Assessing Learner’s Scientific Inquiry Skills Across Time: A Dynamic Bayesian Network Approach

  • Choo-Yee Ting
  • Mohammad Reza Beik Zadeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


In this article, we develop and evaluate three Dynamic Bayesian Network (DBN) models for assessing temporally variable learner scientific inquiry skills (Hypothesis Generation and Variable Identification) in INQPRO learning environment. Empirical studies were carried out to examine the matching accuracies and identify the models’ drawbacks. We demonstrate how the insights gained from a preceding model have eventually led to the improvement of subsequent models. In this study, the entire evaluation process involved 6 domain experts and 61 human learners. The matching accuracies of the models are measured by (1) comparing with the results gathered from the pretest, posttest, and learner’s self-rating scores; and (2) comments given by domain experts based on learners’ interaction logs and the graph patterns exhibited by the models.


Scientific Inquiry Skills Dynamic Bayesian Networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Choo-Yee Ting
    • 1
  • Mohammad Reza Beik Zadeh
    • 2
  1. 1.Faculty of Information Technology, Multimedia University, 63100 CyberjayaMalaysia
  2. 2.Faculty of Engineering, Multimedia University, 63100 CyberjayaMalaysia

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