Leveraging student self-reports to predict learning outcomes

Shaveen Singh

doi:10.1007/978-3-030-23207-8_73

Leveraging student self-reports to predict learning outcomes

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II
Editors	Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	398-403
Number of pages	6
ISBN (Electronic)	9783030232078
ISBN (Print)	9783030232061
DOIs	https://doi.org/10.1007/978-3-030-23207-8_73
Publication status	Published - 2019
Event	International Conference on Artificial Intelligence in Education 2019 - Chicago, United States of America Duration: 25 Jun 2019 → 29 Jun 2019 Conference number: 20th https://link.springer.com/book/10.1007/978-3-030-23204-7 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11626
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Artificial Intelligence in Education 2019
Abbreviated title	AIED 2019
Country/Territory	United States of America
City	Chicago
Period	25/06/19 → 29/06/19
Internet address	https://link.springer.com/book/10.1007/978-3-030-23204-7 (Proceedings)

Access to Document

10.1007/978-3-030-23207-8_73

Cite this

Singh, S. (2019). Leveraging student self-reports to predict learning outcomes. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II (pp. 398-403). (Lecture Notes in Computer Science ; Vol. 11626 ). Springer. https://doi.org/10.1007/978-3-030-23207-8_73

Singh, Shaveen. / Leveraging student self-reports to predict learning outcomes. Artificial Intelligence in Education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II. editor / Seiji Isotani ; Eva Millán ; Amy Ogan ; Peter Hastings ; Bruce McLaren ; Rose Luckin. Cham Switzerland : Springer, 2019. pp. 398-403 (Lecture Notes in Computer Science ).

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author = "Shaveen Singh",

year = "2019",

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editor = "Seiji Isotani and Eva Mill{\'a}n and Amy Ogan and Peter Hastings and Bruce McLaren and Rose Luckin",

booktitle = "Artificial Intelligence in Education",

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Singh, S 2019, Leveraging student self-reports to predict learning outcomes. in S Isotani, E Millán, A Ogan, P Hastings, B McLaren & R Luckin (eds), Artificial Intelligence in Education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II. Lecture Notes in Computer Science , vol. 11626 , Springer, Cham Switzerland, pp. 398-403, International Conference on Artificial Intelligence in Education 2019, Chicago, Illinois, United States of America, 25/06/19. https://doi.org/10.1007/978-3-030-23207-8_73

Leveraging student self-reports to predict learning outcomes. / Singh, Shaveen.

Artificial Intelligence in Education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II. ed. / Seiji Isotani; Eva Millán; Amy Ogan; Peter Hastings; Bruce McLaren; Rose Luckin. Cham Switzerland : Springer, 2019. p. 398-403 (Lecture Notes in Computer Science ; Vol. 11626 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AB - 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. Leveraging student self-reports to predict learning outcomes. In Isotani S, Millán E, Ogan A, Hastings P, McLaren B, Luckin R, editors, Artificial Intelligence in Education: 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II. Cham Switzerland: Springer. 2019. p. 398-403. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-030-23207-8_73

Leveraging student self-reports to predict learning outcomes

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