New Pub: Emotional and motivational effects of automated and personalized feedback

New Pub: Emotional and motivational effects of automated and personalized feedback

Computer-supported collaborative learning, Empirical Study, Feedback, Higher Education, Journal, Learning Analytics, New Pub, Open access
With increasingly large student numbers, providing personalized teacher feedback becomes untenable. On the other hand, providing students feedback about their work is an integral part of ensuring student support throughout their learning trajectory. Fortunately, Learning Analytics now makes it feasible to automatically deploy feedback to many students at once. However, the design of effective feedback still remains an area of investigation Joshua Weidlich, Aron Fink, Ioana Jivet, Jane Yau, Tornike Giorgashvili, Hendrik Drachsler, and Andreas Frey's recently published paper in the Journal of Computer-Assisted Learning focused on one key design feature: the reference frame. Any feedback content must be formulated in reference to some performance level, be it the average of the student group (social comparison), the desired performance level (criterion-referenced comparison), or past performance. A longstanding literature on this…
Read More
Artificial Intelligence Systems for Feedback Generation at JTEL’23

Artificial Intelligence Systems for Feedback Generation at JTEL’23

Summer School, Workshop
How Can AI Help To Learn Better? A Recap of the Workshop on Feedback Generation At jTEL Summer School 2023 in La Manga, Spain, we hosted the "Artificial Intelligence Systems for Feedback Generation" workshop. The workshop was attended by a diverse group of participants who are young researchers in Technology Enhanced learning and interested in how artificial intelligence (AI) can enhance their learning experience. The workshop was led by Daniele Di Mitri, Jan Schneider, Roland Klemke and Bibeg Limbu, all experts in AI in education who shared their insights and experiences with us. What is AI in education? AI in education (AIED) is a research field that explores how to use AI techniques and technologies to support learners with individualized feedback, guidance, and processes. AIED can help learners achieve learning…
Read More
New Pub: Multimodal Learning Experience for Deliberate Practice

New Pub: Multimodal Learning Experience for Deliberate Practice

Book chapter
A new book chapter has been published as part of the Multimodal Learning Analytics Handbook edited by Springer. While digital education technologies have improved to make educational resources more available, the modes of interaction they implement remain largely unnatural for the learner. Modern sensor-enabled computer systems allow extending human-computer interfaces for multimodal communication. Advances in Artificial Intelligence allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support deliberate practice with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This chapter elaborates on the approaches, architectures, and methodologies in five different use cases that use multimodal learning analytics applications for deliberate practice. Di Mitri, D., Schneider, J., Limbu, B., Mat Sanusi, K.A., Klemke, R. (2022). Multimodal Learning Experience for Deliberate Practice. In: Giannakos,…
Read More