<p>Evaluating User Experience (UX) is essential to understand how users interact with and perceive a system. When these evaluation techniques are applied in teaching–learning contexts, their relevance increases, as they directly influence the quality and effectiveness of the Learning Objects (LOs) with which students engage. However, traditional UX methods primarily capture subjective information, which may not accurately reflect the user’s real experience. To address this limitation, this research introduces the UXEEGET framework, which integrates established and emerging technologies, such as Electroencephalography (EEG) and Eye Tracking, with Data Science methods to achieve a more comprehensive understanding of the Learning Experience, that is, the experience derived from interacting with a Learning Object. For this purpose, EEG data, Eye Tracking metrics, and UX form responses were collected from 35 first-semester Computer Engineering students while interacting with a video-based Learning Object on Object-Oriented Programming (OOP). Several Machine Learning models (Logistic Regression, KNN, and SVM) were trained using datasets with and without biometric data (EEG and Eye Tracking) to predict students’ learning experiences, comparing these predictions with their subjective UX form responses. Results from a Wilcoxon test suggest that incorporating EEG and Eye Tracking provides a more accurate and objective understanding of the learning experience than relying solely on traditional UX instruments.</p>