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Using Linguistic Features to Predict the Response Process Complexity Associated with Answering Clinical MCQs
This study examines the relationship between the linguistic characteristics of a test item and the complexity of the response process required to answer it correctly.
Predicting the Difficulty and Response Time of Multiple Choice Questions Using Transfer Learning
The results indicate that, for our sample, transfer learning can improve the prediction of item difficulty when response time is used as an auxiliary task but not the other way around.
Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam
One of the most resource-intensive problems in the educational testing industry relates to ensuring that newly-developed exam questions can adequately distinguish between students of high and low ability.
Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam
To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system.
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