Automatic classification of EFL learners’ self-reported text documents along an affective continuum

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dc.contributor.authorUysal, Derya
dc.contributor.authorUysal, Alper Kürşat
dc.date.accessioned2023-02-15T14:02:17Z
dc.date.available2023-02-15T14:02:17Z
dc.date.issued2022
dc.description.abstractenThis 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.uk
dc.format.pagerangePp. 4-14uk
dc.identifier.citationUysal, D. Automatic classification of EFL learners’ self-reported text documents along an affective continuum / Derya Uysal, Alper Kürşat Uysal // Advanced education. – 2022. – Iss. 20. – Pp. 4-14.uk
dc.identifier.doihttps://doi.org/10.20535/2410-8286.248091
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/52641
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.sourceAdvanced education : збірник наукових праць, Вип. 20uk
dc.subjectaffective factorsuk
dc.subjectEFL learninguk
dc.subjecttext classificationuk
dc.subjectfeature selectionuk
dc.subjectEFL studentsuk
dc.subjecthigher educationuk
dc.subjectaffective barriersuk
dc.titleAutomatic classification of EFL learners’ self-reported text documents along an affective continuumuk
dc.typeArticleuk

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Advanced education: збірник наукових праць, Вип. 20