AUTOMATIC CLASSIFICATION OF EFL LEARNERS' SELF-REPORTED TEXT DOCUMENTS ALONG AN AFFECTIVE CONTINUUM

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dc.authorid0000-0001-5393-5211
dc.authorid0000-0002-4057-934X
dc.contributor.authorUysal, Derya
dc.contributor.authorUysal, Alper Kursat
dc.date.accessioned2026-01-24T12:29:19Z
dc.date.available2026-01-24T12:29:19Z
dc.date.issued2022
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractThis study aims to place EFL learners along an affective continuum via machine learning methods and present a new dataset about affective characteristics of EFL learners. In line with the purposes, written self-reports of 475 students from 5 different faculties in 3 universities in Turkey were collected and manually assigned by the researchers to one of the labels (positive, negative, or neutral). As a result, two combinations of the same dataset (AC-2 and AC-3) including different numbers of classes were used for the assessment of automatic classification approaches. Results revealed that automatic classification confirmed the manual classification to a great extent and machine learning methods could be used to classify EFL students along an affective continuum according to their affective characteristics. Maximum accuracy rate of automatic classification is 90.06% on AC-2 dataset including two classes. Similarly, on AC-3 dataset including three classes, maximum accuracy rate of classification is 71.79%. Last, the top-10 features/words obtained by feature selection methods are highly discriminative in terms of assessing student feelings for EFL learning. It could be stated that there is not an existing study in which feature selection methods and classifiers are used in the literature to automatically classify EFL learners??? feelings.
dc.identifier.doi10.20535/2410-8286.248091
dc.identifier.endpage14
dc.identifier.issn2409-3351
dc.identifier.issn2410-8286
dc.identifier.issue20
dc.identifier.scopus2-s2.0-85152418134
dc.identifier.scopusqualityQ3
dc.identifier.startpage4
dc.identifier.urihttps://doi.org/10.20535/2410-8286.248091
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5280
dc.identifier.wosWOS:000839139300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNatl Technical Univ Ukraine Kyiv Polytechnic Inst, Fac Linguistics
dc.relation.ispartofAdvanced Education
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectaffective factors
dc.subjectEFL learning
dc.subjecttext classification
dc.subjectfeature selection
dc.subjectEFL students
dc.subjecthigher education
dc.subjectaffective barriers
dc.titleAUTOMATIC CLASSIFICATION OF EFL LEARNERS' SELF-REPORTED TEXT DOCUMENTS ALONG AN AFFECTIVE CONTINUUM
dc.typeArticle

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