Caveon Webinar Series: Using Decision Theory for Accurate Pass/Fail Decisions
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The traditional approach to classification testing is extremely inefficient and often difficult to implement in applied settings. Typically, examinees are rank ordered either through Item Response ...
The traditional approach to classification testing is extremely inefficient and often difficult to implement in applied settings. Typically, examinees are rank ordered either through Item Response Theory or Classical Test Theory, and then scores are compared to a difficult-to-define cut score.
This webinar will introduce the use of decision theory which basically asks: “Does this response pattern look like the response pattern of a master or a non-master?” This simpler model has major advantages over IRT and CTT:
1. Only a small sample of clear masters and a small sample of clear non-masters are needed to calibrate questions.
2. There are no assumptions for unidimensionality, and normal distribution or requirement for monotonically increasing probabilities of correct responses.
This model is attractive and a natural for end-of-unit examinations, adaptive testing, and as the routing mechanism for intelligent tutoring systems.
This webinar will explain the model, identify current applications, and introduce free tools for generating, calibrating and scoring data.
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