Yıl 2025,
Cilt: 14 Sayı: 3, 1253 - 1274, 30.09.2025
Burcu Alan
,
Fikriye Kırbağ Zengin
Kaynakça
-
Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding E-learning during COVID-19 at a private medical college. Pakistan Journal of Medical Sciences, 36(COVID19-S4), 57-61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766
-
Ahmed, R. K. A. (2016). Artificial neural networks in E-learning personalization: A review. International Journal of Intelligent Information Systems, 5(6), 104-108. https://doi.org/10.11648/j.ijiis.20160506.14
-
Akkuzu, N., & Akçay, H. (2011). The design of a learning environment based on the theory of multiple intelligence and the study its effectiveness on the achievements, attitudes and retention of students. Procedia Computer Science, 3, 1003-1008. https://doi.org/10.1016/j.procs.2010.12.165
-
Alsalhi, N. R. I. (2020). The representation of multiple intelligences in the science textbook and the extent of awareness of science teachers at the intermediate stage of this theory. Thinking Skills and Creativity, 38, 100706. https://doi.org/10.1016/j.tsc.2020.100706
-
Altınsoy, A.B. (2011). Fen ve teknoloji dersinde çoklu zekâ kuramına dayalı öğretimin öğrencilerin başarılarına etkisi [The effect of teaching based on multiple intelligence theory on students' success in science and technology courses]. [Unpublished master's thesis]. Selcuk University.
-
Araújo, A. C. D., Knijnik, J., & Ovens, A. P. (2021). How does physical education and health respond to the growing influence in media and digital technologies? An analysis of curriculum in Brazil, Australia and New Zealand. Journal of Curriculum Studies, 53(4), 563-577. https://doi.org/10.1080/00220272.2020.1734664
-
Arun Kumar, U., Mahendran, G., & Gobhinath, S. (2022). A review on artificial intelligence based E-learning system. Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, 659-671. https://doi.org/10.1007/978-981-19-2840-6_50
-
Ateş, R. Ö. (2007). 6. sınıflarda maddenin tanecikli yapısı konusunun çoklu zekâ kuramına dayalı öğretimi [Multiple intelligences theory based instruction of the particulate nature of the matter at 6th grade level] [Unpublished master's thesis]. Balikesir University.
-
Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080
-
Burlacu, S. (2011). Characteristics of knowledge-based economy and new technologies in education. Revista Administratie si Management Public (RAMP), (16), 114-119.
-
Büyüköztürk Ş. (2015). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences] (21th edition). Pegem Publishing.
-
Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2016). Bilimsel araştırma yöntemleri [Scientific research methods]. Pegem Publishing.
-
Cresswell, J. W., & Plano Clark, V. L. (2015). Karma yöntem araştırmaları: Tasarımı ve yürütülmesi [Mixed methods research: Design and conduct] (Trans. Y. Dede & S. B. Demir). Ani Publishing.
-
Demirel, Ö. (1999). Planlamadan degerlendirmeye ögrenme sanatı [The art of teaching from planning to evaluation]. Pegem Publiishing.
-
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
-
El-Sabagh, H. A., & Hamed, E. (2020). The relationship between learning-styles and learning motivation of students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal. https://doi.org/10.21608/EAEC.2020.25868.1015
-
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
-
Enger, S. K., & Yager, R. E. (2001). Assessing student understanding in science: A standards-based K-12 handbook. Corwin Press.
-
Eslit, E. (2023). Integrating multiple intelligence and artificial intelligence in language learning: Enhancing personalization and engagement. Preprints. https://doi.org/10.20944/preprints202307.1044.v1
-
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e- Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
-
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage publications limited.
-
Fu, X., Lokesh Krishna, K., & Sabitha, R. (2022). Artificial intelligence applications with e-learning system for China’s higher education platform. Journal of Interconnection Networks, 22(Supp02), 2143016. https://doi.org/10.1142/S0219265921430167
-
George, D. & Mallery, M. (2010). SPSS for windows step by step: A Simple Guide and References. Baston: Allyn &Bacon.
-
Hafidi, M., & Lamia, M. (2015, April). A personalized adaptive e-learning system based on learner's feedback and learner's multiple intelligences. In 2015 12th International Symposium on Programming and Systems (ISPS) (pp. 1-6). IEEE.
-
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
-
Hogo, M. A. (2010). Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert systems with applications, 37(10), 6891-6903. https://doi.org/10.1016/j.eswa.2010.03.032
-
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419-422. https://doi.org/10.1016/j.dsx.2020.04.032
-
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26. https://doi.org10.3102/0013189X033007014
-
Kacalak, W., & Majewski, M. (2009). E-learning systems with artificial intelligence in engineering. In Emerging Intelligent Computing Technology and Applications: 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings 5 (pp. 918-927). Springer Berlin Heidelberg.
-
Kaewkiriya, T., Utakrit, N., & Tiantong, M. (2016). The design of a rule base for an e-learning recommendation system base on multiple intelligences. International Journal of Information and Education Technology, 6(3), 206.
-
Kapıcı, H., & Akçay, H. (2016). Middle school students attitudes toward science scientists science teachers and classes. In The Asia-Pasific Forum on Science Learning and Teaching, 17(1), 1-22.
-
Khan, M. A., Khojah, M., & Vivek. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International, 1-10. https://doi.org/10.1155/2022/1263555
-
Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51, 101421. https://doi.org/10.1016/j.tsc.2023.101421
-
Magomadov, V. S. (2020). The application of artificial intelligence and big data analytics in personalized learning. Journal of Physics: Conference Series, 1691(1), 012169. IOP Publishing. https://doi.org/10.1088/1742-6596/1691/1/012169
-
Manickam, M. V., Mohanapriya, M., Kale, S., Uday, M., Kulkarni, P., Khandagale, Y., & Patil, S. P. (2017). Research study on applications of artificial neural networks and E-learning personalization. International Journal of Civil Engineering and Technology, 8(8), 1422-1432.
-
Mankad, K. B. (2015). The role of multiple intelligence in e-learning. IJSRD-International Journal for Scientific Research & Development, 3(05), 2321-0613.
-
Martin, E., Aziz, M. A., Pujihanarko, A., & Pratiwi, N. R. (2023). Exploring the research on utilizing machine learning in e-Learning systems. International Transactions on Artificial Intelligence, 2(1), 76-80. http://doi.org/10.33050
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publications.
-
Ministry of National Education [MoNE]. (2018). Science course curriculum (Grades 3–8 of primary and lower secondary education). Board of Education and Discipline.
-
Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H., & Shami, A. (2018). E-learning: Challenges and research opportunities using machine learning & data analytics. IEEE Access, 6, 39117-39138. https://doi.org/ 10.1109/ACCESS.2018.2851790
-
Oubalahcen, H., & Tamym, L. (2023). The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey. Procedia Computer Science, 224, 437-442. https://doi.org/10.1016/j.procs.2023.09.061
-
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
-
Parkavi, R., Karthikeyan, P., & Abdullah, A. S. (2024). Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system. Applied Soft Computing, 156, 111451. https://doi.org/10.1016/j.asoc.2024.111451
-
Pitychoutis, K. M., & Al Rawahi, A. (2024). Smart teaching: The synergy of multiple intelligences and artificial intelligence in english as a foreign language ınstruction.
-
Potode, A., & Manjare, P. (2015). E-learning using artificial intelligence. International Journal of Computer Science and Information Technology Research, 3(1), 78-82.
-
Seale, J., Colwell, C., Coughlan, T., Heiman, T., Kaspi-Tsahor, D., & Olenik-Shemesh, D. (2021). ‘Dreaming in colour’: disabled higher education students’ perspectives on improving design practices that would enable them to benefit from their use of technologies. Education and Information Technologies, 26, 1687-1719. https://doi.org/10639-020-10329-7
-
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hâkim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon, 6(11), 1-9. https://doi.org/10.1016/j.heliyon.2020.e05410
-
Susar Kırmızı, F. (2006). İlköğretim 4. sınıf Türkçe öğretiminde çoklu zekâ kuramına dayalı iş birlikli öğrenme yönteminin özetleme stratejisi üzerindeki etkileri [The effects of cooperative learning method based on multiple intelligence theory on summarizing strategy in teaching Turkish to 4th grade of primary school]. Pamukkale University Journal of Social Sciences Institute, (6), 99-108.
-
Şahan, A. (2018). Fen bilimleri öğretiminde çoklu zekâ destekli eğitim modelinin öğrenci başarısına ve fen tutumuna etkisi [The effect of the multiple intelligence supported education model on student success and science attitude in science teaching]. [Unpublished master's thesis]. Kirikkale University.
-
Şengül, S. H. (2007). Çoklu zekâ kuramı temelli öğretimin ilköğretim altıncı sınıf öğrencilerinin dolaşım sistemi başarıları üzerine etkisi [Effects of Multiple Intelligence Theory Based Instruction on Sixth Grade Primary School Students’ Achievement of Circulatory System]. [Unpublished master's thesis]. Balıkesir University.
-
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage.
-
Türkmen, L. (2007). The influences of elementary science teaching method courses on a Turkish teachers college elementary education major students’ attitudes towards science and science teaching. Journal of Baltic Science Education, 6(1), 66-77.
-
Uçak, E. (2006). Maddenin sınıflandırılması ve dönüşümleri" konusunda çoklu zekâ kuramı destekli öğretim yöntemi'nin öğrenci başarısı, tutumu ve hatırda tutma düzeyine etkisi [The effect of multiple intelligence based education method to the level of student success, attitude and remembering in the unit of changes and classification of matter]. [Unpublished master's thesis]. Pamukkale University.
-
Villaverde, J. E., Godoy, D., & Amandi, A. (2006). Learning styles' recognition in e‐learning environments with feed‐forward neural networks. Journal of Computer Assisted Learning, 22(3), 197-206. https://doi.org/10.1111/j.1365-2729.2006.00169.x
-
Yıldırım, A., & Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri [Qualitative research methods in the social sciences] (6th edition). Seckin Publishing.
Yıl 2025,
Cilt: 14 Sayı: 3, 1253 - 1274, 30.09.2025
Burcu Alan
,
Fikriye Kırbağ Zengin
Kaynakça
-
Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding E-learning during COVID-19 at a private medical college. Pakistan Journal of Medical Sciences, 36(COVID19-S4), 57-61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766
-
Ahmed, R. K. A. (2016). Artificial neural networks in E-learning personalization: A review. International Journal of Intelligent Information Systems, 5(6), 104-108. https://doi.org/10.11648/j.ijiis.20160506.14
-
Akkuzu, N., & Akçay, H. (2011). The design of a learning environment based on the theory of multiple intelligence and the study its effectiveness on the achievements, attitudes and retention of students. Procedia Computer Science, 3, 1003-1008. https://doi.org/10.1016/j.procs.2010.12.165
-
Alsalhi, N. R. I. (2020). The representation of multiple intelligences in the science textbook and the extent of awareness of science teachers at the intermediate stage of this theory. Thinking Skills and Creativity, 38, 100706. https://doi.org/10.1016/j.tsc.2020.100706
-
Altınsoy, A.B. (2011). Fen ve teknoloji dersinde çoklu zekâ kuramına dayalı öğretimin öğrencilerin başarılarına etkisi [The effect of teaching based on multiple intelligence theory on students' success in science and technology courses]. [Unpublished master's thesis]. Selcuk University.
-
Araújo, A. C. D., Knijnik, J., & Ovens, A. P. (2021). How does physical education and health respond to the growing influence in media and digital technologies? An analysis of curriculum in Brazil, Australia and New Zealand. Journal of Curriculum Studies, 53(4), 563-577. https://doi.org/10.1080/00220272.2020.1734664
-
Arun Kumar, U., Mahendran, G., & Gobhinath, S. (2022). A review on artificial intelligence based E-learning system. Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, 659-671. https://doi.org/10.1007/978-981-19-2840-6_50
-
Ateş, R. Ö. (2007). 6. sınıflarda maddenin tanecikli yapısı konusunun çoklu zekâ kuramına dayalı öğretimi [Multiple intelligences theory based instruction of the particulate nature of the matter at 6th grade level] [Unpublished master's thesis]. Balikesir University.
-
Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080
-
Burlacu, S. (2011). Characteristics of knowledge-based economy and new technologies in education. Revista Administratie si Management Public (RAMP), (16), 114-119.
-
Büyüköztürk Ş. (2015). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences] (21th edition). Pegem Publishing.
-
Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2016). Bilimsel araştırma yöntemleri [Scientific research methods]. Pegem Publishing.
-
Cresswell, J. W., & Plano Clark, V. L. (2015). Karma yöntem araştırmaları: Tasarımı ve yürütülmesi [Mixed methods research: Design and conduct] (Trans. Y. Dede & S. B. Demir). Ani Publishing.
-
Demirel, Ö. (1999). Planlamadan degerlendirmeye ögrenme sanatı [The art of teaching from planning to evaluation]. Pegem Publiishing.
-
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
-
El-Sabagh, H. A., & Hamed, E. (2020). The relationship between learning-styles and learning motivation of students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal. https://doi.org/10.21608/EAEC.2020.25868.1015
-
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
-
Enger, S. K., & Yager, R. E. (2001). Assessing student understanding in science: A standards-based K-12 handbook. Corwin Press.
-
Eslit, E. (2023). Integrating multiple intelligence and artificial intelligence in language learning: Enhancing personalization and engagement. Preprints. https://doi.org/10.20944/preprints202307.1044.v1
-
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e- Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
-
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage publications limited.
-
Fu, X., Lokesh Krishna, K., & Sabitha, R. (2022). Artificial intelligence applications with e-learning system for China’s higher education platform. Journal of Interconnection Networks, 22(Supp02), 2143016. https://doi.org/10.1142/S0219265921430167
-
George, D. & Mallery, M. (2010). SPSS for windows step by step: A Simple Guide and References. Baston: Allyn &Bacon.
-
Hafidi, M., & Lamia, M. (2015, April). A personalized adaptive e-learning system based on learner's feedback and learner's multiple intelligences. In 2015 12th International Symposium on Programming and Systems (ISPS) (pp. 1-6). IEEE.
-
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
-
Hogo, M. A. (2010). Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert systems with applications, 37(10), 6891-6903. https://doi.org/10.1016/j.eswa.2010.03.032
-
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419-422. https://doi.org/10.1016/j.dsx.2020.04.032
-
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26. https://doi.org10.3102/0013189X033007014
-
Kacalak, W., & Majewski, M. (2009). E-learning systems with artificial intelligence in engineering. In Emerging Intelligent Computing Technology and Applications: 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings 5 (pp. 918-927). Springer Berlin Heidelberg.
-
Kaewkiriya, T., Utakrit, N., & Tiantong, M. (2016). The design of a rule base for an e-learning recommendation system base on multiple intelligences. International Journal of Information and Education Technology, 6(3), 206.
-
Kapıcı, H., & Akçay, H. (2016). Middle school students attitudes toward science scientists science teachers and classes. In The Asia-Pasific Forum on Science Learning and Teaching, 17(1), 1-22.
-
Khan, M. A., Khojah, M., & Vivek. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International, 1-10. https://doi.org/10.1155/2022/1263555
-
Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51, 101421. https://doi.org/10.1016/j.tsc.2023.101421
-
Magomadov, V. S. (2020). The application of artificial intelligence and big data analytics in personalized learning. Journal of Physics: Conference Series, 1691(1), 012169. IOP Publishing. https://doi.org/10.1088/1742-6596/1691/1/012169
-
Manickam, M. V., Mohanapriya, M., Kale, S., Uday, M., Kulkarni, P., Khandagale, Y., & Patil, S. P. (2017). Research study on applications of artificial neural networks and E-learning personalization. International Journal of Civil Engineering and Technology, 8(8), 1422-1432.
-
Mankad, K. B. (2015). The role of multiple intelligence in e-learning. IJSRD-International Journal for Scientific Research & Development, 3(05), 2321-0613.
-
Martin, E., Aziz, M. A., Pujihanarko, A., & Pratiwi, N. R. (2023). Exploring the research on utilizing machine learning in e-Learning systems. International Transactions on Artificial Intelligence, 2(1), 76-80. http://doi.org/10.33050
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publications.
-
Ministry of National Education [MoNE]. (2018). Science course curriculum (Grades 3–8 of primary and lower secondary education). Board of Education and Discipline.
-
Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H., & Shami, A. (2018). E-learning: Challenges and research opportunities using machine learning & data analytics. IEEE Access, 6, 39117-39138. https://doi.org/ 10.1109/ACCESS.2018.2851790
-
Oubalahcen, H., & Tamym, L. (2023). The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey. Procedia Computer Science, 224, 437-442. https://doi.org/10.1016/j.procs.2023.09.061
-
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
-
Parkavi, R., Karthikeyan, P., & Abdullah, A. S. (2024). Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system. Applied Soft Computing, 156, 111451. https://doi.org/10.1016/j.asoc.2024.111451
-
Pitychoutis, K. M., & Al Rawahi, A. (2024). Smart teaching: The synergy of multiple intelligences and artificial intelligence in english as a foreign language ınstruction.
-
Potode, A., & Manjare, P. (2015). E-learning using artificial intelligence. International Journal of Computer Science and Information Technology Research, 3(1), 78-82.
-
Seale, J., Colwell, C., Coughlan, T., Heiman, T., Kaspi-Tsahor, D., & Olenik-Shemesh, D. (2021). ‘Dreaming in colour’: disabled higher education students’ perspectives on improving design practices that would enable them to benefit from their use of technologies. Education and Information Technologies, 26, 1687-1719. https://doi.org/10639-020-10329-7
-
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hâkim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon, 6(11), 1-9. https://doi.org/10.1016/j.heliyon.2020.e05410
-
Susar Kırmızı, F. (2006). İlköğretim 4. sınıf Türkçe öğretiminde çoklu zekâ kuramına dayalı iş birlikli öğrenme yönteminin özetleme stratejisi üzerindeki etkileri [The effects of cooperative learning method based on multiple intelligence theory on summarizing strategy in teaching Turkish to 4th grade of primary school]. Pamukkale University Journal of Social Sciences Institute, (6), 99-108.
-
Şahan, A. (2018). Fen bilimleri öğretiminde çoklu zekâ destekli eğitim modelinin öğrenci başarısına ve fen tutumuna etkisi [The effect of the multiple intelligence supported education model on student success and science attitude in science teaching]. [Unpublished master's thesis]. Kirikkale University.
-
Şengül, S. H. (2007). Çoklu zekâ kuramı temelli öğretimin ilköğretim altıncı sınıf öğrencilerinin dolaşım sistemi başarıları üzerine etkisi [Effects of Multiple Intelligence Theory Based Instruction on Sixth Grade Primary School Students’ Achievement of Circulatory System]. [Unpublished master's thesis]. Balıkesir University.
-
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage.
-
Türkmen, L. (2007). The influences of elementary science teaching method courses on a Turkish teachers college elementary education major students’ attitudes towards science and science teaching. Journal of Baltic Science Education, 6(1), 66-77.
-
Uçak, E. (2006). Maddenin sınıflandırılması ve dönüşümleri" konusunda çoklu zekâ kuramı destekli öğretim yöntemi'nin öğrenci başarısı, tutumu ve hatırda tutma düzeyine etkisi [The effect of multiple intelligence based education method to the level of student success, attitude and remembering in the unit of changes and classification of matter]. [Unpublished master's thesis]. Pamukkale University.
-
Villaverde, J. E., Godoy, D., & Amandi, A. (2006). Learning styles' recognition in e‐learning environments with feed‐forward neural networks. Journal of Computer Assisted Learning, 22(3), 197-206. https://doi.org/10.1111/j.1365-2729.2006.00169.x
-
Yıldırım, A., & Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri [Qualitative research methods in the social sciences] (6th edition). Seckin Publishing.
Yıl 2025,
Cilt: 14 Sayı: 3, 1253 - 1274, 30.09.2025
Burcu Alan
,
Fikriye Kırbağ Zengin
Kaynakça
-
Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding E-learning during COVID-19 at a private medical college. Pakistan Journal of Medical Sciences, 36(COVID19-S4), 57-61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766
-
Ahmed, R. K. A. (2016). Artificial neural networks in E-learning personalization: A review. International Journal of Intelligent Information Systems, 5(6), 104-108. https://doi.org/10.11648/j.ijiis.20160506.14
-
Akkuzu, N., & Akçay, H. (2011). The design of a learning environment based on the theory of multiple intelligence and the study its effectiveness on the achievements, attitudes and retention of students. Procedia Computer Science, 3, 1003-1008. https://doi.org/10.1016/j.procs.2010.12.165
-
Alsalhi, N. R. I. (2020). The representation of multiple intelligences in the science textbook and the extent of awareness of science teachers at the intermediate stage of this theory. Thinking Skills and Creativity, 38, 100706. https://doi.org/10.1016/j.tsc.2020.100706
-
Altınsoy, A.B. (2011). Fen ve teknoloji dersinde çoklu zekâ kuramına dayalı öğretimin öğrencilerin başarılarına etkisi [The effect of teaching based on multiple intelligence theory on students' success in science and technology courses]. [Unpublished master's thesis]. Selcuk University.
-
Araújo, A. C. D., Knijnik, J., & Ovens, A. P. (2021). How does physical education and health respond to the growing influence in media and digital technologies? An analysis of curriculum in Brazil, Australia and New Zealand. Journal of Curriculum Studies, 53(4), 563-577. https://doi.org/10.1080/00220272.2020.1734664
-
Arun Kumar, U., Mahendran, G., & Gobhinath, S. (2022). A review on artificial intelligence based E-learning system. Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, 659-671. https://doi.org/10.1007/978-981-19-2840-6_50
-
Ateş, R. Ö. (2007). 6. sınıflarda maddenin tanecikli yapısı konusunun çoklu zekâ kuramına dayalı öğretimi [Multiple intelligences theory based instruction of the particulate nature of the matter at 6th grade level] [Unpublished master's thesis]. Balikesir University.
-
Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080
-
Burlacu, S. (2011). Characteristics of knowledge-based economy and new technologies in education. Revista Administratie si Management Public (RAMP), (16), 114-119.
-
Büyüköztürk Ş. (2015). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences] (21th edition). Pegem Publishing.
-
Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2016). Bilimsel araştırma yöntemleri [Scientific research methods]. Pegem Publishing.
-
Cresswell, J. W., & Plano Clark, V. L. (2015). Karma yöntem araştırmaları: Tasarımı ve yürütülmesi [Mixed methods research: Design and conduct] (Trans. Y. Dede & S. B. Demir). Ani Publishing.
-
Demirel, Ö. (1999). Planlamadan degerlendirmeye ögrenme sanatı [The art of teaching from planning to evaluation]. Pegem Publiishing.
-
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
-
El-Sabagh, H. A., & Hamed, E. (2020). The relationship between learning-styles and learning motivation of students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal. https://doi.org/10.21608/EAEC.2020.25868.1015
-
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
-
Enger, S. K., & Yager, R. E. (2001). Assessing student understanding in science: A standards-based K-12 handbook. Corwin Press.
-
Eslit, E. (2023). Integrating multiple intelligence and artificial intelligence in language learning: Enhancing personalization and engagement. Preprints. https://doi.org/10.20944/preprints202307.1044.v1
-
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e- Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
-
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage publications limited.
-
Fu, X., Lokesh Krishna, K., & Sabitha, R. (2022). Artificial intelligence applications with e-learning system for China’s higher education platform. Journal of Interconnection Networks, 22(Supp02), 2143016. https://doi.org/10.1142/S0219265921430167
-
George, D. & Mallery, M. (2010). SPSS for windows step by step: A Simple Guide and References. Baston: Allyn &Bacon.
-
Hafidi, M., & Lamia, M. (2015, April). A personalized adaptive e-learning system based on learner's feedback and learner's multiple intelligences. In 2015 12th International Symposium on Programming and Systems (ISPS) (pp. 1-6). IEEE.
-
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
-
Hogo, M. A. (2010). Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert systems with applications, 37(10), 6891-6903. https://doi.org/10.1016/j.eswa.2010.03.032
-
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419-422. https://doi.org/10.1016/j.dsx.2020.04.032
-
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26. https://doi.org10.3102/0013189X033007014
-
Kacalak, W., & Majewski, M. (2009). E-learning systems with artificial intelligence in engineering. In Emerging Intelligent Computing Technology and Applications: 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings 5 (pp. 918-927). Springer Berlin Heidelberg.
-
Kaewkiriya, T., Utakrit, N., & Tiantong, M. (2016). The design of a rule base for an e-learning recommendation system base on multiple intelligences. International Journal of Information and Education Technology, 6(3), 206.
-
Kapıcı, H., & Akçay, H. (2016). Middle school students attitudes toward science scientists science teachers and classes. In The Asia-Pasific Forum on Science Learning and Teaching, 17(1), 1-22.
-
Khan, M. A., Khojah, M., & Vivek. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International, 1-10. https://doi.org/10.1155/2022/1263555
-
Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51, 101421. https://doi.org/10.1016/j.tsc.2023.101421
-
Magomadov, V. S. (2020). The application of artificial intelligence and big data analytics in personalized learning. Journal of Physics: Conference Series, 1691(1), 012169. IOP Publishing. https://doi.org/10.1088/1742-6596/1691/1/012169
-
Manickam, M. V., Mohanapriya, M., Kale, S., Uday, M., Kulkarni, P., Khandagale, Y., & Patil, S. P. (2017). Research study on applications of artificial neural networks and E-learning personalization. International Journal of Civil Engineering and Technology, 8(8), 1422-1432.
-
Mankad, K. B. (2015). The role of multiple intelligence in e-learning. IJSRD-International Journal for Scientific Research & Development, 3(05), 2321-0613.
-
Martin, E., Aziz, M. A., Pujihanarko, A., & Pratiwi, N. R. (2023). Exploring the research on utilizing machine learning in e-Learning systems. International Transactions on Artificial Intelligence, 2(1), 76-80. http://doi.org/10.33050
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publications.
-
Ministry of National Education [MoNE]. (2018). Science course curriculum (Grades 3–8 of primary and lower secondary education). Board of Education and Discipline.
-
Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H., & Shami, A. (2018). E-learning: Challenges and research opportunities using machine learning & data analytics. IEEE Access, 6, 39117-39138. https://doi.org/ 10.1109/ACCESS.2018.2851790
-
Oubalahcen, H., & Tamym, L. (2023). The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey. Procedia Computer Science, 224, 437-442. https://doi.org/10.1016/j.procs.2023.09.061
-
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
-
Parkavi, R., Karthikeyan, P., & Abdullah, A. S. (2024). Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system. Applied Soft Computing, 156, 111451. https://doi.org/10.1016/j.asoc.2024.111451
-
Pitychoutis, K. M., & Al Rawahi, A. (2024). Smart teaching: The synergy of multiple intelligences and artificial intelligence in english as a foreign language ınstruction.
-
Potode, A., & Manjare, P. (2015). E-learning using artificial intelligence. International Journal of Computer Science and Information Technology Research, 3(1), 78-82.
-
Seale, J., Colwell, C., Coughlan, T., Heiman, T., Kaspi-Tsahor, D., & Olenik-Shemesh, D. (2021). ‘Dreaming in colour’: disabled higher education students’ perspectives on improving design practices that would enable them to benefit from their use of technologies. Education and Information Technologies, 26, 1687-1719. https://doi.org/10639-020-10329-7
-
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hâkim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon, 6(11), 1-9. https://doi.org/10.1016/j.heliyon.2020.e05410
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Susar Kırmızı, F. (2006). İlköğretim 4. sınıf Türkçe öğretiminde çoklu zekâ kuramına dayalı iş birlikli öğrenme yönteminin özetleme stratejisi üzerindeki etkileri [The effects of cooperative learning method based on multiple intelligence theory on summarizing strategy in teaching Turkish to 4th grade of primary school]. Pamukkale University Journal of Social Sciences Institute, (6), 99-108.
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Şahan, A. (2018). Fen bilimleri öğretiminde çoklu zekâ destekli eğitim modelinin öğrenci başarısına ve fen tutumuna etkisi [The effect of the multiple intelligence supported education model on student success and science attitude in science teaching]. [Unpublished master's thesis]. Kirikkale University.
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Şengül, S. H. (2007). Çoklu zekâ kuramı temelli öğretimin ilköğretim altıncı sınıf öğrencilerinin dolaşım sistemi başarıları üzerine etkisi [Effects of Multiple Intelligence Theory Based Instruction on Sixth Grade Primary School Students’ Achievement of Circulatory System]. [Unpublished master's thesis]. Balıkesir University.
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Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage.
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The Effect of Artificial Intelligence-Based E-Learning Environment on Students' Attitudes Towards Science Course
Yıl 2025,
Cilt: 14 Sayı: 3, 1253 - 1274, 30.09.2025
Burcu Alan
,
Fikriye Kırbağ Zengin
Öz
This study aims to examine the effects of e-learning environments prepared according to multiple intelligence fields determined by artificial intelligence in science teaching on the attitudes of 5th-grade students towards the science course and to obtain the students' opinions. The study was conducted within the framework of mixed methods. Conducted in the 2022-2023 academic year, the research involved 130 students (58 girls and 72 boys) from one experimental and three control groups at a secondary school in Elâzığ. Quantitative data were collected using the "Science Course Attitude Scale," while qualitative data were gathered through semi-structured interviews. SPSS 23 package program was utilized for quantitative data analysis, performing a One-Way ANOVA, while qualitative data underwent content analysis. Over eight weeks (four hours weekly), the website created for the study first identified the dominant intelligence types of experimental group students. They then received training on the "Matter and Change" unit in an e-learning environment tailored to their intelligence types. In contrast, control groups followed the standard curriculum with teacher-led lessons. The ANOVA results indicated no statistically significant difference in science course attitude scores between the experimental and control groups. However, interviews with experimental group students revealed that their interest, desire, curiosity, and motivation toward the science course increased. They highlighted that the platform tailored to their dominant intelligence types provided benefits such as personalized learning, ease of learning, enjoyable experiences, a positive attitude towards the subject, and an engaging, that is free of boredom learning environment.
Etik Beyan
This study includes a part of the doctoral thesis entitled "The Analysis of E-Learning Settings, Which Are Prepared on the Basis of Multiple Intelligence Domains Determined by Artificial Intelligence in Science Instruction, as per Different Variables." It declares that scientific and ethical principles have been followed while carrying out and writing this study and that all the sources used have been properly cited.
Destekleyen Kurum
This research has been supported by the Scientific Research Projects Unit of Fırat University (Project number: EF.21.01)
Kaynakça
-
Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding E-learning during COVID-19 at a private medical college. Pakistan Journal of Medical Sciences, 36(COVID19-S4), 57-61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766
-
Ahmed, R. K. A. (2016). Artificial neural networks in E-learning personalization: A review. International Journal of Intelligent Information Systems, 5(6), 104-108. https://doi.org/10.11648/j.ijiis.20160506.14
-
Akkuzu, N., & Akçay, H. (2011). The design of a learning environment based on the theory of multiple intelligence and the study its effectiveness on the achievements, attitudes and retention of students. Procedia Computer Science, 3, 1003-1008. https://doi.org/10.1016/j.procs.2010.12.165
-
Alsalhi, N. R. I. (2020). The representation of multiple intelligences in the science textbook and the extent of awareness of science teachers at the intermediate stage of this theory. Thinking Skills and Creativity, 38, 100706. https://doi.org/10.1016/j.tsc.2020.100706
-
Altınsoy, A.B. (2011). Fen ve teknoloji dersinde çoklu zekâ kuramına dayalı öğretimin öğrencilerin başarılarına etkisi [The effect of teaching based on multiple intelligence theory on students' success in science and technology courses]. [Unpublished master's thesis]. Selcuk University.
-
Araújo, A. C. D., Knijnik, J., & Ovens, A. P. (2021). How does physical education and health respond to the growing influence in media and digital technologies? An analysis of curriculum in Brazil, Australia and New Zealand. Journal of Curriculum Studies, 53(4), 563-577. https://doi.org/10.1080/00220272.2020.1734664
-
Arun Kumar, U., Mahendran, G., & Gobhinath, S. (2022). A review on artificial intelligence based E-learning system. Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, 659-671. https://doi.org/10.1007/978-981-19-2840-6_50
-
Ateş, R. Ö. (2007). 6. sınıflarda maddenin tanecikli yapısı konusunun çoklu zekâ kuramına dayalı öğretimi [Multiple intelligences theory based instruction of the particulate nature of the matter at 6th grade level] [Unpublished master's thesis]. Balikesir University.
-
Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080
-
Burlacu, S. (2011). Characteristics of knowledge-based economy and new technologies in education. Revista Administratie si Management Public (RAMP), (16), 114-119.
-
Büyüköztürk Ş. (2015). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences] (21th edition). Pegem Publishing.
-
Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö., Karadeniz, Ş., & Demirel, F. (2016). Bilimsel araştırma yöntemleri [Scientific research methods]. Pegem Publishing.
-
Cresswell, J. W., & Plano Clark, V. L. (2015). Karma yöntem araştırmaları: Tasarımı ve yürütülmesi [Mixed methods research: Design and conduct] (Trans. Y. Dede & S. B. Demir). Ani Publishing.
-
Demirel, Ö. (1999). Planlamadan degerlendirmeye ögrenme sanatı [The art of teaching from planning to evaluation]. Pegem Publiishing.
-
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
-
El-Sabagh, H. A., & Hamed, E. (2020). The relationship between learning-styles and learning motivation of students at Umm Al-Qura University. Egyptian Association for Educational Computer Journal. https://doi.org/10.21608/EAEC.2020.25868.1015
-
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115.
-
Enger, S. K., & Yager, R. E. (2001). Assessing student understanding in science: A standards-based K-12 handbook. Corwin Press.
-
Eslit, E. (2023). Integrating multiple intelligence and artificial intelligence in language learning: Enhancing personalization and engagement. Preprints. https://doi.org/10.20944/preprints202307.1044.v1
-
Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and e- Learning during COVID-19 pandemic. Computer Networks, 176, 107290. https://doi.org/10.1016/j.comnet.2020.107290
-
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage publications limited.
-
Fu, X., Lokesh Krishna, K., & Sabitha, R. (2022). Artificial intelligence applications with e-learning system for China’s higher education platform. Journal of Interconnection Networks, 22(Supp02), 2143016. https://doi.org/10.1142/S0219265921430167
-
George, D. & Mallery, M. (2010). SPSS for windows step by step: A Simple Guide and References. Baston: Allyn &Bacon.
-
Hafidi, M., & Lamia, M. (2015, April). A personalized adaptive e-learning system based on learner's feedback and learner's multiple intelligences. In 2015 12th International Symposium on Programming and Systems (ISPS) (pp. 1-6). IEEE.
-
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
-
Hogo, M. A. (2010). Evaluation of e-learning systems based on fuzzy clustering models and statistical tools. Expert systems with applications, 37(10), 6891-6903. https://doi.org/10.1016/j.eswa.2010.03.032
-
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419-422. https://doi.org/10.1016/j.dsx.2020.04.032
-
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26. https://doi.org10.3102/0013189X033007014
-
Kacalak, W., & Majewski, M. (2009). E-learning systems with artificial intelligence in engineering. In Emerging Intelligent Computing Technology and Applications: 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009. Proceedings 5 (pp. 918-927). Springer Berlin Heidelberg.
-
Kaewkiriya, T., Utakrit, N., & Tiantong, M. (2016). The design of a rule base for an e-learning recommendation system base on multiple intelligences. International Journal of Information and Education Technology, 6(3), 206.
-
Kapıcı, H., & Akçay, H. (2016). Middle school students attitudes toward science scientists science teachers and classes. In The Asia-Pasific Forum on Science Learning and Teaching, 17(1), 1-22.
-
Khan, M. A., Khojah, M., & Vivek. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International, 1-10. https://doi.org/10.1155/2022/1263555
-
Lokare, V. T., & Jadhav, P. M. (2024). An AI-based learning style prediction model for personalized and effective learning. Thinking Skills and Creativity, 51, 101421. https://doi.org/10.1016/j.tsc.2023.101421
-
Magomadov, V. S. (2020). The application of artificial intelligence and big data analytics in personalized learning. Journal of Physics: Conference Series, 1691(1), 012169. IOP Publishing. https://doi.org/10.1088/1742-6596/1691/1/012169
-
Manickam, M. V., Mohanapriya, M., Kale, S., Uday, M., Kulkarni, P., Khandagale, Y., & Patil, S. P. (2017). Research study on applications of artificial neural networks and E-learning personalization. International Journal of Civil Engineering and Technology, 8(8), 1422-1432.
-
Mankad, K. B. (2015). The role of multiple intelligence in e-learning. IJSRD-International Journal for Scientific Research & Development, 3(05), 2321-0613.
-
Martin, E., Aziz, M. A., Pujihanarko, A., & Pratiwi, N. R. (2023). Exploring the research on utilizing machine learning in e-Learning systems. International Transactions on Artificial Intelligence, 2(1), 76-80. http://doi.org/10.33050
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publications.
-
Ministry of National Education [MoNE]. (2018). Science course curriculum (Grades 3–8 of primary and lower secondary education). Board of Education and Discipline.
-
Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H., & Shami, A. (2018). E-learning: Challenges and research opportunities using machine learning & data analytics. IEEE Access, 6, 39117-39138. https://doi.org/ 10.1109/ACCESS.2018.2851790
-
Oubalahcen, H., & Tamym, L. (2023). The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey. Procedia Computer Science, 224, 437-442. https://doi.org/10.1016/j.procs.2023.09.061
-
Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.
-
Parkavi, R., Karthikeyan, P., & Abdullah, A. S. (2024). Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system. Applied Soft Computing, 156, 111451. https://doi.org/10.1016/j.asoc.2024.111451
-
Pitychoutis, K. M., & Al Rawahi, A. (2024). Smart teaching: The synergy of multiple intelligences and artificial intelligence in english as a foreign language ınstruction.
-
Potode, A., & Manjare, P. (2015). E-learning using artificial intelligence. International Journal of Computer Science and Information Technology Research, 3(1), 78-82.
-
Seale, J., Colwell, C., Coughlan, T., Heiman, T., Kaspi-Tsahor, D., & Olenik-Shemesh, D. (2021). ‘Dreaming in colour’: disabled higher education students’ perspectives on improving design practices that would enable them to benefit from their use of technologies. Education and Information Technologies, 26, 1687-1719. https://doi.org/10639-020-10329-7
-
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hâkim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon, 6(11), 1-9. https://doi.org/10.1016/j.heliyon.2020.e05410
-
Susar Kırmızı, F. (2006). İlköğretim 4. sınıf Türkçe öğretiminde çoklu zekâ kuramına dayalı iş birlikli öğrenme yönteminin özetleme stratejisi üzerindeki etkileri [The effects of cooperative learning method based on multiple intelligence theory on summarizing strategy in teaching Turkish to 4th grade of primary school]. Pamukkale University Journal of Social Sciences Institute, (6), 99-108.
-
Şahan, A. (2018). Fen bilimleri öğretiminde çoklu zekâ destekli eğitim modelinin öğrenci başarısına ve fen tutumuna etkisi [The effect of the multiple intelligence supported education model on student success and science attitude in science teaching]. [Unpublished master's thesis]. Kirikkale University.
-
Şengül, S. H. (2007). Çoklu zekâ kuramı temelli öğretimin ilköğretim altıncı sınıf öğrencilerinin dolaşım sistemi başarıları üzerine etkisi [Effects of Multiple Intelligence Theory Based Instruction on Sixth Grade Primary School Students’ Achievement of Circulatory System]. [Unpublished master's thesis]. Balıkesir University.
-
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage.
-
Türkmen, L. (2007). The influences of elementary science teaching method courses on a Turkish teachers college elementary education major students’ attitudes towards science and science teaching. Journal of Baltic Science Education, 6(1), 66-77.
-
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Yapay Zeka Tabanlı E-Öğrenme Ortamının Öğrencilerin Fen Dersine Yönelik Tutumları Üzerine Etkisi
Yıl 2025,
Cilt: 14 Sayı: 3, 1253 - 1274, 30.09.2025
Burcu Alan
,
Fikriye Kırbağ Zengin
Öz
Bu çalışmanın amacı, fen öğretiminde yapay zekâ ile belirlenen çoklu zekâ alanlarına göre hazırlanmış e-öğrenme ortamlarının, 5.sınıf öğrencilerinin fen dersine yönelik tutumlarına etkisinin incelenmesi ve öğrencilerin görüşlerinin alınmasıdır. Çalışma, karma yöntem çerçevesinde yürütülmüştür. Çalışma, 2022-2023 eğitim öğretim yılı Elâzığ iline bağlı bir ortaokulda öğrenim görmekte olan biri deney, üçü kontrol olmak üzere 58 kız, 72’si erkek toplamda 130 öğrenci ile gerçekleştirilmiştir. Çalışmanın nicel verileri “Fen Dersine Yönelik Tutum Ölçeği” ile toplanırken, nitel verileri yarı yapılandırılmış mülakat ile toplanmıştır. Nicel verilerin analizinde SPSS 23 paket programı kullanılmıştır ve Tek Yönlü Varyans Analizi (ANOVA) yapılmıştır. Çalışmanın nitel verileri ise içerik analizi ile değerlendirilmiştir. Çalışma sekiz haftada (haftada dört saat) tamamlanmıştır. Çalışmaya özel olarak tasarlanan web sitesinde öncelikle deney grubu öğrencilerinin her birinin baskın zekâ türü belirlenmiş ardından ise baskın zekâ türlerine uygun olarak hazırlanmış e-öğrenme ortamında “Madde ve Değişim” ünitesine yönelik eğitim almaları sağlanmıştır. Kontrol gruplarında ise mevcut öğretim programına uygun, öğretmen eşliğinde dersler normal seyrinde işlenmiştir. Çalışmanın ANOVA sonuçlarına göre; deney grubu öğrencilerinin fen dersine yönelik tutumları ile kontrol gruplarının fen dersine yönelik tutumlarının arasında deney grubu lehine anlamlı bir farklılık tespit edilmemiştir. Uygulama sonrasında deney grubu öğrencileri ile yapılan mülakat sonuçlarında ise öğrenciler fen bilimleri dersine yönelik; ilgi, istek, merak ve motivasyonlarının arttığını sıklıkla dile getirmişlerdir. Ayrıca, platformun baskın zekâ tipine uygun olarak hazırlanmış olmasının kişiselleştirilmiş öğrenme, kolay öğrenme, eğlenerek öğrenme, derse karşı olumlu tutum geliştirme, başarıya katkı sağlama, dikkat çekici olma ve sıkılmadan öğrenme gibi birçok açıdan avantaj sağladığını da belirtmişlerdir.
Etik Beyan
This study includes a part of the doctoral thesis entitled "The Analysis of E-Learning Settings, Which Are Prepared on the Basis of Multiple Intelligence Domains Determined by Artificial Intelligence in Science Instruction, as per Different Variables." It declares that scientific and ethical principles have been followed while carrying out and writing this study and that all the sources used have been properly cited.
Destekleyen Kurum
Bu araştırma Fırat Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir (Proje numarası: EF.21.01)
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