Modeling affect in student-driven learning scenarios
Added Title
International Conference on Educational Data Mining (6th : 2013)
EDM 2013
College
College of Computer Studies
Department/Unit
Advance Research Institute for Informatics, Computing and Networking
Document Type
Conference Proceeding
Source Title
Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013
Publication Date
1-1-2013
Abstract
Much research has been done on affect detection in learning environments because it has been reported to provide better interventions to support student learning. However, students’ actions inside these environments are limited by the system’s interface and the domain it was designed for. In this research, we investigated a learning environment wherein students had full control over their activities and they had to manage their own goals, tasks and affective states. We identified features that would describe students’ learning behavior in this kind of environment and used them for building affect models. Our results showed that although a general affect model with acceptable performance could be created, user-specific affect models seemed to perform better. © 2013 International Educational Data Mining Society. All rights reserved.
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Recommended Citation
Inventado, P. B., Legaspi, R. S., Cabredo, R., & Numao, M. (2013). Modeling affect in student-driven learning scenarios. Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013 Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/3875
Disciplines
Computer Sciences
Keywords
Affect (Psychology); Data mining; Self-culture
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