Repository logo
 
Publication

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

dc.contributor.authorAzevedo, José Manuel
dc.contributor.authorOliveira, Ema
dc.contributor.authorBeites, P. D.
dc.date.accessioned2020-02-07T16:47:52Z
dc.date.available2020-02-07T16:47:52Z
dc.date.issued2019
dc.description.abstractPurpose 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. Design/methodology/approach A representative data set of students’ grades from tests, randomly generated with a bank of questions implemented in Moodle, was used for analysis. The data were extracted from a Moodle database using MySQL with an ODBC connector, and collected in MS ExcelTM worksheets, and appropriate macros programmed with VBA were used. The analysis with the CTT was done through appropriate MS ExcelTM formulas, and the analysis with the IRT was approached with an MS ExcelTM add-in. Findings The Difficulty and Discrimination Indexes were calculated for all the questions having enough answers. It was found that the majority of the questions presented values for these indexes, which leads to a conclusion that they have quality. The analysis also showed that the bank of questions presents some internal consistency and, consequently, some reliability. Groups of questions with similar features were obtained, which is very important for the teacher to develop tests as fair as possible. Originality/value 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. These groups allow the identification of difficulty levels in the questions on the bank of questions, thus allowing teachers to build tests, randomly generated with Moodle, that include questions with several difficulty levels in the tests, as it should be done. As far as the authors’ knowledge, there are no similar results in the literature.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1108/IJILT-02-2019-0023pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/9146
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://www.emerald.com/insight/content/doi/10.1108/IJILT-02-2019-0023/full/htmlpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAnalyticspt_PT
dc.subjecte-assessmentpt_PT
dc.subjectItem Responsept_PT
dc.titleUsing Learning Analytics to evaluate the quality of multiple-choice questions: a perspective with Classical Test Theory and Item Response Theorypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage341pt_PT
oaire.citation.issue4pt_PT
oaire.citation.startPage322pt_PT
oaire.citation.titleThe International Journal of Information and Learning Technologypt_PT
oaire.citation.volume36pt_PT
person.familyNameOliveira
person.familyNameDamas Beites
person.givenNameEma Patrícia de Lima
person.givenNamePatrícia
person.identifier.ciencia-id0910-B8EE-A6F6
person.identifier.ciencia-id591A-8D9A-73CE
person.identifier.orcid0000-0003-3341-1757
person.identifier.orcid0000-0003-0266-7055
person.identifier.ridK-5772-2017
person.identifier.scopus-author-id37041170800
rcaap.embargofctCopyright cedido à editora no momento da publicaçãopt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationb380b9d9-6d31-48e7-8750-5ddc091ef68c
relation.isAuthorOfPublication9f86873f-5364-4413-a23d-ceab6f2372a3
relation.isAuthorOfPublication.latestForDiscoveryb380b9d9-6d31-48e7-8750-5ddc091ef68c

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Paper_AzevedoBeitesOliveira2019_FINAL_Version.pdf
Size:
693.68 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: