Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines

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  • Author(s): Matayoshi, Jeffrey; Uzun, Hasan; Cosyn, Eric
  • Language:
    English
  • Source:
    International Educational Data Mining Society. 2022.
  • Publication Date:
    2022
  • Document Type:
    Speeches/Meeting Papers
    Reports - Research
  • Additional Information
    • Availability:
      International Educational Data Mining Society. e-mail: [email protected]; Web site: https://educationaldatamining.org/conferences/
    • Peer Reviewed:
      Y
    • Physical Description:
      7
    • Education Level:
      Junior High Schools
      Middle Schools
      Secondary Education
      Higher Education
      Postsecondary Education
    • Descriptors:
      Knowledge Level
      Mathematical Models
      Learning Experience
      Comparative Analysis
      Learning Management Systems
      Classification
      Artificial Intelligence
      Student Evaluation
      Computer Assisted Testing
      Middle School Students
      Chemistry
      Science Projects
      Undergraduate Students
      Algebra
      Science Achievement
      Mathematics Achievement
      Recall (Psychology)
      Accuracy
    • Abstract:
      Knowledge space theory (KST) is a mathematical framework for modeling and assessing student knowledge. While KST has successfully served as the foundation of several learning systems, recent advancements in machine learning provide an opportunity to improve on purely KST-based approaches to assessing student knowledge. As such, in this work we compare the performance of an existing KST-based adaptive assessment to that of a newly developed version--with this new version combining the predictive power of a neural network model with the strengths of existing KST-based approaches. Using a cluster randomized experiment containing data from approximately 140,000 assessments, we show that the new neural network assessment engine improves on the performance of the existing KST version, both on standard classification metrics, as well as on measures more specific to the student learning experience. [For the full proceedings, see ED623995.]
    • Abstract:
      As Provided
    • Publication Date:
      2022
    • Accession Number:
      ED624168