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Educational technologies, such as teacher dashboards, are being developed to support teachers’ instruction and students’ learning. Specifically, dashboards support teachers in providing the just-in-time instruction needed by students in complex contexts such as science inquiry. In this study, we used the Inq-Blotter teacher-alerting dashboard to investigate whether teacher support elicited by the technology influenced students’ inquiry performance in a science intelligent tutoring system, Inq-ITS. Results indicated that students’ inquiry improved after receiving teachers’ help, elicited by the Inq-Blotter alerts. This inquiry improvement was significantly greater than for matched students who did not receive help from the teacher in response to alerts. Epistemic network analyses were then used to investigate the patterns in the discursive supports provided to students by teachers. These analyses revealed significant differences in the types of support that fostered (versus did not foster) student improvement; differences across teachers were also found. Overall, this study used innovative tools and analyses to understand how teachers use this technological genre of alerting dashboards to dynamically support students in science inquiry.
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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.
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- Award ID(s):
- 1724889
- PAR ID:
- 10095368
- 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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Accepted Manuscript1.0
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