Machine learning
In his research Di Mitri captures multiple modalities of the learning process real-time through wearable and contextual sensors. By annotating these multimodal data (the input space) by expert assessments or self-reports (the output space), machine learning models can be trained to predict the learning performance. This can lead to continuous formative assessment and feedback generation, which can be used to personalise and contextualise content, improve awareness and support informed decisions about learning.
Di Mitri presented a paper about his research at the Artificial Intelligence in Education 2017 conference in Wuhan (China). This conference, co-located with the Educational Data Mining 2017 conference gathered top researchers in the field of data science in education. The paper is called 'Digital Learning Projection: Learning performance estimation from multimodal learning experiences.'