Research Group Leader in Artificial Intelligence in Education at DIPF
Information Center for Education
Rostocker Straße 6, 60323 Frankfurt am Main
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Telephone: +49 (0) 69-24708-386
Email: dimitri [at] dipf.de
Daniele Di Mitri is Research Group Leader at EduTech group at DIPF Leibniz Institute for Research and Information in Education. Daniele holds a BSc degree in Computer Science and an MSc degree in Artificial Intelligence. In 2015, he took part in the Extreme Blue excellence research programme at IBM Amsterdam. Daniele received his PhD title at the OUNL (2016-2020) in Learning Analytics and Wearable sensors support. In his PhD research, he investigates the potentials of collecting and analysing multimodal during physical interactions for automatic feedback and human behaviour analysis. His PhD project, the Multimodal Tutor, he received the Martin Wolpers Award as best PhD project in the field of Technology-Enhanced Learning 2018. He is member of the CrossMMLA Special Interest Group of the Society of Learning Analytics Research and he is the chair of the Learning Analytics Hackathon (LAKathon) series.
- Multimodal Learning Analytics
- Artificial Intelligence in Education
- Technology-Enhanced Learning
- Machine Learning
- Human Computer Interaction
- Multimodal Tutor for CPR
- Learning Pulse
- Visual Inspection Tool
- Nexus speech assistant
- Mobius - transportation mode recognition
- PhD in Learning Analyics and Wearable-Sensors Support Open University of the Netherlands, Heerlen Netherlands 2020
- Master of Science in Artificial Intelligence, Department of Data Science and Knowledge Engineering, Maastricht University 2016
- Bachelor of Science in Computer Science and Digital Communication, University of Bari, Italy 2020
Selected Publications
- Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2019). Detecting mistakes in CPR training with multimodal data and neural networks. Sensors (Switzerland), 19(14), 1–20. DOI: 10.3390/s19143099
- Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338–349. DOI: 10.1111/jcal.12288
- Di Mitri, D., Schneider, J., Trebing, K., Sopka, S., Specht, M. & Drachsler, H. (2020) Real-time Multimodal Feedback with the CPR Tutor. In: Bittencourt, I.I., Cukurova, M. & Muldner, K. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science. Springer, Cham. DOI: 10.1007/978-3-030-52237-7_12
- Di Mitri, D., Schneider, J., Specht, M., Drachsler, H. (2019) The Multimodal Learning Analytics Pipeline. In Proceedings of the Artificial Intelligence and Adaptive Education Conference - AIAED'19. Beijing, China: IEEE.
- 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
- Di Mitri D. (2018) Multimodal Tutor for CPR. In: Penstein Rosé C. et al. (eds) Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science, vol 10948. Springer, Cham. DOI: 10.1007/978-3-319-93846-2_96
- Schneider, J., Di Mitri, D., Limbu, B. & Drachsler, H. (2018). Multimodal Learning Hub: a tool for capturing customizable multimodal learning experiences. In European Conference on Technology Enhanced Learning. Springer, Cham. DOI: 10.1007/978-3-319-98572-5_4
- 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
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