Academic Journal
Question–Answer Models for Teaching Programming in Kazakh: A Morphology-Aware Controlled Study.
| Τίτλος: | Question–Answer Models for Teaching Programming in Kazakh: A Morphology-Aware Controlled Study. |
|---|---|
| Συγγραφείς: | Tukeyev, Ualsher, Rysbek, Bekarys, Hnatkowska, Bogumiła |
| Πηγή: | Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p6156, 25p |
| Θεματικοί όροι: | Question answering systems, Low-resource languages, Natural language processing, Computer programming education, Python programming language, Model validation |
| Περίληψη: | This study investigates whether morphology-aware input processing can improve question-answering performance for teaching programming in Kazakh, a low-resource language with rich morphological variation. Rather than proposing a new neural architecture, the study presents a controlled empirical evaluation of an adapted pipeline that combines FEMSeg_kaz preprocessing with morphology-aware post-processing (MAPP) normalization and domain-specific fine-tuning. A dataset of 50,386 Kazakh-language Python programming question–answer pairs was constructed with GPT-4o assistance and expert validation. Each pair consists of a student-style programming question in Kazakh and a corresponding instructional answer, usually including a short explanation and, when appropriate, a Python code example. Experiments with multilingual encoders and a controlled MiniLM setup show that morphology-aware normalization produces measurable improvements under fixed evaluation conditions, while its combination with domain-specific fine-tuning improves several answer selection and contextual-overlap indicators. The findings suggest that carefully controlled adaptation of established NLP techniques can support the development of Kazakh educational QA systems in morphologically rich, low-resource settings. Because no separate validation split or early stopping was used, these findings should be interpreted as controlled fixed-epoch comparison results rather than as fully optimized model selection results. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20763417&ISBN=&volume=16&issue=12&date=20260615&spage=6156&pages=6156-6180&title=Applied Sciences (2076-3417)&atitle=Question%E2%80%93Answer%20Models%20for%20Teaching%20Programming%20in%20Kazakh%3A%20A%20Morphology-Aware%20Controlled%20Study.&aulast=Tukeyev%2C%20Ualsher&id=DOI:10.3390/app16126156 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 194910364 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.41931152344 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Question–Answer Models for Teaching Programming in Kazakh: A Morphology-Aware Controlled Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tukeyev%2C+Ualsher%22">Tukeyev, Ualsher</searchLink><br /><searchLink fieldCode="AR" term="%22Rysbek%2C+Bekarys%22">Rysbek, Bekarys</searchLink><br /><searchLink fieldCode="AR" term="%22Hnatkowska%2C+Bogumiła%22">Hnatkowska, Bogumiła</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Sciences (2076-3417); Jun2026, Vol. 16 Issue 12, p6156, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Question+answering+systems%22">Question answering systems</searchLink><br /><searchLink fieldCode="DE" term="%22Low-resource+languages%22">Low-resource languages</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates whether morphology-aware input processing can improve question-answering performance for teaching programming in Kazakh, a low-resource language with rich morphological variation. Rather than proposing a new neural architecture, the study presents a controlled empirical evaluation of an adapted pipeline that combines FEMSeg_kaz preprocessing with morphology-aware post-processing (MAPP) normalization and domain-specific fine-tuning. A dataset of 50,386 Kazakh-language Python programming question–answer pairs was constructed with GPT-4o assistance and expert validation. Each pair consists of a student-style programming question in Kazakh and a corresponding instructional answer, usually including a short explanation and, when appropriate, a Python code example. Experiments with multilingual encoders and a controlled MiniLM setup show that morphology-aware normalization produces measurable improvements under fixed evaluation conditions, while its combination with domain-specific fine-tuning improves several answer selection and contextual-overlap indicators. The findings suggest that carefully controlled adaptation of established NLP techniques can support the development of Kazakh educational QA systems in morphologically rich, low-resource settings. Because no separate validation split or early stopping was used, these findings should be interpreted as controlled fixed-epoch comparison results rather than as fully optimized model selection results. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=194910364 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/app16126156 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 6156 Subjects: – SubjectFull: Question answering systems Type: general – SubjectFull: Low-resource languages Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Computer programming education Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Model validation Type: general Titles: – TitleFull: Question–Answer Models for Teaching Programming in Kazakh: A Morphology-Aware Controlled Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tukeyev, Ualsher – PersonEntity: Name: NameFull: Rysbek, Bekarys – PersonEntity: Name: NameFull: Hnatkowska, Bogumiła IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20763417 Numbering: – Type: volume Value: 16 – Type: issue Value: 12 Titles: – TitleFull: Applied Sciences (2076-3417) Type: main |
| ResultId | 1 |