Workshop @ JTELSS – Artificial Intelligence in Education and Multimodal Learning Experience

Workshop @ JTELSS – Artificial Intelligence in Education and Multimodal Learning Experience

Workshop
At this year’s JTEL summer school in Halkidiki, Greece (see previous blog post here), Daniele Di Mitri and Jan Schneider together with Prof Roland Klemke and Dr Bibeg Limbu, contributed in a mini-track on Artificial Intelligence in Education. The mini-track started with the session "Artificial Intelligence in Education, Multimodal Learning Experience and Ethics of AI (MAIED)". The purpose of this session was to provide an overview of all the topic of AI in Education and Multimodal Learning Experiences. The workshop started with a lecture style presentation from the presenters on AI in Education, Multimodality, theories behind Multimodal Learning Experiences and application use cases. https://twitter.com/dimstudi0/status/1529012671495868416 Thus the workshop included a pitch-style presentation of the PhD research by all the PhD candidates at the summer school involved in the field of AI in…
Read More
Fernando P. Cardenas-Hernandez joins the team

Fernando P. Cardenas-Hernandez joins the team

Multimodal Learning Analytics, Project, Team
Starting 1st July 2021, Fernando P. Cardenas-Hernandez joins the team as a doctoral researcher.  He earned his Master’s degree in Microsystems from the University of Freiburg. After his graduation, he worked as a software engineer in different companies. Some of his previous projects made use of microcontrollers, SBCs and thermal & industrial cameras. He is currently involved in the MILKI-PSY project.
Read More
New Pub: Towards Automatic Collaboration Analytics for Group Speech Data Using Multimodal Learning Analytics

New Pub: Towards Automatic Collaboration Analytics for Group Speech Data Using Multimodal Learning Analytics

General education, Journal, Multimodal Learning Analytics, Open access, Publication
Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters…
Read More