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Title: Sensor-Free Predictive Models of Affect in an Online Learning Environment
A significant amount of research has illustrated the impact of student emotional and affective state on learning outcomes. Just as human teachers and tutors often adapt instruction to accommodate changes in student affect, the ability for computer-based systems to similarly become affect-aware, detecting and personalizing instruction in response to student affective state, could significantly improve student learning. Personalized and affective interventions in tutoring systems can be realized through affect-aware learning technologies to deter students from practicing poor learning behaviors in response to negative affective states and to optimize the amount of learning that occurs over time. In this paper, we build off previous work in affect detection within intelligent tutoring systems (ITS) by applying two methodologies to develop sensor-free models of student affect with only data recorded from middle-school students interacting with an ITS. We develop models of four affective states to evaluate and determine significant predictors of affect. Namely, we develop a model which discerns students’ reported interest significantly better than majority class.  more » « less
Award ID(s):
1724889
PAR ID:
10095368
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the Eleventh International Conference on Educational Data Mining
Page Range / eLocation ID:
634-637
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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