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This study investigates the use of exit tickets as formative assessments in maths-integrated computer science (CS) lessons for grade 5 students. Exit tickets are brief surveys administered immediately after instructional activities. Using structural equation modelling (SEM), we analysed data from 1,067 students to examine the reliability and validity of exit tickets in predicting summative pre/post survey results. The study found that the exit ticket responses consistently assessed student affect at two administration points, meeting strict measurement invariance criteria (χ2(21) = 1.34, p = 1.00). Confirmatory factor analysis revealed that exit tickets predicted student self-efficacy and interest in CS, which are key educational outcomes. These findings suggest that exit tickets can be valuable tools for enhancing instructional practices and supporting student learning and engagement in CS education. The study concludes with recommendations for effectively implementing exit tickets in educational settings.
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Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts
Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors’ efficiency in managing large numbers of students. However, these technologies are not designed from students’ perspectives, nor designed for students’ own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students’ self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students’ self-regulated learning behaviors through a “scaling for empowerment” design perspective, (b) identify design guidelines to mitigate students’ concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.
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- Award ID(s):
- 1842693
- PAR ID:
- 10310022
- Date Published:
- Journal Name:
- Learning@Scale
- Format(s):
- Medium: X
- 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/10.1145/3386527.3405917
