Utilizing sensor data to model students’ creativity in a digital environment |
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| Affiliation: | 1. Professor, SKKU Business School, Sungkyunkwan University, Seoul 110-745, KOREA;1. Faculty of Medicine, Maastricht University, Maastricht, the Netherlands;2. Rett Expertise Center – Governor Kremers Center, Maastricht University Medical Center, Maastricht, the Netherlands;3. Division of Balance Disorders, Department of Otorhinolaryngology and Head and Neck Surgery, Maastricht University Medical Center, Maastricht, Netherlands;4. Faculty of Physics, Tomsk State University, Tomsk, Russia;1. Department of Radiology, Maastricht University Medical Center, PO box 5800, 6202, AZ, Maastricht, the Netherlands;2. Department of Education, Utrecht University, Heidelberglaan 1, Room E3.34, 3584, CS, Utrecht, the Netherlands;3. Maastricht University, School for Oncology and Developmental Biology (GROW), Universiteitssingel 40, 6229, ER, Maastricht, the Netherlands;4. Department of Radiology, Institut Curie, 26 rue d''Ulm, 75248, Paris Cedex 05, France;5. Department of Radiology, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584, CX, Utrecht, the Netherlands;6. Maastricht University, School of Health Professions Education, Maastricht University, PO Box 616, 6200, MD, Maastricht, the Netherlands;1. Department of Education, Utrecht University, The Netherlands;2. Welten Institute, Open University of the Netherlands, The Netherlands;3. Humanities Laboratory, Lund University, Sweden |
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| Abstract: | While creativity is essential for developing students’ broad expertise in Science, Technology, Engineering, and Math (STEM) fields, many students struggle with various aspects of being creative. Digital technologies have the unique opportunity to support the creative process by (1) recognizing elements of students’ creativity, such as when creativity is lacking (modeling step), and (2) providing tailored scaffolding based on that information (intervention step). However, to date little work exists on either of these aspects. Here, we focus on the modeling step. Specifically, we explore the utility of various sensing devices, including an eye tracker, a skin conductance bracelet, and an EEG sensor, for modeling creativity during an educational activity, namely geometry proof generation. We found reliable differences in sensor features characterizing low vs. high creativity students. We then applied machine learning to build classifiers that achieved good accuracy in distinguishing these two student groups, providing evidence that sensor features are valuable for modeling creativity. |
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| Keywords: | Creativity Student modeling Eye tracking EEG Skin conductance Intelligent Tutoring Systems |
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