Abstract
Academic performance is typically measured through assessments on standardised tests. However, considerably less is known about the relationship between students self-assessment (metacognition and affective states) captured during the reading process and their academic performance. This paper presents a preliminary analysis of data gathered during a blended course offering using student self-reports on learning material as predictor of their academic outcomes. The results point to the predictive potential of such self-reports and the potentially critical role of incorporating such student self-reports in learner modelling and for driving teaching interventions.
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Singh, S. (2019). Leveraging Student Self-reports to Predict Learning Outcomes. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_73
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DOI: https://doi.org/10.1007/978-3-030-23207-8_73
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