A Bayesian Classification Approach for Handling Uncertainty in Adaptive E-Assessment

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New technologies have provided the educational field with innovations that allow significant improvement in the teaching learning process. The development of information technology has increased the popularity of Web based education, ; particularly E-assessment This paper provides an overview of Bayesian Network and its application to handle uncertainty in adaptive E-assessment. An adaptive assessment system was realized in PHP and MYSQL. The empirical data was collected and the relationship between the response time and the assessment grading was established. The classifier accuracy obtained using Bayesian classifier was compared with C4.5 Decision tree algorithm and Naïve Bayes classifier. The superiority of Bayesian algorithm over the well known algorithms in handling uncertainty of adaptive assessment data is established in this paper.
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Adaptive Assessment; Degree of Toughness (DT); Bayesian Network; Naive Bayes Classifier; Classifier Accuracy; F-Measure; Area Under Curve (AUC)

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