Abstract: Tinkering activity, defined as the iterative modification of code and debugging, is a key indicator of engagement and problem-solving approaches for novice programmers. In this work, we propose an interpretable machine learning model that relies on feature engineering and regression analysis to identify and measure the extent of tinkering activity in C/C++ programming environments. Based on a feature-dense dataset obtained from compiler log files, we employ binary logistic regression for categorical prediction and multiple linear regression for continuous prediction of tinkering activity. Our model is centered on interpretability, filling the gap of existing black-box models in educational analytics. The model’s key predictors, including attempt rate, corrective moves, and syntactic changes, are found to have high statistical significance. Through the integration of behavioral analytics and regression-based machine learning, this study makes a contribution to the development of intelligent tutoring systems that are capable of providing real-time, personalized feedback. The results of this study highlight the role of interpretable models in improving human-machine interaction in programming education.