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Found 7 papers, 3 papers with code
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text
Classical approaches to question calibration are either subjective or require newly created questions to be deployed before being calibrated.
Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text
They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text.
On the application of Large Language Models for language teaching and assessment technology
The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention.
A quantitative study of NLP approaches to question difficulty estimation
We find that Transformer based models are the best performing across different educational domains, with DistilBERT performing almost as well as BERT, and that they outperform other approaches even on smaller datasets.
Complexity-based partitioning of CSFI problem instances with Transformers
Then, we leverage the errors of such model and train a second Transformer-based model to partition the problem instances into groups of different complexity, thus detecting the ones that can be solved without using too expensive resources.
Introducing a framework to assess newly created questions with Natural Language Processing
Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e. g., learning path customization, drop-out prediction).
R2DE: a NLP approach to estimating IRT parameters of newly generated questions
The main objective of exams consists in performing an assessment of students' expertise on a specific subject.
