Artificial Intelligence

    

Active Learning for Question Difficulty Prediction

Authors: Shashwat Gupta, Jibril Frej, Paola Mejia, Tanja Kaesar

This paper focuses on question difficulty estimation (calibration), and its applications in educational scenarios and beyond. The emphasis is on the use of Active Learning to bound the minimum number of labelled samples that we need. It also explores using various SOTA methods for predicting question difficulty, with a specific focus on German textual questions using the Lernnavi dataset. The study refines preprocessing techniques for question data and metadata to improve question difficulty estimation.

Comments: 18 Pages.

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Submission history

[v1] 2023-12-29 01:28:13

Unique-IP document downloads: 225 times

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