The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

Research output: ThesisDoctoral ThesisInternal (IDIP)

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Abstract

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks.
Original languageEnglish
QualificationPhD
Awarding Institution
Supervisors/Advisors
  • Drachsler, Hendrik, Supervisor
  • Specht, Marcus, Co-supervisor
  • Schneider, Dr. J., Co-supervisor, External person
Award date4 Sep 2020
Place of PublicationHeerlen
Publisher
Print ISBNs978-94-93211-21-6
Publication statusPublished - 4 Sep 2020

Keywords

  • multimodal data
  • learning analytics
  • intelligent tutoring systems
  • sensor-based learning
  • adaptive feedback
  • CPR Tutor

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