Exploring Item Difficulty Prediction: Data Driven Approach for Item Difficulty Estimation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Exploring Item Difficulty Prediction: Data Driven Approach for Item Difficulty Estimation

Résumé

This paper presents a comprehensive study of learning assessment, delving into the concept of item difficulty and learner perception. It addresses two critical dimensions: the methodologies employed, particularly data-driven approaches, and the necessary data for this analysis. Traditional difficulty estimation methods focus on question content or student performance. Recent studies suggest using machine learning and natural language processing to predict question difficulty. These models are subject-specific and often overlook individual student differences, limiting their wider application. The work aims to examine data of real-world testing scenarii, so that assembling and building a rich and diverse dataset. It offers valuable insights into the factors influencing item difficulty by giving the maximum amount of information considering the test and the student. It presents experiments to build and train predictive machine learning models for difficulty prediction. At the end, thanks to experiments, we can show a nuanced understanding of the assessment challenge and lay the groundwork for incorporating psychological factors into difficulty estimation as a subsequent phase.
Fichier non déposé

Dates et versions

hal-04706345 , version 1 (23-09-2024)

Identifiants

Citer

Mohamed Lamgarraj, Céline Joiron, Aymeric Parant, Gilles Dequen. Exploring Item Difficulty Prediction: Data Driven Approach for Item Difficulty Estimation. 20th International Conference on Intelligent Tutoring Systems (ITS), Jun 2024, Thessaloniki, Greece. pp.415-424, ⟨10.1007/978-3-031-63028-6_36⟩. ⟨hal-04706345⟩
9 Consultations
0 Téléchargements

Altmetric

Partager

More