This is the most up-to-date list of my research experience. You can also find me in other Academic exchange platforms:

Daniele counts 32 publications, 341 citations and 10 H-index starting from 2014, (Google Scholar, 21th December 2021).

Journal publications

  • Di Mitri, D., Schneider, J., & Drachsler, H. (2021). Keep Me in the Loop: Real-Time Feedback with Multimodal Data. International Journal of Artificial Intelligence in Education. doi: 10.1007/s40593-021-00281-z
  • Mavrikis, M., Cukurova, M., Di Mitri, D., Schneider, J., Drachsler, H. (2021). A short history, emerging challenges and co-operation structures for Artificial In- telligence in education. Bildung und Erziehung, 74(3), 249-263. doi: 10.13109/buer.2021.74.3.249
  • Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? - A Systematic Literature Review on Chatbots in Education. Frontiers in Artificial Intelligence, 4. doi: 10.3389/frai.2021.654924
  • 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. (Most-cited publication)

Conference publications

  • Di Mitri, D. (2021). Restoring Context in Online Teaching with Artificial Intelli- gence and Multimodal Learning Experiences. In E. Langran & D. Rutledge (Eds.), Proceedings of SITE Interactive Conference (pp. 494-501). Online, United States: Association for the Advancement of Computing in Education (AACE). Retrieved December 6, 2021 from https://www.learntechlib.org/primary/p/220376/.
  • Ciordas-Hertel, G.-P., Rödling, S., Schneider, J., Di Mitri, D., Weidlich, J., & Drach- sler, H. (2021). Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning. Sensors, 21(19), 6649. doi: 10.3390/s21196649.
  • Karademir O., Ahmad A., Schneider J., Di Mitri D., Jivet I., Drachsler H. (2022). Designing the Learning Analytics Cockpit - A Dashboard that Enables Interven- tions. In: De la Prieta F. et al. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 11th International Conference. MIS4TEL 2021. Lecture Notes in Networks and Systems, vol 326. Springer, Cham. doi: 10.1007/978-3-030-86618-1_10.
  • Buraha T., Schneider J., Di Mitri D., Schiffner D. (2021). Analysis of the “D’oh!” Moments. Physiological Markers of Performance in Cognitive Switching Tasks. In: De Laet T., Klemke R., Alario-Hoyos C., Hilliger I., Ortega-Arranz A. (eds) Technology-Enhanced Learning for a Free, Safe, and Sustainable World. EC-TEL 2021. Lecture Notes in Computer Science, vol 12884. Springer, Cham. doi: 10.1007/978-3-030-86436-1_11.
  • Mat Sanusi, K. A., Di Mitri, D., Limbu, B., & Klemke, R. (2021). Table Ten- nis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks. Sensors, 21(9), 3121. doi: 10.3390/s21093121
  • Di Mitri D., Asyraaf Mat Sanusi K., Trebing K., Bromuri S. (2021). MOBIUS: Smart Mobility Tracking with Smartphone Sensors. In: Paiva S., Lopes S.I., Zitouni R., Gupta N., Lopes S.F., Yonezawa T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. doi: 10.1007/978-3-030-76063-2_31 (video) (slides) (best paper award)
  • 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 (slides)
  • Di Mitri, D. , Schneider, J., Specht, M., Drachsler, H. (2019). The Multimodal Learning Analytics Pipeline. In Proceedings of the Artificial Intelligence and Adap- tive Education Conference – AIAED’19. Beijing, China: IEEE. ResearchGate (slides)
  • 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 (slides)
  • Guest, W., Wild, F., Di Mitri, D., Klemke, R., Karjalainen, J., & Helin, K. (2019, December). Architecture and design patterns for distributed, scalable augmented reality and wearable technology systems. In 2019 IEEE International Conference on Engineering, Technology and Education (TALE) (pp. 1-8). IEEE. doi: 10.1109/TALE48000.2019.9225855.
  • 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 Learn- ing 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 Sev- enth International Learning Analytics & Knowledge Conference 2017 (LAK ’17) (pp. 188-197). New York, NY, USA. ACM. doi: 10.1145/3027385.3027447 (video)(slides)
  • Guest, W., Wild, F., Vovk, A., Fominykh, M., Limbu, B., Klemke, R. Sharma, P., Karjalainen, J., Smith, C., Rasool, J., Aswat, S., Helin, K., Di Mitri, D., and Schnei- der, J. (2017) Affordances for Capturing and Re-enacting Expert Performance with Wearables. In: Lavoué É., Drachsler H., Verbert K., Broisin J., Pérez-Sanagustín M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science, vol 10474. Springer, Cham. doi: 10.1007/978-3-319-66610-5_34

Workshop publications

  • Buraha, T., Schneider, J., Di Mitri, D. (2021) In Spikol, D. et al. (Eds.), Pro- ceedings of the Third Multimodal Learning Analytics Across (Physical and Digital) Spaces (CrossMMLA).
  • Di Mitri, D. (2019) Detecting Medical Simulation Errors with Machine learning and Multimodal Data. Doctoral Consortium of the Seventeenth Conference of Artificial Intelligence in Medicine (AIME’19). Poznań, Poland. (slides)
  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2019) Multimodal Pipeline: A generic approach for handling multimodal data forsupporting learning. First workshop on AI-based Multimodal Analytics for Understanding Human Learning in Real-world Educational Contexts (AIMA4EDU) IJCAI’19 Macau, China (best paper award).
  • Schneider, J., Di Mitri, D., Specht, M., & Drachsler, H. (2019) Multimodal Learning Analytics Runtime Framework. In Spikol, D. et al. (Eds.), Proceedings of the Third Multimodal Learning Analytics Across (Physical and Digital) Spaces (CrossMMLA).
  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018) Multimodal Chal- lenge: Analytics Beyond User-computer Interaction Data. In Pardo, A., Bartimote, K., Lynch, G., Buckingham Shum, S., Ferguson, R., Merceron, A., & Ochoa, X. (Eds.). (2018). Companion Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 362-365). Sydney, Australia: Society for Learning Analytics Research.
  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018) The Big Five: Ad- dressing Recurrent Multimodal Learning Data Challenges. In R. Martinez-Maldonado et al. (Eds.), Proceedings of the Second Multimodal Learning Analytics Across (Physical and Digital) Spaces (CrossMMLA), Vol. 2163. CEUR Proceedings. http://ceur-ws.org/Vol-2163/#paper6
  • Di Mitri, D. (2017). Digital Learning Projection. Learning performance estimation from multimodal learning experiences. In E. André, R. Baker, X. Hu, Ma. M.T. Rodrigo, & B. du Boulay (Eds.), Proceedings of AIED 2017, 18th International Conference on Artificial Intelligence in Education (pp. 609–612). Wuhan, China: Springer International Publishing. doi: 10.1007/978-3-319-61425-0 (slides)
  • Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2016). Learning Pulse: Using Wearable Biosensors and Learning Analytics to In- vestigate and Predict Learning Success in Self-regulated Learning. In R. Martinez- Maldonado & D. Hernandez-Leo (Eds.), Proceedings of the First International Workshop on Learning Analytics Across Physical and Digital Spaces, Vol. 1601 (pp. 34-39): CEUR Proceedings. http://ceur-ws.org/Vol-1601/CrossLAK16Paper7.pdf

Books & Collections

  • Di Mitri, D., Martinez-Maldonado, R., Santos, O., Schneider, J., Asyraaf Mat Sanusi, K., Cukurova, M., Spikol, D., Molenaar, I., Michail, G., Klemke, R. & Azevedo, R. (2021) Proceedings of the 1st International Workshop on Multimodal Artificial Intelligence in Education (MAIEd’21). At the 22nd International Con- ference on Artificial Intelligence in Education (AIED 2021). Online, Utrecht, The Netherlands. CEUR-WS.org/Vol-2902, ISSN 1613-0073.
  • Giannakos, M., Spikol, D., Di Mitri, D., Sharma, K., Ochoa, X., Hammad, R. (2022) Multimodal Learning Analytics Handbook. Springer Nature. In preparation.
  • Di Mitri, D. (2020). The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences. PhD dissertation. Open Universiteit. OU Digital Library. ISBN: 978-94-93211-21-6

Book chapters

  • Di Mitri, Schneider, J., Drachsler, H. (2021) The Rise of Multimodal Tutors in Education: Insights from Recent Research. In review. To appear in Handbook of Open, Distance, and Digital Education. in Springer’s Major Reference Works Series.
  • Di Mitri, D., Limbu, B., Schneider, J., Klemke, R. (2021) Multimodal Learning An- alytics For Deliberate Practice. Accepted. To appear in The Multimodal Learning Analytics Handbook. Springer Nature.
  • Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H. (2020) Der multimodale Lern- Hub: Ein Werkzeug zur Erfassung individualisierbarer und sensorgestützter multi- modaler Lernerfahrungen. In Digitale Bildung und Künstliche Intelligenz in Deutsch- land (pp. 537-557). Springer, Wiesbaden.

Technical reports

  • Klemke, R., Di Mitri, D., Limbu, B., Sharma (2018). Software Prototype with Sensor Fusion API Specification and Usage Description. WEKIT project deliverable D3.6.
  • Klemke, R., Di Mitri, D., Limbu, B., Schneider, J., Sharma, P., Wild, F., Azam, T. (2017, 4 July). Software Prototype with Sensor Fusion API Specification and Usage Description. WEKIT project deliverable D3.3.