Enhancing software and learning with Serbian student feedback corpora.
Automated collection and analysis of student feedback within Intelligent Tutoring Systems are vital for the continuous refinement of both educational content and software performance, ensuring that learning environments remain responsive to student needs. This study presents the creation and annotat...
| Published in: | Language Resources & Evaluation Vol. 59; no. 4; pp. 4193 - 4222 |
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| Main Authors: | , , , |
| Format: | Article |
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Springer Nature
Dec2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=189912027&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 189912027 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2025 vid: 59 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 189912027 10.1007/s10579-025-09855-y ppf: 4193 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.6MB tig: atl: Enhancing software and learning with Serbian student feedback corpora. aug: au: Vidaković, Dragan Luburić, Nikola Kovačević, Aleksandar Slivka, Jelena affil: https://ror.org/00xa57a59 Department of Computing and Control Engineering, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia su: Intelligent tutoring systems Data augmentation Requirements engineering Natural language processing Data analysis Classification sug: subj: Intelligent tutoring systems Data augmentation Requirements engineering Natural language processing Data analysis Classification keyword: Crowd-based requirements engineering Information and Computing Sciences Computer Software Information Systems Education Specialist Studies In Education Low-resource language Text-based emotion detection Transformers ab: Automated collection and analysis of student feedback within Intelligent Tutoring Systems are vital for the continuous refinement of both educational content and software performance, ensuring that learning environments remain responsive to student needs. This study presents the creation and annotation of Serbian student feedback corpora within an Intelligent Tutoring System, intending to enhance both software functionality and educational experiences. The research addresses gaps in existing studies by implementing a transparent and standardized data annotation process, with Inter-Annotator Agreement scores confirming the reliability of the annotation process. The resulting datasets were then processed using fine-tuned multilingual transformer models, with data augmentation techniques enhancing the analysis. Additionally, a few-shot prompting of a large language model was explored to further improve classification accuracy. The experimental results show that fine-tuned transformer models, combined with data augmentation, significantly enhance the accuracy of feedback analysis, achieving performance levels comparable to human annotators and surpassing baseline models. This automated approach to analyzing student feedback provides substantial time and resource savings for educators and software developers, enabling more efficient and timely improvements to both the software and the educational strategies. This work not only contributes to the development of Serbian language resources but also establishes a foundation for future research in Crowd-based Requirements Engineering and Text-based Emotion Detection within educational contexts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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