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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.
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From Reaction to Anticipation: Predicting Future Affect
The educational data mining community has extensively investigated affect detection in learning platforms, finding associations between affective states and a wide range of learning outcomes. Based on these insights, several studies have used affect detectors to create interventions tailored to respond to when students are bored, confused, or frustrated. However, these detector-based interventions have depended on detecting affect when it occurs and therefore inherently respond to affective states after they have begun. This might not always be soon enough to avoid a negative experience for the student. In this paper, we aim to predict students' affective states in advance. Within our approach, we attempt to determine the maximum prediction window where detector performance remains sufficiently high, documenting the decay in performance when this prediction horizon is increased. Our results indicate that it is possible to predict confusion, frustration, and boredom in advance with performance over chance for prediction horizons of 120, 40, and 50 seconds, respectively. These findings open the door to designing more timely interventions.
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- Award ID(s):
- 1917545
- PAR ID:
- 10546433
- Editor(s):
- Benjamin, Paaßen; Carrie, Demmans Epp
- Publisher / Repository:
- International Educational Data Mining Society
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5281/zenodo.12729885
