Motivation plays a significant role in shaping students’ educational outcomes. Understanding the factors that influence student motivation is crucial for enhancing academic performance and designing effective learning environments. This study utilizes to examine various types of motivation, aiming to develop an integrated framework for analyzing and predicting student motivation. The proposed framework employs multi-source data and evaluates three machine learning techniques: Random Forest, Stacking Classifier, and Voting Classifier. These models are applied to data collected from a UK-based institution, specifically from the Computer Science department. The findings highlight the superior performance of the Random Forest model in identifying learning analytics characteristics that influence each motivation type, achieving an accuracy of 0.9950. The Stacking Classifier model also demonstrates strong performance, with an accuracy of 0.9900, while the Voting Classifier model achieves an accuracy of 98.00%. Additionally, this study explores the underlying philosophy of using Kaggle data and its features to classify student motivation, supporting the effectiveness of these machine learning techniques in this context. While the results are promising within the context of a single-institution Computer Science setting, further studies are needed to validate the applicability of this methodology across broader educational frameworks.

Keywords:motivation, machine learning, learning analytics, prediction, Random Forest Classifier, Stacking classifier, voting classifier, explainable AI in education.