doi: 10.18178/ijiet.2026.16.9.2708
Predicting the Preparation Level of Future Mathematics Teachers Using Machine Learning Models and Wolfram Alpha Data
Rima Abdualiyeva1
,
Karlygash Shetiyeva2,* ,
Aigul Aldabergenova2 ,
Gulmaral Mashripkhanova2 ,
Assem Yerkinova3
- 1Department of Informatics and Digitalization of Education, Zhetysu University named after I. Zhansugurov, Taldykorgan, Kazakhstan
- 2Department of Information Technologies and Artificial Intelligence, Zhetysu University named after I. Zhansugurov, Taldykorgan, Kazakhstan
- 3Alem.AI Foundation, Astana, Kazakhstan
- Manuscript receivedMarch 17, 2026
- revisedApril 15, 2026
- acceptedMay 15, 2026
- publishedSeptember 21, 2026
Abstract
The study examines the use of a machine learning framework that predicts the preparation level of prospective mathematics teachers through the integration of academic, behavioral, and analytic measures based on mathematical tasks and Wolfram Alpha-supported analysis. The study adopted a quantitative predictive research design using a purposive sample of 72 students in their last year of studying mathematics teaching within a single institution (195 task-level observations). The dependent variable, preparation level, was conceptualized at the individual level by utilizing a preparation rubric of the institution and categorized into three tertile groups: low, medium, and high. Given the hierarchical structure of the data, statistical analyses were performed using linear mixed-effects models with Student_ID as a random effect. In order to avoid leakage of information among repeated tasks from the same candidate, student-level features were extracted from the task-level observations in the predictive modeling phase. Various machine learning models were tested using stratified five-fold cross-validation with hyperparameter optimization. The findings indicated statistically significant variations between the preparation levels in terms of error rate, symbolic steps of solutions, test scores, completion times, and progress rates. The Random Forest model yielded the highest performance, with accuracy of 0.889 ± 0.064 and macro-F1 score of 0.888 ± 0.064. These results indicate that integrating features based on academic and behavioral characteristics, along with analytical features, yields an efficient approach to modeling teacher preparation level. However, it should be noted that further research and validation are required on larger datasets from different institutions.
Keywords
- prediction
- teacher preparation
- mathematics education
- machine learning
- Wolfram Alpha
- symbolic analysis
- preparation level
How to Cite
Rima Abdualiyeva, Karlygash Shetiyeva, Aigul Aldabergenova, Gulmaral Mashripkhanova, and Assem Yerkinova, "Predicting the Preparation Level of Future Mathematics Teachers Using Machine Learning Models and Wolfram Alpha Data," International Journal of Information and Education Technology, vol. 16, no. 9, pp. 2508-2515, 2026. https://doi.org/10.18178/ijiet.2026.16.9.2708
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).