PILOT EVALUATION OF AN AI-IN-PYTHON ELECTIVE COURSE: EFFECTS ON MIDDLE SCHOOLERS' COMPUTATIONAL THINKING AND MOTIVATION

Article Sidebar

PDF
Published: 16.08.2026

Main Article Content

Nurzhaubayeva Tursynay Asankhankyzy
Limited Liability Partnership “PhysTech School Almaty”
Maratova Tolganay Farkhatkyzy
Kazakh National Women’s Teacher Training University

Abstract

The article presents the quantitative findings of a research study evaluating the effectiveness of the author's 34-hour elective course "Artificial Intelligence Programming in Python," piloted among 7th-grade students. Utilizing a pre/post assessment research design, changes in computational thinking levels, practical skills in utilizing applied libraries (NumPy, Pandas, OpenCV, scikit- learn), and academic motivation were measured. The initial diagnostic survey revealed a significant gap between students' high interest in AI (87%) and their baseline practical readiness (only 8% were capable of working with data libraries). A post-course evaluation in the pilot group (N = 24) demonstrated statistically significant growth across all parameters: 92% of students successfully completed practical mini-projects, 83% mastered basic data processing and computer vision, and 78% exhibited high autonomy in solving algorithmic problems. The findings confirm the pedagogical value of integrating applied AI programming into middle school education.

Downloads

Download data is not yet available.

Article Details

Data Availability Statement

All data supporting the findings of this study are presented in the text of the scientific work.

Issue

No. 305 (2026): 19th ISPC «Scientific Horizon in the Context of Social Crises» (August 16-18, 2026; Tokyo, Japan).

Section

Pedagogy and Education
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Biographies

Nurzhaubayeva Tursynay Asankhankyzy, Limited Liability Partnership “PhysTech School Almaty”

Master of Natural Sciences, Computer Science Teacher

Maratova Tolganay Farkhatkyzy, Kazakh National Women’s Teacher Training University

Doctor of Philosophy (PhD) in Computer Science

How to Cite

Nurzhaubayeva, T., & Maratova, T. (2026). PILOT EVALUATION OF AN AI-IN-PYTHON ELECTIVE COURSE: EFFECTS ON MIDDLE SCHOOLERS’ COMPUTATIONAL THINKING AND MOTIVATION. Scientific Collection «InterConf», 305, 111–116. https://interconf.openpubarchive.com/index.php/proceeding/article/view/43

References

Hamouda, S., Shaffer, C. A., & Edwards, S. H. (2025). Quantitative pre/post assessment of programming gaps and computational thinking in K-12 computer science education. ACM Transactions on Computing Education, 25(1), 45 –68. https://doi.org/10.1145/3631234 «Scientific Horizon in the Context No

Price, T. W., Dong, Y., & Lipovac, D. (2023). Empirical evaluation of computational thinking development in middle school Python curricula. IEEE Transactions on Education, 66(4), 312 –322. https://doi.org/10.1109/TE.2023.3289012

Yadav, A., Good, J., Voogt, J., & Fisser, P. (2017). Computational thinking in K -12 learning environments: A quantitative pre/ post evaluation framework. Computers & Education, 115, 101 –115. https://doi.org/10.1016/j.compedu.2017.07.014

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33 –35. https://doi.org/10.1145/1118178.1118215

Grover, S., & Pea, R. (2013). Computational Thinking in K –12: A Review of the State of the Field. Educational Researcher, 42(1), 38 –43. https://doi.org/10.3102/0013189X12459518 116 This work is distributed under the terms of the Creative