Research Article
- Published in
- Volume 9, Issue 3 (2026)
- Pages
- 365–373
- Publication Date
- September 30, 2026
- Submission Date
- February 24, 2026
- Acceptance Date
- August 30, 2026
- Subjects
- Instructional Technologies
Abstract
This study examined the effectiveness of a Teachable Machine-based instructional program for developing electronic concepts among eighth-grade students in the Palestinian technology curriculum. A quasi-experimental, nonequivalent pretest-posttest control-group design compared two pre-existing classes, since students were not individually randomized. The sample comprised 80 male students from one school, with 40 in each group. The experimental group completed 12 lessons over six weeks using a sequence that integrated Google Teachable Machine for supervised image classification with PictoBlox for linking classified images to concept names and explanations; the control group studied the same unit conventionally. Achievement was measured with a 25-item Electronic Concepts Test. The experimental group achieved a higher post-test mean (M = 24.20, SD = 0.88) than the control group (M = 7.75, SD = 2.56), t(78) = 38.44, p < .001, 95% CI [15.60, 17.31], Cohen's d = 8.60, and Black's modified gain coefficient was 1.57. A carefully structured visual-classification sequence can support immediate acquisition of electronic concepts, but the exceptionally large effect, the single-school sample, and the absence of delayed measurement require cautious interpretation and independent replication.
Keywords
- artificial intelligence in education
- Teachable Machine
- electronic concepts
- quasi-experimental design
- eighth grade
References (11)
- Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. Wiley.
- Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., & Chen, A. (2020). Teachable Machine: Approachable web-based tool for exploring machine learning classification. In Extended abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–8). Association for Computing Machinery.
- Casal-Otero, L., Catalá, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10, Article 29.
- Gresse von Wangenheim, C., Hauck, J. C. R., Pacheco, F. S., & Bueno, M. F. B. (2021). Visual tools for teaching machine learning in K–12: A ten-year systematic mapping. Education and Information Technologies, 26, 5733–5778.
- Khamis, M. A. (2013). Educational computer and multimedia technology [In Arabic]. Dar Al-Sahab.
- Martins, R. M., Gresse von Wangenheim, C., Rauber, M. F., & Hauck, J. C. R. (2023). Machine learning for all! Introducing machine learning in middle and high school. International Journal of Artificial Intelligence in Education. Advance online publication.
- Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.
- Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial intelligence teaching and learning in K–12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 4, Article 100145.
- Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., & Vartiainen, H. (2020). Co-designing machine learning apps in K–12 with primary school children. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (pp. 308–310). IEEE.
- Yim, I. H. Y., & Su, J. (2024). Artificial intelligence learning tools in K–12 education: A scoping review. Journal of Computers in Education. Advance online publication.
- Yue, M., Jong, M. S. Y., & Dai, Y. (2022). Pedagogical design of K–12 artificial intelligence education: A systematic review. Sustainability, 14, Article 15620.
This article is published under the CC BY 4.0 license.