and Design, 8(3), 728-739. https://doi.org/10.21923/jesd.722323
  • Chklovski, T., Jung, R., Fofang, J. B., Gonzales, P., Hub, B. T., & La Paz, B. (2019). Implementing a 15-week AI-education program with under-resourced families across 13 global communities. In International joint conference on artificial intelligence.
  • Chung, C. J., & Shamir, L. (2020). Introducing machine learning with scratch and robots as a pilot program for k-12 computer science education. Science Education, 6, 7. https://people.cs.ksu.edu/~lshamir/publications/ICFL2020.pdf
  • Demirkaya, H., & Sarpel, E. (2018). In the training and development applications virtual reality from the new generation computer technologies, cluster computing and artificial intelligence. Karadeniz Uluslararasi Bilimsel Dergi, (40), 231-245. https://doi.org/10.17498/kdeniz.460145
  • Dixson, M. D. (2015). Measuring student engagement in the online course: The online student engagement scale (OSE). Online Learning, 19(4), n4. https://files.eric.ed.gov/fulltext/EJ1079585.pdf
  • Gok, M. (2017). Predicting academic achievement with machine learning methods. Gazi University Journal of Science and Technology Part C: Design and Technology, 5(3), 139-148. https://dergipark.org.tr/en/download/article-file/341510
  • Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (2019, May). Can children understand machine learning concepts? the effect of uncovering black boxes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. England. https://doi.org/10.1145/3290605.3300645
  • Karasar, N. (2017). Preparing report in research. Nobel Publication Distribution.
  • Kayahan, S. (2018). Artificial intelligence in educational applications: A review. University of Trakya, 201, 20.
  • Kim, K., & Kwon, K. (2024). A systematic review of the evaluation in K-12 artificial intelligence education from 2013 to 2022. Interactive Learning Environments, 1-29. https://doi.org/10.1080/10494820.2024.2335499
  • Lane, D. (2018). Explaining artificial intelligence. Hello World, 4, 44-46.
  • Martin, F., Vahedian Movahed, S., Dimino, J., Farrell, A., Irankhah, E., Ghosh, S., ... & Narain, S. (2024). Perception, Trust, Attitudes, and Models: Introducing Children to AI and Machine Learning with Five Software Exhibits. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1740-1741). https://doi.org/10.1145/3626253.3635512
  • Mduma, N., Kalegele, K., & Machuve, D. (2019). A survey of machine learning approaches and techniques for student dropout prediction. Data Science Journal, 18, 1–10. https://doi.org/10.5334/dsj-2019-014
  • Murphy, R. F. (2019). Artificial intelligence applications to support k–12 teachers and teaching. RAND Corp., USA. https://www.rand.org/pubs/perspectives/PE315.html
  • Nabiyev, V. V. (2012). Artificial intelligence: human-computer interaction. Seckin Publishing.
  • Nafea, I. T. (2018). Machine learning in educational technology, H. Farhadi (Ed.), Machine learning - advanced techniques and emerging applications (s. 175-183). Intech Open. https://www.jstor.org/stable/pdf/resrep19907.pdf
  • Park, D., Ahn, J., Jang, J., Yu, W., Kim, W., Bae, Y., & Yoo, I. (2020). The development of software teaching-learning model based on machine learning platform. Journal of The Korean Association of Information Education, 24(1), 49-57. https://doi.org/10.14352/jkaie.2020.24.1.49
  • Peters, L. (2019). An educational programming environment for teaching the principles of machine learning using lego mindstorms [Unpublished doctoral dissertation]. University of Applied Sciences Leipzig.
  • Polat, E., Hopcan, S., & Arslantaş, T. K. (2022). Çevrimiçi öğrenci bağlılık ölçeğinin Türkçe’ye uyarlanması: geçerlik ve güvenirlik çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 12(1), 41-56.
  • Priya, S., Bhadra, S., Chimalakonda, S., & Venigalla, A. S. M. (2024). ML-Quest: a game for introducing machine learning concepts to K-12 students. Interactive Learning Environments, 32(1), 229-244. https://doi.org/10.1080/10494820.2022.2084115
  • Quiroz, P., & Gutierrez, F. J. (2024). Scratch-NB: A Scratch Extension for Introducing K-12 Learners to Supervised Machine Learning. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education, V. 1 (pp. 1077-1083). https://doi.org/10.1145/3626252.3630920
  • Reyes, A. A., Elkin, C., Niyaz, Q., Yang, X., Paheding, S., & Devabhaktuni, V. K. (2020, August). A preliminary work on visualization-based education tool for high school machine learning education. In 2020 IEEE Integrated STEM Education Conference (ISEC), IEEE. https://ieeexplore.ieee.org/abstract/document/9280629
  • Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., & Subsoontorn, P. (2018). Kids making AI: integrating machine learning, gamification, and social context in STEM education. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering. https://ieeexplore.ieee.org/abstract/document/8615249
  • Senocak, D. (2020). Artificial intelligence in open and distance learning environments: Its opportunities and the concerns raised. Journal of Open Educational Practices and Research, 6(3), 56-78. https://dergipark.org.tr/en/download/article-file/1191727
  • Tataj, X., & Kola, M. (2021). Education policies in line with the latest developments in the field of artificial intelligence: case of Albania. Human & Human, 8(27), 101-116. https://doi.org/10.29224/insanveinsan.818263
  • Tektas, M., Tektas, N., Onat, N., Gokmen, G., Kocyigit, G., & Akinci, T. Ç. (2010). Preparation of web based artificial intelligence techniques training simulators. Marmara University BAP Commission Presidency Project Report, 76-90. https://docplayer.biz.tr/631443-Web￾tabanli-yapay-zeka-tekn-kler-proje-no-fen-e-050608-138.html
  • Tseng, T., Davidson, M. J., Morales-Navarro, L., Chen, J. K., Delaney, V., Leibowitz, M., ... & Shapiro, R. B. (2024). Co-ML: Collaborative machine learning model building for developing dataset design practices. ACM Transactions on Computing Education, 24 (2), 1-37. https://doi.org/10.1145/3641552
  • Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research (JPEER), 1(2), 2. https://doi.org/10.5703/1288284314636
  • Yildiz, O. (2014). Evaluation of the performance of distance education students with machine learning. [Unpublished doctoral dissertation]. Istanbul University.
  • Zhu, K. (2019). An educational approach to machine learning with mobile applications [Unpublished doctoral dissertation]. Massachusetts Institute of Technology.
There are 34 citations in total.

Details

Primary Language English
Subjects Other Fields of Education (Other)
Journal Section Research Articles
Authors

Esma Nur Özen 0000-0003-3749-9442

Elif Polat 0000-0002-6086-9002

Yavuz Samur 0000-0003-4269-7099

Early Pub Date September 14, 2024
Publication Date
Submission Date March 25, 2024
Acceptance Date July 4, 2024
Published in Issue Year 2024 Volume: 5 Issue: 2

Cite

APA Özen, E. N., Polat, E., & Samur, Y. (2024). Can Machine Learning Be Taught to Pre-service Teachers in the STEM Fields?. Instructional Technology and Lifelong Learning, 5(2), 214-236. https://doi.org/10.52911/itall.1458322

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