AlKhuzaey, Samah, Grasso, Floriana 
ORCID: 0000-0001-8419-6554, Payne, Terry R ORCID: 0000-0002-0106-8731 and Tamma, Valentina AM ORCID: 0000-0002-1320-610X
(2021)
A Systematic Review of Data-Driven Approaches to Item Difficulty Prediction.
In: Artificial Intelligence in Education - 22nd International Conference, 2021-6-14 - 2021-9-18, Utrecht, The Netherlands and online.
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Abstract
Assessment quality and validity is heavily reliant on the quality of items included in an assessment or test. Difficulty is an essential factor that can determine items and tests’ overall quality. Therefore, item difficulty prediction is extremely important in any pedagogical learning environment. Data-driven approaches to item difficulty prediction are gaining more and more prominence, as demonstrated by the recent literature. In this paper, we provide a systematic review of data-driven approaches to item difficulty prediction. Of the 148 papers that were identified that cover item difficulty prediction, 38 papers were selected for the final analysis. A classification of the different approaches used to predict item difficulty is presented, together with the current practices for item difficulty prediction with respect to the learning algorithms used, and the most influential difficulty features that were investigated.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Uncontrolled Keywords: | Difficulty prediction, Item difficulty, Question difficulty, Systematic review, Difficulty modelling, Difficulty estimation |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Nov 2023 10:35 |
| Last Modified: | 02 Nov 2023 10:35 |
| DOI: | 10.1007/978-3-030-78292-4_3 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169481 |
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