Daniele Di Mitri

Postdoctoral Researcher in Artificial Intelligence

Open Universiteit
Faculteit Onderwijswetenschappen
Leren en innoveren met ICT / Centre For Actionable Research of the Open University



  • learning analytics
  • multimodal data
  • intelligent tutoring
  • artificial intelligence
  • machine learning

Expert Statement

In my research, I investigate the potentials of collecting and analysing multimodal during physical and distance learning activities. These data are collected through wearable sensors and internet of things devices and can capture modalities such as hands movement, gaze, gestures or physiological information like heart rate or brain waves. The multimodal data integrated with information about the learning context and activity, are used as input for machine learning models for automatic feedback and human behaviour analysis. 

Voorbeeld van relevante publicatie

Di Mitri D, Schneider J, Specht M, Drachsler H. Detecting Mistakes in CPR Training with Multimodal Data and Neural Networks. Sensors. 2019; 19(14):3099. DOI: 10.3390/s19143099

Di Mitri D, Schneider J, Specht M, Drachsler H. From signals to knowledge: A conceptual model for multimodal learning analytics. J Comput Assist Learn. 2018;34:338–349. DOI: 10.1111/jcal.12288

Di Mitri D., Schneider J., Specht M., Drachsler H. (2019) Read Between the Lines: An Annotation Tool for Multimodal Data for Learning. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge – LAK19 (pp. 51–60). New York, NY, USA: ACM. DOI: 10.1145/3303772.3303776 / Slideshare

Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2017). Learning Pulse: a Machine Learning Approach for Predicting Performance in Self-Regulated Learning Using Multimodal Data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference 2017 (LAK ’17) (pp. 188-197). New York, NY, USA. ACM. DOI: 10.1145/3027385.3027447 https://dl.acm.org/doi/10.1145/3303772.3303776





Nederlands, Engels