Artificial intelligence applications in education: Natural language processing in detecting misconceptions

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dc.authoridKökver, Yunus/0000-0002-9864-2866
dc.authoridÇelik, Harun/0000-0002-3096-8624
dc.contributor.authorKökver, Yunus
dc.contributor.authorPektaş, Hüseyin Miraç
dc.contributor.authorÇelik, Harun
dc.date.accessioned2025-01-21T16:35:59Z
dc.date.available2025-01-21T16:35:59Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThis study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design cycle was used. The dataset obtained from 402 teacher candidates was analysed by Natural Language Processing (NLP) methods. Data was classified using Machine Learning (ML), one of the AI tools, and supervised learning algorithms. It was concluded that 175 teacher candidates did not have sufficient knowledge about the concept of greenhouse effect. It was found that the AI algorithm with the highest accuracy rate and used to predict teacher candidates' misconceptions was Multilayer Perceptron (MLP). Furthermore, through the Enhanced Ensemble Model Architecture developed by researchers, the combination of ML algorithms has achieved the highest accuracy rate. The kappa (kappa) value was examined in determining the significant difference between the AI algorithm and the human expert evaluation, and it was found that there was a significant difference, and the strength of agreement was significant according to the research findings. The findings of the current study represent a significant alternative to the prevailing pedagogical approach, which has increasingly come to rely on information technologies in the process of improving conceptual understanding through the detection of conceptual misconceptions. In addition, recommendations were made for future studies.
dc.description.sponsorshipAnkara University
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1007/s10639-024-12919-1
dc.identifier.issn1360-2357
dc.identifier.issn1573-7608
dc.identifier.scopus2-s2.0-85200787295
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s10639-024-12919-1
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24223
dc.identifier.wosWOS:001284832600014
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEducation and Information Technologies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectMisconception; Natural language processing; Machine learning; Artificial intelligence
dc.titleArtificial intelligence applications in education: Natural language processing in detecting misconceptions
dc.typeArticle

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