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

• Published in: Mathematics (Basel) Vol. 11; no. 19; p. 4104
• Main Authors: Štěpánek, Lubomír, Dlouhá, Jana, Martinková, Patrícia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Accuracy; Algorithms; Automation; Back propagation networks; Classification; Comparative analysis; Computational linguistics; Data mining; Datasets; elastic net regression; Language processing; Machine learning; Maximum likelihood method; Natural language interfaces; Neural networks; Performance prediction; Rasch model; Readability; Regularization; regularization methods; Root-mean-square errors; Subject specialists; Support vector machines; text features and item wording; text-based item difficulty prediction; United Kingdom; Czech Republic
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11194104

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.