Abstract
Accuracy in estimating knowledge with multiple-choice quizzes largely depends on the distractor discrepancy. The order and duration of distractor views provide significant information to itemize knowledge estimates and detect cheating. To date, a precise and accurate method for segmenting time spent for a single quiz item has not been developed. This work proposes process mining tools for test-taking strategy classification by extracting informative trajectories of interaction with quiz elements. The efficiency of the method was verified in the real learning environment where the difficult knowledge test items were mixed with simple control items. The proposed method can be used for segmenting the quiz-related thinking process for detailed knowledge examination.
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