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,…
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New pub: The Rise of Multimodal Tutors in Education

New pub: The Rise of Multimodal Tutors in Education

Artificial Intelligence, Book chapter, Further Education, Higher Education, Multimodal Learning Analytics, Open access, Publication
A book chapter entitled "The Rise of Multimodal Tutors in Education" written by Daniele Di Mitri, Jan Schneider & Hendrik Drachsler was published open access in the "Handbook of Open, Distance and Digital Education" edited by Olaf Zawacki-Richter and Insung Jung. Abstract This chapter describes the insights derived from the design and development of the Multimodal Tutor, a system that uses artificial intelligence to provide digital feedback and support psychomotor skills acquisition. In this chapter, we discuss the insights which we gained from eight studies: (1) an exploratory study combining physiological data and learning performance (Learning Pulse); (2) a literature survey on multimodal data for learning and a conceptual model (the Multimodal Learning Analytics Model); (3) an analysis of the technical challenges of Multimodal Learning Analytics (the Big Five Challenges);…
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