The Effect of Artificial Intelligence-Based E-Learning Environment on Students' Attitudes Towards Science Course
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.
Anahtar Kelimeler
Artificial Intelligence, Machine Learning, Multiple Intelligence Theory, Science Education, E-Learning, Science Attitude
Destekleyen Kurum
This research has been supported by the Scientific Research Projects Unit of Fırat University (Project number: EF.21.01)
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.
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.