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Eğitimde Yapay Zekâ ve Derin Öğrenme Alanında 2019-2023 Yıllar Arasında Yayınlanan Makalelerin Betimsel Analizi

Year 2024, Volume: 8 Issue: 2, 177 - 197, 30.09.2024
https://doi.org/10.31200/makuubd.1459260

Abstract

Günümüzde eğitimdeki teknolojik ilerlemeler, öğrenme süreçlerini dönüştürme potansiyeline sahiptir. Bu çalışmanın amacı, eğitimde yapay zekâ ve derin öğrenme uygulamalarını değerlendirerek, kullanım alanları, teknolojiler ve veri kaynaklarını incelemektir. Araştırmada eğitimde yapay zekâ ve derin öğrenme üzerine yapılan çalışmalar, sistematik olarak taranacak, ardından istatistiksel ile betimsel analiz yöntemleri kullanılarak değerlendirilecektir. Bu doğrultuda gerçekleştirilen çalışmada, 2019-2023 yılları arasında “Artificial Intelligence and Deep Learning” anahtar kelimesinin Web of Science’da yayınlanan SSCI veSCI-Expanded indekslerinde Eğitim/Eğitim araştırmaları alanında yayınlanan makaleler değerlendirilmiştir. Araştırma kapsamında tespit edilen 60 çalışma içerisinde; 3 makaleye erişim sağlanamamış, 2 makalenin de aynısı bulunduğu tespit edildiğinden 55 makale değerlendirmeye alınmıştır. Araştırmanın amacı doğrultusunda, incelenen makalelerin yılı, anahtar kelimeleri, dergi adları, araştırma yöntemleri ve türleri, veri toplama araçları, veri analiz yöntemleri, katılımcıların seviyesi ve sayısı gibi çeşitli faktörler açısından bir değerlendirme gerçekleştirilmiştir. Araştırma sonuçlarına göre, makalelerin çoğunluğunun 2023 yılında yayımlandığı, Çin’in en fazla çalışma yapılan ülke olduğu, eğitim araştırması alanında daha çok çalışmanın bulunduğu görülmüştür. Anahtar kelimeler arasında, Deep Learning, Artificial Intelligence ve Learning terimlerinin öne çıktığı belirlenmiş, “Education and Information Technologies” dergisinin bu konuda öne çıkan bir yayın kaynağı olduğu ortaya çıkmıştır. Araştırmalarda genellikle nicel araştırma yöntemleri tercih edilmiş, veri toplamak için ölçek ve test kullanılmış araştırma türü olarak deneysel-uygulamalı çalışmalar yapıldığı görülmüştür. Çalışmaların genellikle üniversite öğrencileriyle yapıldığı ve katılımcı sayısının 1-100 arasında olduğu tespit edilmiştir. Araştırma sonuçları, eğitimde yapay zekâ ve derin öğrenme kullanımının önemini vurgulamakta ve gelecekteki eğitim sistemlerinin bu teknolojik gelişmelerden nasıl yararlanabileceğini açıklamaktadır. Yapay zekâ ve derin öğrenme, öğrenme süreçlerini zenginleştirerek, öğrencilerin potansiyellerini daha etkili bir şekilde gerçekleştirmelerine olanak tanıyabilir.

References

  • Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. doi:10.1016/j.tele.2019.01.007
  • Akgün, E., & Ustun, A. B. (2023). Mobil artırılmış gerçeklikle öğrenmeye yönelik içerik analizi. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, (56), 362-383. https://doi.org/10.53444/deubefd.1153240
  • Bingöl, K., Akan, A. E., Örmecioğlu, H. T., & Er, A. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210. https://doi.org/10.17341/gazimmfd.647981
  • Brooks, C., & Thompson, C. (2017). Predictive modelling in teaching and learning. Handbook of Learning Analytics, 61-68.
  • Chen, X., Zou, D., Cheng, G., & Xie, H. (2021). Artificial intelligence-assisted personalized language learning: Systematic review and co-citation analysis. In 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 241-245). doi:10.1109/icalt52272.2021.00079
  • Chen, D. C., You, C. S., & Su, M. S. (2022). Development of professional competencies for artificial intelligence in finite element analysis. Interactive Learning Environments, 30(7), 1265-1272. doi:10.1080/10494820.2020.1719162
  • Chiu, M. C., Hwang, G. J., Hsia, L. H., & Shyu, F. M. (2024). Artificial intelligence-supported art education: A deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, 1-19. https://doi.org/10.1080/10494820.2022.2100426
  • Doleck, T., Jarrell, A., Poitras, E. G., Chaouachi, M., & Lajoie, S. P. (2016). A tale of three cases: Examining accuracy, efficiency, and process differences in diagnosing virtual patient cases. Australasian Journal of Educational Technology, 32(5). https://doi.org/10.14742/ajet.2759
  • Goel, A. (2017). AI dducation for the world. AI Magazine, 38(2), 3-4. doi:10.1609/aimag.v38i2.2740
  • Gong, D., Yang, H. H., Wu, D., & Dai, J. (2023). Relationships between teaching presence, connected classroom climate, and deep learning within the rotational synchronous teaching model. Education and Information Technologies, 28(2), 1715-1733.
  • He, J., Ma, T., & Zhang, Y. (2023). Design of blended learning mode and practice community using intelligent cloud teaching. Education and Information Technologies, 28(8), 10593-10615.
  • Humphry, T., & Fuller, A. L. (2023). Potential ChatGPT use in undergraduate chemistry laboratories. Journal of Chemical Education, 100(4), 1434-1436.
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial intelligence, 1, 100001. doi:10.1016/j.caeai.2020.100001
  • Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013-2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94-117.
  • Kahn, K., & Winters, N. (2021). Constructionism and AI: A history and possible futures. British Journal of Educational Technology, 52(3), 1130-1142. doi:10.1111/bjet.13088
  • Kovač, V. B., Nome, D. Ø., Jensen, A. R., & Skreland, L. L. (2023). The why, what and how of deep learning: critical analysis and additional concerns. Education Inquiry, 1-17. https://doi.org/10.1080/20004508.2023.2194502
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • Lottridge, S., Woolf, S., Young, M., Jafari, A., & Ormerod, C. (2023). The use of annotations to explain labels: Comparing results from a human‐rater approach to a deep learning approach. Journal of Computer Assisted Learning, 39(3), 787-803. https://doi.org/10.1111/jcal.12784
  • Lu, P., Chen, S., & Zheng, Y. (2012). Artificial intelligence in civil engineering. Mathematical Problems in Engineering, 2012(1), 145974. doi:10.1155/2012/145974
  • Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of management analytics, 6(1), 1-29. doi:10.1080/23270012.2019.1570365
  • Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning (Online), 15(1), 188. doi:10.3991/ijet.v15i01.11435
  • Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2). doi:10.7189/jogh.08.020303
  • Prieto, L., Sharma, K., Kidziński, Ł., Rodríguez-Triana, M., & Dillenbourg, P. (2018). Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data. Journal of Computer Assisted Learning, 34(2), 193-203. doi:10.1111/jcal.12232
  • Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28(7), 8299-8333. Quinn, J., McEachen, J., Fullan, M., Gardner, M., & Drummy, M. (2019). Dive into deep learning: Tools for engagement. Corwin press.
  • Tsingos, C., Bosnic-Anticevich, S., & Smith, L. (2015). Learning styles and approaches: Can reflective strategies encourage deep learning? Currents in Pharmacy Teaching and Learning, 7(4), 492-504. doi:10.1016/j.cptl.2015.04.006
  • Watson, C., Wilson, A., Drew, V., & Thompson, T. L. (2016). Small data, online learning and assessment practices in higher education: a case study of failure? Assessment & Evaluation in Higher Education, 42(7), 1030-1045.doi:10.1080/02602938.2016.1223834
  • Xu, W., & Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195-4223.
  • Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. doi:10.1016/j.caeai.2021.100008
  • Yong, B., Jiang, X., Lin, J., Sun, G., & Zhou, Q. (2022). Online practical deep learning education: Using collective intelligence from a resource sharing perspective. Educational Technology & Society, 25(1), 193-204. https://www.jstor.org/stable/48647040
  • Zhang, C. (2019). Research on the fluctuation and factors of China TFP of IT industry. Journal of Industrial Integration and Management, 4(04), 1950013. doi:10.1142/s2424862219500131

Descriptive Analysis of Articles Published Between 2019 and 2023 in the Field of Artificial Intelligence and Deep Learning

Year 2024, Volume: 8 Issue: 2, 177 - 197, 30.09.2024
https://doi.org/10.31200/makuubd.1459260

Abstract

Technological advances in education have the potential to significantly transform learning processes. This study aims to evaluate the current state of artificial intelligence (AI) and deep learning applications in education, focusing on their areas of application, the technologies used, and the data sources available. The research will systematically review studies on AI and deep learning in education, using statistical and descriptive analysis methods. It will evaluate articles published between 2019 and 2023 in the SSCI and SCI-Expanded indexes of Web of Science, using the keyword "Artificial Intelligence and Deep Learning" in the field of Education Research. Out of 60 identified studies, 3 articles could not be accessed, and 2 duplicates were found, eaving leaving 55 articles for evaluation. The analysis covers several factors, including year of publication, keywords, journal names, research methods, data collection tools, and evel and number of participants. Results indicate that most articles were published in 2023, with China being the most active country in the field. Keywords such as Deep Learning, Artificial Intelligence, and Learning emerged prominently, with "Education and Information Technologies" identified as a significant publication source. Quantitative research methods were predominant, with scales and used to collect data, and experimental and applied studies were the most common. Most of the studies were conducted with university students, with the number of participants ranging from 1 to 100. The findings highlight the importance of AI and deep learning in education, and how future education systems can use these advances to enrich the learning process and help students realize their potential more effectively

References

  • Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. doi:10.1016/j.tele.2019.01.007
  • Akgün, E., & Ustun, A. B. (2023). Mobil artırılmış gerçeklikle öğrenmeye yönelik içerik analizi. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, (56), 362-383. https://doi.org/10.53444/deubefd.1153240
  • Bingöl, K., Akan, A. E., Örmecioğlu, H. T., & Er, A. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210. https://doi.org/10.17341/gazimmfd.647981
  • Brooks, C., & Thompson, C. (2017). Predictive modelling in teaching and learning. Handbook of Learning Analytics, 61-68.
  • Chen, X., Zou, D., Cheng, G., & Xie, H. (2021). Artificial intelligence-assisted personalized language learning: Systematic review and co-citation analysis. In 2021 International Conference on Advanced Learning Technologies (ICALT) (pp. 241-245). doi:10.1109/icalt52272.2021.00079
  • Chen, D. C., You, C. S., & Su, M. S. (2022). Development of professional competencies for artificial intelligence in finite element analysis. Interactive Learning Environments, 30(7), 1265-1272. doi:10.1080/10494820.2020.1719162
  • Chiu, M. C., Hwang, G. J., Hsia, L. H., & Shyu, F. M. (2024). Artificial intelligence-supported art education: A deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, 1-19. https://doi.org/10.1080/10494820.2022.2100426
  • Doleck, T., Jarrell, A., Poitras, E. G., Chaouachi, M., & Lajoie, S. P. (2016). A tale of three cases: Examining accuracy, efficiency, and process differences in diagnosing virtual patient cases. Australasian Journal of Educational Technology, 32(5). https://doi.org/10.14742/ajet.2759
  • Goel, A. (2017). AI dducation for the world. AI Magazine, 38(2), 3-4. doi:10.1609/aimag.v38i2.2740
  • Gong, D., Yang, H. H., Wu, D., & Dai, J. (2023). Relationships between teaching presence, connected classroom climate, and deep learning within the rotational synchronous teaching model. Education and Information Technologies, 28(2), 1715-1733.
  • He, J., Ma, T., & Zhang, Y. (2023). Design of blended learning mode and practice community using intelligent cloud teaching. Education and Information Technologies, 28(8), 10593-10615.
  • Humphry, T., & Fuller, A. L. (2023). Potential ChatGPT use in undergraduate chemistry laboratories. Journal of Chemical Education, 100(4), 1434-1436.
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial intelligence, 1, 100001. doi:10.1016/j.caeai.2020.100001
  • Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013-2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94-117.
  • Kahn, K., & Winters, N. (2021). Constructionism and AI: A history and possible futures. British Journal of Educational Technology, 52(3), 1130-1142. doi:10.1111/bjet.13088
  • Kovač, V. B., Nome, D. Ø., Jensen, A. R., & Skreland, L. L. (2023). The why, what and how of deep learning: critical analysis and additional concerns. Education Inquiry, 1-17. https://doi.org/10.1080/20004508.2023.2194502
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • Lottridge, S., Woolf, S., Young, M., Jafari, A., & Ormerod, C. (2023). The use of annotations to explain labels: Comparing results from a human‐rater approach to a deep learning approach. Journal of Computer Assisted Learning, 39(3), 787-803. https://doi.org/10.1111/jcal.12784
  • Lu, P., Chen, S., & Zheng, Y. (2012). Artificial intelligence in civil engineering. Mathematical Problems in Engineering, 2012(1), 145974. doi:10.1155/2012/145974
  • Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of management analytics, 6(1), 1-29. doi:10.1080/23270012.2019.1570365
  • Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning (Online), 15(1), 188. doi:10.3991/ijet.v15i01.11435
  • Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2). doi:10.7189/jogh.08.020303
  • Prieto, L., Sharma, K., Kidziński, Ł., Rodríguez-Triana, M., & Dillenbourg, P. (2018). Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data. Journal of Computer Assisted Learning, 34(2), 193-203. doi:10.1111/jcal.12232
  • Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28(7), 8299-8333. Quinn, J., McEachen, J., Fullan, M., Gardner, M., & Drummy, M. (2019). Dive into deep learning: Tools for engagement. Corwin press.
  • Tsingos, C., Bosnic-Anticevich, S., & Smith, L. (2015). Learning styles and approaches: Can reflective strategies encourage deep learning? Currents in Pharmacy Teaching and Learning, 7(4), 492-504. doi:10.1016/j.cptl.2015.04.006
  • Watson, C., Wilson, A., Drew, V., & Thompson, T. L. (2016). Small data, online learning and assessment practices in higher education: a case study of failure? Assessment & Evaluation in Higher Education, 42(7), 1030-1045.doi:10.1080/02602938.2016.1223834
  • Xu, W., & Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195-4223.
  • Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. doi:10.1016/j.caeai.2021.100008
  • Yong, B., Jiang, X., Lin, J., Sun, G., & Zhou, Q. (2022). Online practical deep learning education: Using collective intelligence from a resource sharing perspective. Educational Technology & Society, 25(1), 193-204. https://www.jstor.org/stable/48647040
  • Zhang, C. (2019). Research on the fluctuation and factors of China TFP of IT industry. Journal of Industrial Integration and Management, 4(04), 1950013. doi:10.1142/s2424862219500131
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Big Data
Journal Section Articles
Authors

Gülhan Ünsal 0009-0005-0652-1733

Fatma Gizem Karaoğlan Yılmaz 0000-0003-4963-8083

Early Pub Date October 1, 2024
Publication Date September 30, 2024
Submission Date March 27, 2024
Acceptance Date September 30, 2024
Published in Issue Year 2024 Volume: 8 Issue: 2

Cite

APA Ünsal, G., & Karaoğlan Yılmaz, F. G. (2024). Eğitimde Yapay Zekâ ve Derin Öğrenme Alanında 2019-2023 Yıllar Arasında Yayınlanan Makalelerin Betimsel Analizi. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 8(2), 177-197. https://doi.org/10.31200/makuubd.1459260


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