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Artificial Intelligence: Its Role and Potential in Education

Yıl 2024, , 483 - 497, 31.03.2024
https://doi.org/10.15869/itobiad.1331201

Öz

Artificial intelligence (AI), which has attracted great attention in recent years, has been widely used in the field of education as in many other fields. AI in education is used to improve student learning, support teachers and provide a more personalized educational experience. AI plays an important role with adaptive learning systems in improving students' learning processes. These systems assess students' individual needs and provide them with appropriate learning materials. AI also monitors students' performance, identifies their weaknesses, and provides additional support in these areas. Thus, students are enabled to learn more effectively and to reveal their full potential. By supporting teachers, AI facilitates classroom management and helps teachers use their time more efficiently. Automated assessment systems allow teachers to quickly assess assignments and exams, while improving the process of providing feedback. In addition, AI also helps teachers understand students' interests and learning styles, so that more personalized instruction can be offered. Another important use of AI in education is student counseling. AI-based counseling systems can guide students in matters such as career choices, university applications, and academic planning. These systems can provide students with viable career options, support the application process, and help them identify their future goals. As a result, the use of AI in education has great potential to improve student learning processes, provide support to teachers and provide a more personalized educational experience. In this study; The subject of AI was examined in a general framework under the title of education and the role of AI in education was discussed. It is thought that AI will contribute to the field by revealing the teacher and how it can be used in the field of education.

Kaynakça

  • Akyürek, H.A. (2013). Intelligent workforce management by using artificial intelligence techniques. Master Thesis. Mevlana University, Institute of Science and Technology, Konya, Turkey.
  • Atasoy, S. (2012). Performance management modelling with artifical neural network and fuzzy neural network in human resources. Master Thesis, Yıldız Teknik University, Institute of Science and Technology, İstanbul, Turkey.
  • Badal, Y.T. & Sungkur, R.K. (2023). Predictive modelling and analytics of students’ grades using machine learning algorithms. Educ Inf Technol 28, 3027–3057 (2023). https://doi.org/10.1007/s10639-022-11299-8
  • Bahçeci, F. (2015). Öğrenme yönetim sistemlerinde kullanılan öğrenme analitikleri araçlarının incelenmesi. Turkish Journal of Educational Studies, 2(1), 41–58.
  • Baker, R. S. J. & Yacef, K. (2009). The state of educational data mining in 2009: a review and future visions. Journal of Educational Data Mining, 1(1), 3-17. doi.org/10.5281/zenodo.3554657
  • Baker,T.,Smıth,L. & Anissa,N.(2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Access address: https://www.nesta.org.uk/report/education-rebooted/.
  • Bozkır, A. S., Sezer, E. & Gök, B. (2009). Öğrenci Seçme Sınavında (ÖSS) öğrenci başarımını etkileyen faktörlerin veri madenciliği yöntemleriyle tespiti. 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09), 13-15 Mayıs, Karabük University, Karabük, 37-43. Access address: https://www.researchgate.net/publication/237693243_Ogrenci_Secme_Sinavinda_OSS_Ogrenci_Basarimini_Etkileyen_Faktorlerin_Veri_Madenciligi_Yontemleriyle_Tespiti
  • Chen, Y. & Zhai, L. (2023). A comparative study on student performance prediction using machine learning. Educ Inf Technol 28, 12039–12057. https://doi.org/10.1007/s10639-023-11672-1.
  • Chung, J. Y. & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346-353. doi.org/10.1016/j.childyouth.2018.11.030
  • Çakıt, E. & Dağdeviren, M. (2022). Predicting the percentage of student placement: A comparative study of machine learning algorithms. Education and Information Technologies, 27(1), 997-1022. doi.org/10.1007/s10639-021-10655-4
  • Dekker, G. W., Pechenizkiy, M. & Vleeshouwers. J.M. (2009). Predicting Students Drop Out: A Case Study. EDM’09 - Educ. Data Min. 2009 2nd Int. Conf. Educ. Data Min. 41-50. doi:10.1037/0893-3200.21.3.344.
  • Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498-506. doi.org/10.1016/j.dss.2010.06.003
  • Demir, O. (2019). Sürdürülebilir kalkınma için yapay zekâ. G. Telli (Ed.), Yapay zekâ ve gelecek, (ss. 44-63). İstanbul: Doğu Kitapevi.
  • Djulovic, A. & Li, D. (2013). Towards freshman retention prediction: A comparative study. International Journal of Information and Education Technology, 3(5), 494-500. Access address: http://www.ijiet.org/papers/324-K045.pdf
  • Erdoğan, Ş. & Timor, M. (2005). A data mining application in a student database. Havacılık ve Uzay Teknolojileri Dergisi, 2(2), 53 - 57. Access address: https://jast.hho.msu.edu.tr/index.php/JAST/article/view/132
  • Fahimirad, M. & Kotamjani, S. S. (2018). A review on application of artificial intelligence in teaching and learning in educational contexts. International Journal of Learning and Development, 8(4), 106-118. doi:10.5296/ijld.v8i4.14057
  • Fırat, M. & Yüzer, T. V. (2016). Learning analytics: assessment of mass data in distance education. International Journal on New Trends in Education and Their Implications, 7(2), 1-8. Access address: http://www.ijonte.org/FileUpload/ks63207/File/01.mehmet_firat_.pdf
  • Greller, W. & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42-57. Access address: https://www.researchgate.net/publication/234057371_Translating_Learning_into_Numbers_A_Generic_Framework_for_Learning_Analytics
  • Guleria, P. & Sood, M. (2023). Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Education and Information Technologies, 28(1), 1081-1116. doi.org/10.1007/s10639-022-11221-2
  • Iam-On, N. & Boongoen, T. (2017). Generating descriptive model for student dropout: A review of clustering approach. Human-centric Computing and Information Sciences, 7(1), 1-24. doi.org/10.1186/s13673-016-0083-0
  • Iatrellis, O., Savvas, I. K., Fitsilis, P. & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26, 69-88. doi.org/10.1007/s10639-020-10260-x
  • Ibrahim, Z. & Rusli, D. (2007). Predicting students’ academic performance: Comparing artificial neural network, decision tree and linear regression. 21st Annual SAS Malaysia Forum, 5th September. Access address:
  • https://www.researchgate.net/publication/228894873_Predicting_Students'_Academic_Performance_Comparing_Artificial_Neural_Network_Decision_Tree_and_Linear_Regression Johnson, L., Smith, R., Willis, H., Levine, A. & Haywood, K. (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium.
  • Karabatak, M. (2008). Association rule extraction for feature selection, classification and prediction applications and software development. (PhD Thesis). Fırat University, Elazığ,Turkey.
  • Karaca, B. & Telli, G. (2019). Yapay zekânın çeşitli süreçlerdeki rolü ve tahminleme fonksiyonu. G. Telli (Ed.), Yapay zekâ ve gelecek (172-185). İstanbul: Doğu Kitapevi.
  • Kay, J. (2015). Whither or wither the AI of AIED?. In AIED Workshops. Access address: https://www.researchgate.net/publication/283824441_Whither_or_wither_the_AI_of_AIED
  • Kharbat, F. F., Alshawabkeh, A. & Woolsey, M. L. (2021). Identifying Gaps in Using Artificial Intelligence to Support Students with İntellectual Disabilities From Education And Health Perspectives. Aslib Journal of Information Management, 73(1), 101-128. doi.org/10.1108/AJIM-02-2020-0054
  • Kocayiğit, A. & Uşun, S. (2020). Milli Eğitim Bakanlığına bağlı okullarda görev yapan öğretmenlerin uzaktan eğitime yönelik tutumları. AVRASYA Uluslararası Araştırmalar Dergisi, 8(23), 285–299.
  • Kuprenko, V. (2020). Artificial intelligence in education: benefits, challenges, and use cases. Access address: https://medium.com/towards-artificial-intelligence/artificial-intelligence-in-education-benefitschallenges-and-use-cases-db52d8921f7a
  • Long, P. & Siemens, G. (2014). Penetrating the fog: analytics in learning and education. Italian Journal of Educational Technology, 22(3), 132-137. Access address: http://www.learntechlib.org/p/183382/
  • Mishra, T., Kumar, D. & Gupta, S. (2014). Mining students’ data for prediction performance. 2014 Fourth International Conference on Advanced Computing & Communication Technologies, 255-262. Doı: 10.1109/acct.2014.105
  • Obschonka, M. & Audretsch, D. B. (2020). Artificial intelligence and big data entrepreneurship: a new era has begun. Small Business Economics, 55, 529-539. doi:10.1007/s11187-019-00202-4
  • Osetskyi, V., Vitrenko, A., Tatomyr, I., Bilan, S. & Hirnyk, Y. (2020). Artificial intelligence application in education: Financial implications and prospects. Financial and credit activity problems of theory and practice, 2(33), 574-584. doi.org/10.18371/fcaptp.v2i33.207246
  • Öztemel, E. (2003). Yapay sinir ağları. İstanbul: PapatyaYayıncılık.
  • Pehlivan, B. (2018). Yapay zekânın eğitimdeki 10 kullanım alanı. Access address: http://www.socialbusinesstr.com/2018/03/15/yapay-zekanin-egitimdeki-10-kullanim-alani/].
  • Polat, A. (2021). Examining dropout and graduation status of open high school students using educational data mining. (PhD Thesis), Sakarya University, Institute of Education Sciences, Sakarya, Turkey.
  • Romero, C., Ventura, S. & Pechenizkiy, M. (2013). Handbook of Educational Data Mining. 526.
  • Sara, N.-B., Halland, R., Igel, C. & Alstrup, S. (2015). High-school dropout prediction using machine learning: A Danish large-scale study. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence, 319-324.
  • Sağdıç, Z. A. & Sunagül, S. B. (2020). Otizm spektrum bozukluğu ve yapay zekâ uygulamaları. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 6(3), 92-111. Access address: https://dergipark.org.tr/tr/pub/auad/issue/56247/768540
  • Schatzel, K., Callahan, T., Scott, C. J. & Davis, T. (2011). Reaching the non-traditional stopout population: A segmentation approach. Journal of Marketing for Higher Education, 21(1), 47-60. doi.org/10.1080/08841241.2011.569590
  • Shabbir, J. & Anwer, T. (2018). Artificial intelligence and its role in near future. Cornell University, 1.
  • Sivakumar, S., Venkataraman, S. & Selvaraj, R. (2016). Predictive modeling of student dropout indicators in educational data mining using improved decision tree. Indian Journal of Science and Technology, 9(4), 1-5. doı: 10.17485/ijst/2016/v9i4/87032
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Yapay Zekâ: Eğitimdeki Rolü ve Potansiyeli

Yıl 2024, , 483 - 497, 31.03.2024
https://doi.org/10.15869/itobiad.1331201

Öz

Son yıllarda büyük ilgi gören yapay zekâ (YZ) pek çok alanda olduğu gibi eğitim alanında da yaygın olarak kullanılmaya başlanmıştır. Eğitimde YZ, öğrencilerin öğrenme süreçlerini iyileştirmek, öğretmenlere destek sağlamak ve daha kişiselleştirilmiş bir eğitim deneyimi sunmak amacıyla kullanılmaktadır. Öğrencilerin öğrenme süreçlerini geliştirmede YZ, adaptif öğrenme sistemleriyle önemli bir rol oynamaktadır. Bu sistemler, öğrencilerin bireysel ihtiyaçlarını değerlendirerek onlara uygun öğrenme materyalleri sunar. YZ aynı zamanda öğrencilerin performansını izleyerek, zayıf yönlerini belirlemekte ve bu alanlarda ek destek sağlamaktadır. Böylece, öğrencilerin daha etkili bir şekilde öğrenmeleri ve potansiyellerini tam anlamıyla ortaya çıkarmaları sağlanmaktadır. Öğretmenlere destek olarak, YZ öğretmenlerin sınıf yönetimini kolaylaştırmakta ve zamanlarını daha verimli kullanmalarına yardımcı olmaktadır. Otomatik değerlendirme sistemleri, öğretmenlerin ödevleri ve sınavları hızlı bir şekilde değerlendirmelerine olanak tanırken, geri bildirim sağlama sürecini de iyileştirir. Ayrıca, YZ, öğretmenlere öğrencilerin ilgi alanlarını ve öğrenme stillerini anlama konusunda da yardımcı olmakta ve böylece daha kişiselleştirilmiş bir öğretim sunulabilmektedir. YZ'nin eğitimdeki bir başka önemli kullanım alanı ise öğrenci danışmanlığıdır. YZ tabanlı danışmanlık sistemleri, öğrencilere kariyer seçimleri, üniversite başvuruları ve akademik planlama gibi konularda rehberlik edebilir. Bu sistemler, öğrencilere uygun kariyer seçenekleri sunabilir, başvuru sürecinde destek sağlayabilir ve gelecekteki hedeflerini belirlemelerine yardımcı olabilir. Sonuç olarak, eğitimde yapay zekâ kullanımı, öğrencilerin öğrenme süreçlerini geliştirmek, öğretmenlere destek sağlamak ve daha kişiselleştirilmiş bir eğitim deneyimi sunmak için büyük bir potansiyele sahiptir. Bu çalışmada; YZ konusu eğitim başlığı altında genel bir çerçevede incelenmiş ve YZ’nin eğitimdeki rolü tartışılmıştır. Eğitim alanında YZ’nin öğretmene ve nasıl kullanabileceğini ortaya koymasıyla alana katkı sağlayacağı düşünülmektedir.

Kaynakça

  • Akyürek, H.A. (2013). Intelligent workforce management by using artificial intelligence techniques. Master Thesis. Mevlana University, Institute of Science and Technology, Konya, Turkey.
  • Atasoy, S. (2012). Performance management modelling with artifical neural network and fuzzy neural network in human resources. Master Thesis, Yıldız Teknik University, Institute of Science and Technology, İstanbul, Turkey.
  • Badal, Y.T. & Sungkur, R.K. (2023). Predictive modelling and analytics of students’ grades using machine learning algorithms. Educ Inf Technol 28, 3027–3057 (2023). https://doi.org/10.1007/s10639-022-11299-8
  • Bahçeci, F. (2015). Öğrenme yönetim sistemlerinde kullanılan öğrenme analitikleri araçlarının incelenmesi. Turkish Journal of Educational Studies, 2(1), 41–58.
  • Baker, R. S. J. & Yacef, K. (2009). The state of educational data mining in 2009: a review and future visions. Journal of Educational Data Mining, 1(1), 3-17. doi.org/10.5281/zenodo.3554657
  • Baker,T.,Smıth,L. & Anissa,N.(2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Access address: https://www.nesta.org.uk/report/education-rebooted/.
  • Bozkır, A. S., Sezer, E. & Gök, B. (2009). Öğrenci Seçme Sınavında (ÖSS) öğrenci başarımını etkileyen faktörlerin veri madenciliği yöntemleriyle tespiti. 5. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09), 13-15 Mayıs, Karabük University, Karabük, 37-43. Access address: https://www.researchgate.net/publication/237693243_Ogrenci_Secme_Sinavinda_OSS_Ogrenci_Basarimini_Etkileyen_Faktorlerin_Veri_Madenciligi_Yontemleriyle_Tespiti
  • Chen, Y. & Zhai, L. (2023). A comparative study on student performance prediction using machine learning. Educ Inf Technol 28, 12039–12057. https://doi.org/10.1007/s10639-023-11672-1.
  • Chung, J. Y. & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346-353. doi.org/10.1016/j.childyouth.2018.11.030
  • Çakıt, E. & Dağdeviren, M. (2022). Predicting the percentage of student placement: A comparative study of machine learning algorithms. Education and Information Technologies, 27(1), 997-1022. doi.org/10.1007/s10639-021-10655-4
  • Dekker, G. W., Pechenizkiy, M. & Vleeshouwers. J.M. (2009). Predicting Students Drop Out: A Case Study. EDM’09 - Educ. Data Min. 2009 2nd Int. Conf. Educ. Data Min. 41-50. doi:10.1037/0893-3200.21.3.344.
  • Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498-506. doi.org/10.1016/j.dss.2010.06.003
  • Demir, O. (2019). Sürdürülebilir kalkınma için yapay zekâ. G. Telli (Ed.), Yapay zekâ ve gelecek, (ss. 44-63). İstanbul: Doğu Kitapevi.
  • Djulovic, A. & Li, D. (2013). Towards freshman retention prediction: A comparative study. International Journal of Information and Education Technology, 3(5), 494-500. Access address: http://www.ijiet.org/papers/324-K045.pdf
  • Erdoğan, Ş. & Timor, M. (2005). A data mining application in a student database. Havacılık ve Uzay Teknolojileri Dergisi, 2(2), 53 - 57. Access address: https://jast.hho.msu.edu.tr/index.php/JAST/article/view/132
  • Fahimirad, M. & Kotamjani, S. S. (2018). A review on application of artificial intelligence in teaching and learning in educational contexts. International Journal of Learning and Development, 8(4), 106-118. doi:10.5296/ijld.v8i4.14057
  • Fırat, M. & Yüzer, T. V. (2016). Learning analytics: assessment of mass data in distance education. International Journal on New Trends in Education and Their Implications, 7(2), 1-8. Access address: http://www.ijonte.org/FileUpload/ks63207/File/01.mehmet_firat_.pdf
  • Greller, W. & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42-57. Access address: https://www.researchgate.net/publication/234057371_Translating_Learning_into_Numbers_A_Generic_Framework_for_Learning_Analytics
  • Guleria, P. & Sood, M. (2023). Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Education and Information Technologies, 28(1), 1081-1116. doi.org/10.1007/s10639-022-11221-2
  • Iam-On, N. & Boongoen, T. (2017). Generating descriptive model for student dropout: A review of clustering approach. Human-centric Computing and Information Sciences, 7(1), 1-24. doi.org/10.1186/s13673-016-0083-0
  • Iatrellis, O., Savvas, I. K., Fitsilis, P. & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26, 69-88. doi.org/10.1007/s10639-020-10260-x
  • Ibrahim, Z. & Rusli, D. (2007). Predicting students’ academic performance: Comparing artificial neural network, decision tree and linear regression. 21st Annual SAS Malaysia Forum, 5th September. Access address:
  • https://www.researchgate.net/publication/228894873_Predicting_Students'_Academic_Performance_Comparing_Artificial_Neural_Network_Decision_Tree_and_Linear_Regression Johnson, L., Smith, R., Willis, H., Levine, A. & Haywood, K. (2011). The 2011 Horizon Report. Austin, Texas: The New Media Consortium.
  • Karabatak, M. (2008). Association rule extraction for feature selection, classification and prediction applications and software development. (PhD Thesis). Fırat University, Elazığ,Turkey.
  • Karaca, B. & Telli, G. (2019). Yapay zekânın çeşitli süreçlerdeki rolü ve tahminleme fonksiyonu. G. Telli (Ed.), Yapay zekâ ve gelecek (172-185). İstanbul: Doğu Kitapevi.
  • Kay, J. (2015). Whither or wither the AI of AIED?. In AIED Workshops. Access address: https://www.researchgate.net/publication/283824441_Whither_or_wither_the_AI_of_AIED
  • Kharbat, F. F., Alshawabkeh, A. & Woolsey, M. L. (2021). Identifying Gaps in Using Artificial Intelligence to Support Students with İntellectual Disabilities From Education And Health Perspectives. Aslib Journal of Information Management, 73(1), 101-128. doi.org/10.1108/AJIM-02-2020-0054
  • Kocayiğit, A. & Uşun, S. (2020). Milli Eğitim Bakanlığına bağlı okullarda görev yapan öğretmenlerin uzaktan eğitime yönelik tutumları. AVRASYA Uluslararası Araştırmalar Dergisi, 8(23), 285–299.
  • Kuprenko, V. (2020). Artificial intelligence in education: benefits, challenges, and use cases. Access address: https://medium.com/towards-artificial-intelligence/artificial-intelligence-in-education-benefitschallenges-and-use-cases-db52d8921f7a
  • Long, P. & Siemens, G. (2014). Penetrating the fog: analytics in learning and education. Italian Journal of Educational Technology, 22(3), 132-137. Access address: http://www.learntechlib.org/p/183382/
  • Mishra, T., Kumar, D. & Gupta, S. (2014). Mining students’ data for prediction performance. 2014 Fourth International Conference on Advanced Computing & Communication Technologies, 255-262. Doı: 10.1109/acct.2014.105
  • Obschonka, M. & Audretsch, D. B. (2020). Artificial intelligence and big data entrepreneurship: a new era has begun. Small Business Economics, 55, 529-539. doi:10.1007/s11187-019-00202-4
  • Osetskyi, V., Vitrenko, A., Tatomyr, I., Bilan, S. & Hirnyk, Y. (2020). Artificial intelligence application in education: Financial implications and prospects. Financial and credit activity problems of theory and practice, 2(33), 574-584. doi.org/10.18371/fcaptp.v2i33.207246
  • Öztemel, E. (2003). Yapay sinir ağları. İstanbul: PapatyaYayıncılık.
  • Pehlivan, B. (2018). Yapay zekânın eğitimdeki 10 kullanım alanı. Access address: http://www.socialbusinesstr.com/2018/03/15/yapay-zekanin-egitimdeki-10-kullanim-alani/].
  • Polat, A. (2021). Examining dropout and graduation status of open high school students using educational data mining. (PhD Thesis), Sakarya University, Institute of Education Sciences, Sakarya, Turkey.
  • Romero, C., Ventura, S. & Pechenizkiy, M. (2013). Handbook of Educational Data Mining. 526.
  • Sara, N.-B., Halland, R., Igel, C. & Alstrup, S. (2015). High-school dropout prediction using machine learning: A Danish large-scale study. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence, 319-324.
  • Sağdıç, Z. A. & Sunagül, S. B. (2020). Otizm spektrum bozukluğu ve yapay zekâ uygulamaları. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 6(3), 92-111. Access address: https://dergipark.org.tr/tr/pub/auad/issue/56247/768540
  • Schatzel, K., Callahan, T., Scott, C. J. & Davis, T. (2011). Reaching the non-traditional stopout population: A segmentation approach. Journal of Marketing for Higher Education, 21(1), 47-60. doi.org/10.1080/08841241.2011.569590
  • Shabbir, J. & Anwer, T. (2018). Artificial intelligence and its role in near future. Cornell University, 1.
  • Sivakumar, S., Venkataraman, S. & Selvaraj, R. (2016). Predictive modeling of student dropout indicators in educational data mining using improved decision tree. Indian Journal of Science and Technology, 9(4), 1-5. doı: 10.17485/ijst/2016/v9i4/87032
  • Somyürek, S., Güyer, T., Atasoy, B. & Ünal, M. (2021). E-Öğrenme Ortamları ve Öğrenme Analitikleri. 14(3).
  • Şen, B., Uçar, E. & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468-9476. doi.org/10.1016/j.eswa.2012.02.112
  • Tuğluk, M. N. & Gök-Çolak, F. (2019). Sanayi toplumu ve eğitimi. A. D. Öğretir Özçelik ve M. N. Tuğluk (Ed.), Eğitimde ve endüstride 21. yüzyıl becerileri (ss. 305-335). Ankara: Pegem Akademi.
  • Turing, AM. (1950). Computing Machinery And Intelligence. Mind, LIX No. 236, 433. Wong, T. Y. & Bressler, N. M. (2016). Artificial ıntelligence with deep learning technology looks into diabetic retinopathy screening. Jama, 316(22), 2366-2367. doi:10.1001/jama.2016.17563
  • UNESCO (2017). Data Revolution To Measure Equity in Education For Sdgs. Access address: http://www.iiep.unesco.org/en/data-revolution-measure-equity-educationsdgs-cies2017-3886
  • Meskens, N., Vandamme, J. P. & Superby Aguirre, J. F. (2008). Predicting academic performance by data mining methods. Access address: http://hdl.handle.net/2078/20837
  • Yakupoğlu, Y. (2018). Educational data mining and an application. Master Thesis. İstanbul Teknik University, İstanbul, Turkey.
  • Yılmaz, İ. (2012). An application artificial intelligent for permanent staff calculate, Master Thesis. Gazi University, Institute of Science and Technology, Ankara, Turkey.
  • Yolcu, H. & Kurt, M. (2021). Uzaktan eğitim sürecinde EBA canlı derslerle ilgili öğretmenlerin görüşleri. EKEV Akademisi Dergisi, 25(87), 241–262.
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yeni İletişim Teknolojileri
Bölüm Makaleler
Yazarlar

Ayşe Alkan 0000-0002-9125-1408

Erken Görünüm Tarihi 30 Mart 2024
Yayımlanma Tarihi 31 Mart 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Alkan, A. (2024). Artificial Intelligence: Its Role and Potential in Education. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 13(1), 483-497. https://doi.org/10.15869/itobiad.1331201
İnsan ve Toplum Bilimleri Araştırmaları Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.