Janet Mee


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Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam
Victoria Yaneva | Le An Ha | Peter Baldwin | Janet Mee
Proceedings of the 12th Language Resources and Evaluation Conference

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. The current practice for obtaining this information is the costly procedure of pretesting: new items are administered to test-takers and then the items that are too easy or too difficult are discarded. This paper presents the first study towards automatic prediction of an item’s probability to “survive” pretesting (item survival), focusing on human-produced MCQs for a medical exam. Survival is modeled through a number of linguistic features and embedding types, as well as features inspired by information retrieval. The approach shows promising first results for this challenging new application and for modeling the difficulty of expert-knowledge questions.


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Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam
Le An Ha | Victoria Yaneva | Peter Baldwin | Janet Mee
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

Predicting the construct-relevant difficulty of Multiple-Choice Questions (MCQs) has the potential to reduce cost while maintaining the quality of high-stakes exams. In this paper, we propose a method for estimating the difficulty of MCQs from a high-stakes medical exam, where all questions were deliberately written to a common reading level. 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. The results show that the proposed approach outperforms various baselines with a statistically significant difference. Best results were achieved when using the full feature set, where embeddings had the highest predictive power, followed by linguistic features. An ablation study of the various types of linguistic features suggested that information from all levels of linguistic processing contributes to predicting item difficulty, with features related to semantic ambiguity and the psycholinguistic properties of words having a slightly higher importance. Owing to its generic nature, the presented approach has the potential to generalize over other exams containing MCQs.