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Leveraging Non-cognitive Student Self-reports to Predict Learning Outcomes

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

Metacognitive competencies related to cognitive tasks have been shown to be a powerful predictor of learning. However, considerably less is known about the relationship between student’s metacognition related to non-cognitive dimensions, such as their affect or lifestyles, and academic performance. This paper presents a preliminary analysis of data gathered by Performance Learning Education (PL), with respect to students’ self-reports on non-cognitive dimensions as possible predictors of their academic outcomes. The results point to the predictive potential of such self-reports, to the importance of students exercising their self-understanding during learning, and to the potentially critical role of incorporating such student’s self-reports in learner modelling.

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Correspondence to Kaśka Porayska-Pomsta .

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Porayska-Pomsta, K., Mavrikis, M., Cukurova, M., Margeti, M., Samani, T. (2018). Leveraging Non-cognitive Student Self-reports to Predict Learning Outcomes. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_86

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_86

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93845-5

  • Online ISBN: 978-3-319-93846-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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