Leveraging student self-reports to predict learning outcomes

Shaveen Singh

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    1 Citation (Scopus)


    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 languageEnglish
    Title of host publicationArtificial Intelligence in Education
    Subtitle of host publication20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part II
    EditorsSeiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
    Place of PublicationCham Switzerland
    Number of pages6
    ISBN (Electronic)9783030232078
    ISBN (Print)9783030232061
    Publication statusPublished - 2019
    EventInternational Conference on Artificial Intelligence in Education 2019 - Chicago, United States of America
    Duration: 25 Jun 201929 Jun 2019
    Conference number: 20th
    https://link.springer.com/book/10.1007/978-3-030-23204-7 (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceInternational Conference on Artificial Intelligence in Education 2019
    Abbreviated titleAIED 2019
    Country/TerritoryUnited States of America
    Internet address

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