Authors:
Mathias Bauer
;
Cassandra Bräuer
;
Jacqueline Schuldt
and
Heidi Krömker
Affiliation:
Technische Universität Ilmenau, Germany
Keyword(s):
Adaptive E-Learning, Motivation, Self-Regulated Learning, Sensory Data, Technology Enhanced Learning and Teaching, Wearable Technology.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
e-Learning
;
e-Learning Hardware and Software
;
e-Learning in Engineering Education
;
e-Learning Platforms
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Intelligent Tutoring Systems
;
Simulation and Modeling
;
Simulation Tools and Platforms
Abstract:
Surveys show besides the number of students also the drop-out rates are increasing, especially in early phases of studying natural or engineering sciences. The research project “SensoMot - Sensor Measures of Motivation for Adaptive Learning” tries to counter this development by means of improving the quality of teaching in the department of micro technology with the help of an adaptive e-learning system. For that purpose, the mediated learning content should be better adapted to the individual prior knowledge, competencies and motivational profiles of the learners. Furthermore, the continuous sensory data acquisition of physiological parameters of the learner shall be accomplished by current wearable technology. The paper presents first results in the form of conceptual determinations concerning self-reports and physiological measures, instructional design and adaptation techniques and further includes the early involvement of the subsequent users in the development process through a
n iterative, formative evaluation of prototypical solutions.
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