Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00577144" target="_blank" >RIV/67985807:_____/23:00577144 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11210/23:10470307 RIV/00216208:11110/23:10470307 RIV/00216208:11410/23:10470307
Result on the web
<a href="https://dx.doi.org/10.3390/math11194104" target="_blank" >https://dx.doi.org/10.3390/math11194104</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math11194104" target="_blank" >10.3390/math11194104</a>

Result language
angličtina
Original language name
Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms
Original language description
This work presents a comparative analysis of various machine learning (ML) methods for predicting item difficulty in English reading comprehension tests using text features extracted from item wordings. A wide range of ML algorithms are employed within both the supervised regression and the classification tasks, including regularization methods, support vector machines, trees, random forests, back-propagation neural networks, and Naïve Bayes. Moreover, the ML algorithms are compared to the performance of domain experts. Using f-fold cross-validation and considering the root mean square error (RMSE) as the performance metric, elastic net outperformed other approaches in a continuous item difficulty prediction. Within classifiers, random forests returned the highest extended predictive accuracy. We demonstrate that the ML algorithms implementing item text features can compete with predictions made by domain experts, and we suggest that they should be used to inform and improve these predictions, especially when item pre-testing is limited or unavailable. Future research is needed to study the performance of the ML algorithms using item text features on different item types and respondent populations.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA21-03658S" target="_blank" >GA21-03658S: Theoretical foundations of computational psychometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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