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

Journal publications

2019

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

2018

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

Conference proceedings publications

2019

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. ResearchGate / Slideshare

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

2018

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

2017

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 / Slideshare / Video

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 Schneider, 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

2016

Di Mitri, D., Scheffel, M., Drachsler, H., Börner, D., Ternier, S., & Specht, M. (2016). Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate 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

Workshop proceedings publication

2019

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. ResearchGate / Slideshare

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. ResearchGate (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). http://crossmmla.org/?attachment_id=338

2018

Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018) Multimodal Challenge: 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. https://lakhackathon.files.wordpress.com/2018/02/lakhackathon2018_paper_5.pdf

Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018) The Big Five: Addressing 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 / Slideshare

2017

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 / Slideshare

Project deliverables

Klemke, R. (Ed.), Di Mitri, D., Limbu, B., Sharma (2018). Software Prototype with Sensor Fusion API Specification and Usage Description. WEKIT project deliverable D3.6.

Klemke, R. (Ed.), 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.

Presentations

Di Mitri, D. (2019, 6 March). Read Between The Lines. Paper Presentation at the 9th Learning Analytics & Knowledge conference 2018, Tempe, Arizona, United States. / Slideshare

Di Mitri, D. (2017, 15 March). Learning Pulse. Paper Presentation at the 7th Learning Analytics & Knowledge conference 2017, Vancouver, Canada.

Di Mitri, D. (2017, 14 March). Digital Learning Projection: Learning State Estimation From Multimodal Learning Experiences. Presentation at the Doctoral Consortium of the Learning Analytics & Knowledge conference 2017. Vancouver, Canada.

Klemke, R., Limbu, B., Di Mitri, D., & Schneider, J. (2017, 27 November). Wearable Experience for Knowledge Intensive Training. Presentation to visitors from Open University of Shanghai. Heerlen, The Netherlands.

Lectures

Di Mitri, D. (2019, 4th April) Multimodal Tutors: Enhancing learning using multimodal data and machine learning. Guest Lecture to the Adaptive Learning Lab of the Behavioural Science Institute, Radboud University Nijmegen, The Netherlands.

Di Mitri, D. (2018, 24 November) Multimodal Tutors: Enhancing learning using multimodal data and machine learning. OU Informatica StudieDag, Eindhoven, The Netherlands.

Di Mitri, D. (2018, 22 January) Multimodal Learning Analytics: 
an IoT approach for social signals. Guest lecture at Hogeschool Zuyd, Internet of Things Course Heerlen, The Netherlands.

Workshops and interactive sessions

Di Mitri, D. & Schneider, J. (2019, 6th June) Multimodal Tutor. Thematic workshop at the 15th EATEL Summer School in Technology Enhanced Learning (JTEL), Bari, Italy. / Slideshare

Di Mitri, D. & Specht, M. (2019, 8 April) Multimodal Learning Analytics parallel workshop and demonstration. Learning Analytics in Higher Education Conference, Delft Technical University Delft, The Netherlands. / Slideshare

Di Mitri, D. & Schneider, J. (2018, 15 May) Sensors for Learning, Thematic workshop at the 14th EATEL Summer School in Technology Enhanced Learning (JTEL), Dürres, Albania. / Slideshare

Di Mitri, D. & Schneider, J. (2017, 12 October) Multimodal Machines, Thematic workshop at the 13th EATEL Summer School in Technology Enhanced Learning (JTEL), Aveiro, Portugal. / Slidehsare

Awards

  • Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2019) Multimodal Pipeline: A generic approach for handling multimodal data forsupporting learning. Best Paper Award at AIMA4EDU workshop at IJCAI'19.
  • Schneider, J., Di Mitri, D., Drachsler, H. (2019) Best Demonstration Award at LAK19 with "Multimodal Tutor Builder Kit".
  • Di Mitri, D. (2018, 9 September) Martin Wolpers Award to the most young promising researcher at the 14th EATEL Summer School on Technology-Enhanced Learning.  Martin Wolpers’ Award, funded by Research Institute for Innovation & Technology in Education (UNIR iTED) at Universidad Internacional de La Rioja.

Courses

February 2019 - ICT-ILO Course on Multimodal Learning Experiences

Interviews

March 2019 - Hackademia Podcast Interview - What are Multimodal Learning Analytics?

Reviewing experience

Conferences

  • Learning Analytics & Knowledge conference (LAK'17) 2017
  • Learning Analytics & Knowledge conference (LAK'18) 2018
  • Learning Analytics & Knowledge conference (LAK'19) 2019
  • Technology Enhanced Learning conference (EC-TEL'17) 2017
  • Technology Enhanced Learning conference (EC-TEL'18) 2018
  • Technology Enhanced Learning conference (EC-TEL'19) 2018
  • Computer-Human Interaction conference (CHI'19) 2019
  • Artificial Intelligence in Education (AIED'19) 2019

Journals

  • PLOS ONE
  • IEEE Transactions on Cognitive and Developmental Systems
  • International Journal of Serious Games IJSG 2019
  • Journal of Computer Assisted Learning, JCAL 2018 Special Issue
  • Transaction on Learning Technologies 2019