Increasing school dropout rates are a problem in many educational systems, with student disengagement being one significant factor. Learning analytics is a new field with a key role in educational institutions in the coming years. It may help make strategic decisions to reduce student disengagement. The use of technology in educational environments has grown significantly and, with it, awareness of the importance of student engagement. We exploit tracking and wearable technologies to increase user engagement in learning processes, exploring also the area of multimodal learning analytics (MMLA). We use wearables and Internet of Things for education, an interactive and collaborative system designed to improve motivation and learning. This article presents the results obtained in different experiments conducted in a secondary school in a long-term participatory learning context. The captured data were analyzed and used to identify different students' behavior patterns, showing their progress and motivation. Subsequently, from the captured data and aiming at a decision-making phase, we used machine learning techniques and MMLA methodologies to construct models able to 'explain' when student engagement is present, so this knowledge can later be exploited. In particular, we chose decision trees and rule systems based on a set of variables with proven relevance to the problem. The evaluation of this novel engagement classification system confirms the high performance of these variables. The rules obtained, which can be easily interpreted by a nonexpert, help the teacher to observe, analyze, and make decisions with the purpose of fostering engagement.