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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Studying Affect Dynamics and Chronometry Using Sensor-Free Detectors
Student affect has been found to correlate with short- and long-term learning outcomes, including college attendance as well as interest and involvement in Science, Technology, Engineering, and Mathematics (STEM) careers. However, there still remain significant questions about the processes by which affect shifts and develops during the learning process. Much of this research can be split into affect dynamics, the study of the temporal transitions between affective states, and affective chronometry, the study of how an affect state emerges and dissipates over time. Thus far, these affective processes have been primarily studied using field observations, sensors, or student self-report measures; however, these approaches can be coarse, and obtaining finer grained data produces challenges to data fidelity. Recent developments in sensor-free detectors of student affect, utilizing only the data from student interactions with a computer based learning platform, open an opportunity to study affect dynamics and chronometry at moment-to-moment levels of granularity. This work presents a novel approach, applying sensor-free detectors to study these two prominent problems in affective research.
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- Award ID(s):
- 1724889
- NSF-PAR ID:
- 10095360
- Date Published:
- Journal Name:
- Proceedings of the 11th International Conference on Educational Data Mining
- Page Range / eLocation ID:
- 157-166
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Despite the prevalence and potential of K–12 engineering outreach programs, the moment‐to‐moment dynamics of outreach educators' facilitation of engineering learning experiences are understudied. There is a need to identify outreach educators' teaching moves and to explore the implications of these moves.
Purpose/Hypothesis We offer a preliminary framework for characterizing engineering outreach educators' teaching moves in relation to principles of ambitious instruction. This study describes outreach educators' teaching moves and identifies learning opportunities afforded by these moves.
Design/Method Through discourse analysis of video recordings of a university‐led engineering outreach program, we identified teaching moves of novice engineering outreach educators in interaction with elementary student design teams. We considered 18 outreach educators' teaching moves through a lens of ambitious instruction.
Results In small group interactions, outreach educators used ambitious, conservative, and inclusive teaching moves. These novice educators utilized talk moves that centered students' ideas and agency. Ambitious moves included two novel teaching moves: design check‐ins and revoicing tangible manifestations of students' ideas. Ambitious moves offered students opportunities to engage in engineering design. Conservative moves provided opportunities for students to make technical and affective progress, and to experience engineering norms.
Conclusions Our work is formative in describing engineering outreach educators' teaching moves and points to outreach educators' capability in using ambitious moves. Ambitious engineering instruction may be a useful framework for designing engineering outreach to support students' participation and progress in engineering design. Additionally, conservative teaching moves, typically considered constraining, may support productive student affect and engagement in engineering design.
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