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dc.contributor.authorSanz-Fayos, Javier
dc.contributor.authorde-la-Fuente-Valentín, Luis
dc.contributor.authorVerdú, Elena
dc.date2022
dc.date.accessioned2023-05-17T08:40:06Z
dc.date.available2023-05-17T08:40:06Z
dc.identifier.citationSanz-Fayos, J., de-la-Fuente-Valentín, L., Verdú, E. (2022). Keyword-Based Processing for Assessing Short Answers in the Educational Field. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_10es_ES
dc.identifier.isbn9783031196461
dc.identifier.issn1865-0929
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14675
dc.description.abstractWhen grading open-ended engineering exam responses, it is assessed to what extent its content and quality suit the requirements and accomplish the objectives of the test. This is a time consuming and subjective task. The support of a software tool that identifies the correctness of the response and provides useful feedback to both student and teacher may alleviate its complexity. In this work, a semi-automatic evaluation method based on augmented Spanish keyword recognition is presented. This assessment is based on the occurrence of a set of keyterms that the teacher expects to appear in a good response. The evaluation is based on an augmented catalogue of terms, automatically created from the teacher selected keyterms, resulting in an ad hoc thesauri. The method uses state-of-the-art techniques, but also ad hoc procedures developed from the Spanish corpus Wikicorpus, for pre-processing the texts. The results, tested using real anonymized data from engineering exam topics such as database techniques and bigdata, indicate good performance in the thesauri creation and keyword matching. Besides, the keyterms strategy allows simple individualized feedback. However, the relationship found between automatic and human grading indicates that further research is required.es_ES
dc.language.isoenges_ES
dc.publisherCommunications in Computer and Information Sciencees_ES
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-031-19647-8_10es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectdictionaryes_ES
dc.subjectfeedbackes_ES
dc.subjectkeytermses_ES
dc.subjectnatural language processinges_ES
dc.subjectsemi-automatic evaluationes_ES
dc.subjectScopus(2)es_ES
dc.titleKeyword-Based Processing for Assessing Short Answers in the Educational Fieldes_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/978-3-031-19647-8_10


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