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Bayesian Networks in Educational Assessment

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Adaptive Content with Evidence-Based Diagnosis (ACED)

Val Shute and Russell Almond

ACED was an NSF sponsored assessment "for" learning system that had the following features:

  • ACED Model was constructed using Evidence-centered assessment design (ECD) methodology.
  • Proficiency Model based on expert analysis of domain (Geometric series).
  • Q-Matrix was augmented to included prior opinion about item difficulty.
  • Accessible Content? -- Tasks were designed to be presented either on-screen or in special touch-graphics mode for persons with low visual acuity.
  • Elaborated task-specific feedback? was available for all items.
  • Bayesian network scoring engine?
  • Weight-of-evidence? algorithm for adaptive task selection
  • Evaluation a randomized trial with 268 middle school students showed that
    • ACED (with adaptivity and elaborated feedback) produced significant gains between the pre-test and post-test.
    • ACED scores correlated well with post-test scores, and the correlation "'improved'" when elaborated feedback was given.

This Wiki has the following details about ACED:

  • People -- Who worked on the project
  • Model -- Description of the model.
  • Data -- Description of the data collected from the experiment, and how to gain access.
  • Publications -- A list of reports that can provide additional information.
  • ACED BN in R -- A re-analysis of the ACED data using a Bayesian Network using the gRain and bnlearn packages in R

HomePage ACED Data Model People Publications

ACED development and data collection was sponsored by National Science Foundation Grant No. 0313202.

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Page last modified on August 04, 2016, at 09:14 PM