Publicación:
On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique

Vista sencilla de ítem
dc.contributor.authorHernández González, Jerónimo
dc.contributor.authorHerrera Caro, Pedro Javier
dc.date.accessioned2024-05-20T11:39:29Z
dc.date.available2024-05-20T11:39:29Z
dc.date.issued2023-12
dc.description.abstractIn peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction, one of the most appealing features of peer assessment for instructors. Our proposal uses a probabilistic model to estimate a grade for each test, accounting for the degree of precision and bias of grading peers. The grade that the instructor would assign to a test can help enhance the model. Our main hypothesis is that guiding the instructor through supervision of a peer-assessed task by pointing out to them which test to evaluate next can lead to improvement in the validity of the model-estimated grades at an early stage. Moreover, the instructor can decide how many tests to grade based on their own criteria of tolerable uncertainty, as measured by the model. We validate the method using both synthetically generated data and real data collected in an actual class. Models that link the roles of the student as grading peer and as test-taker appear to better exploit available information, although simpler models are more appropriate in specific conditions. The best performing technique for guiding the instructor is that which selects the test with the highest expected entropy reduction. In general, empirical results are in line with the hypothesis of this study.en
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1109/TLT.2023.3319733
dc.identifier.issn1939-1382
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12376
dc.journal.issue6
dc.journal.titleIEEE Transactions on Learning Technologies
dc.journal.volume16
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsPeer assessment
dc.subject.keywordsWorkload management
dc.subject.keywordsProbabilistic graphical models
dc.subject.keywordsActive machine learning
dc.titleOn the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Techniquees
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
relation.isAuthorOfPublication.latestForDiscoveryd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Herrera_Caro_Pedro_Javier_Instructor_guidance.pdf
Tamaño:
1.1 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos y papers