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ERIC Number: ED537205
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 38
Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra
Baker, Ryan S. J. d.; Gowda, Sujith M.; Wixon, Michael; Kalka, Jessica; Wagner, Angela Z.; Salvi, Aatish; Aleven, Vincent; Kusbit, Gail W.; Ocumpaugh, Jaclyn; Rossi, Lisa
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in research attempting to understand the root causes and impacts of different forms of affect. However, current approaches to affect detection have largely relied upon sensor systems, which are expensive and typically not physically robust to classroom conditions, reducing their potential real-world impact. Work towards sensor-free affect detection has produced detectors that are better than chance, but not substantially better--especially when subject to stringent cross-validation processes. In this paper we present models which can detect student engaged concentration, confusion, frustration, and boredom solely from students' interactions within a Cognitive Tutor for Algebra. These detectors are designed to operate solely on the information available through students' semantic actions within the interface, making these detectors applicable both for driving interventions and for labeling existing log files in the PSLC DataShop, facilitating future discovery with models analyses at scale. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation; Bill and Melinda Gates Foundation
Authoring Institution: International Educational Data Mining Society