Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes

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  • Additional Information
    • Availability:
      Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: [email protected]; Web site: http://learning-analytics.info/journals/index.php/JLA/
    • Peer Reviewed:
      Y
    • Source:
      22
    • Sponsoring Agency:
      National Science Foundation (NSF)
    • Contract Number:
      DRL1031398
    • Education Level:
      Secondary Education
      Middle Schools
      Junior High Schools
    • Subject Terms:
      Affective Behavior; Student Behavior; Learner Engagement; Web Based Instruction; Educational Technology; Technology Uses in Education; Tutoring; High Stakes Tests; Mathematics Tests; Longitudinal Studies; Probability; Problem Solving; Correlation; Scaffolding (Teaching Technique); Models; Predictor Variables; Standardized Tests; Scores; Performance; Mathematics Instruction; Middle School Students; Urban Schools; Observation; Coding
    • Subject Terms:
      Massachusetts
    • Subject Terms:
      Massachusetts Comprehensive Assessment System
    • ISSN:
      1929-7750
    • Abstract:
      In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate student affective states and behaviour based on post-hoc analysis of tutor log-data. For every student action in the tutor, the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, or frustration, and estimates of the probability that the student is exhibiting off-task or gaming behaviours. We used data from the ASSISTments math tutoring system and found that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and, surprisingly, frustration are both associated with positive learning outcomes. In a second analysis, we build a unified model that predicts student standardized examination scores from a combination of student affect, disengaged behaviour, and performance within the learning system. This model achieves high overall correlation to standardized exam score, showing that these types of features can effectively infer longer-term learning outcomes.
    • Abstract:
      As Provided
    • Number of References:
      28
    • Publication Date:
      2017
    • Accession Number:
      EJ1127034