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Mobile Learning and Competency-Based Education in TVET: A Bibliometric Synthesis.

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Title: Mobile Learning and Competency-Based Education in TVET: A Bibliometric Synthesis.
Authors: Wagino, Wagino1 wagino@ft.unp.ac.id, Tymofiiv, Volodymyr2, Polin, Ken3, Hidayat, Nuzul1, Muslim, Muslim1, Koto, Rahmat Desman1
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 8, p4-32. 29p.
Subjects: Mobile learning, Bibliometrics, Outcome-based education, Educational technology, Gamification, Occupational training, Digital divide
Abstract: Mobile learning has emerged as a transformative approach in education, yet a comprehensive synthesis of its application in Technical and Vocational Education and Training (TVET) remains limited. This study maps the research landscape, identifies key themes, evaluates theoretical frameworks, and uncovers research gaps in mobile learning for TVET from 2020 to 2026. A Bibliometric-Systematic Literature Review (B-SLR) was conducted following PRISMA 2020 guidelines using the Scopus database (N = 600 records). Bibliometric analysis employed VOSviewer and the Bibliometrix R package; quality assessment used MMAT 2018. Systematic review of 58 included studies revealed that technology-enhanced pedagogical approaches generally improved learning outcomes in TVET, although effect sizes varied by intervention type and context. VR/AR interventions showed moderate to large effect sizes (d = 0.40-0.84) with some studies reporting null findings, while gamification and LMS-based approaches demonstrated positive but design-dependent results. Nine thematic clusters were identified, with AI and smart technology among the fastest-growing. TAM and UTAUT dominated theoretical frameworks (43.1%), revealing a critical gap in learning-quality-oriented theories. A sub-analysis comparing medical-CBME studies (n = 14) with traditional TVET studies (n = 44) revealed divergent theoretical orientations and assessment challenges. Southeast Asia emerged as the dominant research region, though this concentration warrants caution in generalizing findings. Infrastructure and digital divide constituted the most frequently reported barrier, while psychomotor skills assessment frameworks represented the most TVET-distinctive unaddressed challenge. This study recommends prioritizing longitudinal research, AI integration, the development of psychomotor assessment tools, cross-cultural validation, and cost-effectiveness analysis. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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.)
Database: Engineering Source
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  Data: Mobile Learning and Competency-Based Education in TVET: A Bibliometric Synthesis.
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  Data: <searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Outcome-based+education%22">Outcome-based education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br /><searchLink fieldCode="DE" term="%22Gamification%22">Gamification</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+training%22">Occupational training</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+divide%22">Digital divide</searchLink>
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  Data: Mobile learning has emerged as a transformative approach in education, yet a comprehensive synthesis of its application in Technical and Vocational Education and Training (TVET) remains limited. This study maps the research landscape, identifies key themes, evaluates theoretical frameworks, and uncovers research gaps in mobile learning for TVET from 2020 to 2026. A Bibliometric-Systematic Literature Review (B-SLR) was conducted following PRISMA 2020 guidelines using the Scopus database (N = 600 records). Bibliometric analysis employed VOSviewer and the Bibliometrix R package; quality assessment used MMAT 2018. Systematic review of 58 included studies revealed that technology-enhanced pedagogical approaches generally improved learning outcomes in TVET, although effect sizes varied by intervention type and context. VR/AR interventions showed moderate to large effect sizes (d = 0.40-0.84) with some studies reporting null findings, while gamification and LMS-based approaches demonstrated positive but design-dependent results. Nine thematic clusters were identified, with AI and smart technology among the fastest-growing. TAM and UTAUT dominated theoretical frameworks (43.1%), revealing a critical gap in learning-quality-oriented theories. A sub-analysis comparing medical-CBME studies (n = 14) with traditional TVET studies (n = 44) revealed divergent theoretical orientations and assessment challenges. Southeast Asia emerged as the dominant research region, though this concentration warrants caution in generalizing findings. Infrastructure and digital divide constituted the most frequently reported barrier, while psychomotor skills assessment frameworks represented the most TVET-distinctive unaddressed challenge. This study recommends prioritizing longitudinal research, AI integration, the development of psychomotor assessment tools, cross-cultural validation, and cost-effectiveness analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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.)
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        Value: 10.3991/ijim.v20i08.61205
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        Text: English
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      – SubjectFull: Mobile learning
        Type: general
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      – SubjectFull: Outcome-based education
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      – SubjectFull: Occupational training
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      – SubjectFull: Digital divide
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              Text: 2026
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