Using Learning Analytics to evaluate the quality of multiple-choice questions: A perspective with Classical Test Theory and Item Response Theory

Article Status
Published
Authors/contributors
Title
Using Learning Analytics to evaluate the quality of multiple-choice questions: A perspective with Classical Test Theory and Item Response Theory
Abstract
The purpose of this paper is to find appropriate forms of analysis of multiple-choice questions (MCQ) to obtain an assessment method, as fair as possible, for the students. The authors intend to ascertain if it is possible to control the quality of the MCQ contained in a bank of questions, implemented in Moodle, presenting some evidence with Item Response Theory (IRT) and Classical Test Theory (CTT). The used techniques can be considered a type of Descriptive Learning Analytics since they allow the measurement, collection, analysis and reporting of data generated from students’ assessment.
Publication
The International Journal of Information and Learning Technology
Date
2019-8-5
Volume
36
Issue
4
Pages
322-341
Journal Abbr
IJILT
Citation Key
azevedo2019
Accessed
22/01/2024, 19:00
ISSN
2056-4880
Short Title
Using Learning Analytics to evaluate the quality of multiple-choice questions
Language
en
Library Catalogue
DOI.org (Crossref)
Extra
<标题>: 利用学习分析评估多项选择题的质量:基于经典测验理论与项目反应理论的视角 <AI Smry>: The main contribution and originality that can be found in this research is the definition of groups of questions with similar features, regarding their difficulty and discrimination properties, which allow teachers to build tests, randomly generated with Moodle, that include questions with several difficulty levels in the tests, as it should be done. Read_Status: New Read_Status_Date: 2026-01-26T11:33:43.294Z
Citation
Azevedo, J. M., Oliveira, E. P., & Beites, P. D. (2019). Using Learning Analytics to evaluate the quality of multiple-choice questions: A perspective with Classical Test Theory and Item Response Theory. The International Journal of Information and Learning Technology, 36(4), 322–341. https://doi.org/10.1108/IJILT-02-2019-0023
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