The Motivational Effects of Feedback: Development of a Machine Learning Model to Predict Student Motivation from Professor Feedback

The application of feedback to enhance motivation is beneficial across various life contexts. While both feedback and motivation have been studied widely in psychological science, most of this research has used close-ended approaches to study feedback empirically, which limits the scope of investigation. The present study was one of the first applications of text-analysis to assess the impact of feedback on the recipient's motivation. A transformer machine-learning model was used to create a tool that can predict the average motivating influence of a particular feedback statement, as perceived by a recipient within an academic context. Feedback was defined and evaluated from the perspective of Feedback Intervention Theory (FIT). Both research hypotheses were supported, given that the model's motivation predictions were positively associated with the actual motivation scores of feedback statements, and the model was closer to estimating the true motivation scores than expected by chance. These findings, paired with additional exploratory analyses, demonstrated the utility and effectiveness of the model in predicting perceived student motivation from feedback statements. Thus, this research provided a reliable tool researchers and practitioners in academia could use to evaluate the motivating influence of feedback for students, and it might inspire future studies in this domain.