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A Machine Learning Approach to Predict Technology Integration Among Mathematics Teacher Educators

Artigo2025

Churun L. Maknun; Rizky Rosjanuardi; Dadan Dasari

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Resumo

This study aims to predict the willingness and proficiency of Mathematics Teacher Educators (MTEs) in integrating technology into mathematics instruction. This study employs a machine learning approach to predict the willingness and proficiency of Mathematics Teacher Educators (MTEs) in integrating technology into mathematics instruction. Secondary data from 104 MTEs were analyzed using a decision tree model to identify the most significant predictors of technology integration. The analysis revealed that familiarity with and perceived ease of use of technology were the primary determinants of MTEs’ willingness to adopt digital tools in teaching. Moreover, higher education levels and institutional support were associated with greater technological proficiency. Despite limited prior experience, many MTEs displayed a constructive attitude toward technology adoption, suggesting a readiness to engage in continuous digital learning. These findings provide actionable insights for policymakers and professional development designers in developing targeted training programs that enhance digital competence among teacher educators. Beyond its practical implications, this study contributes by showing how machine learning can extend TAM and UTAUT frameworks, revealing nonlinear relationships and offering data-driven insights into technology integration among mathematics teacher educators.

Dados da obra

Tipo
Artigo
Periódico
IAFOR Journal of Education, v. 13, n. 3, p. 259-290
Editora
The International Academic Forum
Ano
2025 (2025-12)
Idioma
en
Palavras-chave
decision tree model; mathematics education; predictive analysis; teacher proficiency; technology integration; Artificial Intelligence; Prediction; Teacher Educators; Mathematics Teachers; Mathematics Instruction; Educational Technology; Decision Making; Decision Support Systems; Computer Attitudes; Computer Uses in Education; Flow Charts; Influences; Familiarity; Models; Intention
Temas
Formação de Professores de Matemática
DOI
10.22492/ije.13.3.15
ISSN
2187-0594
Acesso
Download disponível
Fontes
DOAJ — Directory of Open Access Journals; ERIC — Education Resources Information Center
Na Biblioteca desde
01/10/2026 · verificada em 03/10/2026

Como citar

ABNT
MAKNUN, Churun L.; ROSJANUARDI, Rizky; DASARI, Dadan. A Machine Learning Approach to Predict Technology Integration Among Mathematics Teacher Educators. IAFOR Journal of Education, v. 13, n. 3, p. 259-290, 2025. DOI: https://doi.org/10.22492/ije.13.3.15.

APA

Maknun, C. L., Rosjanuardi, R., & Dasari, D. (2025). A Machine Learning Approach to Predict Technology Integration Among Mathematics Teacher Educators. IAFOR Journal of Education, 13(3), 259-290. https://doi.org/10.22492/ije.13.3.15

BibTeX
@article{maknun2025,
  author = {Maknun, Churun L. and Rosjanuardi, Rizky and Dasari, Dadan},
  title = {A Machine Learning Approach to Predict Technology Integration Among Mathematics Teacher Educators},
  year = {2025},
  journal = {IAFOR Journal of Education},
  volume = {13},
  number = {3},
  pages = {259-290},
  publisher = {The International Academic Forum},
  doi = {10.22492/ije.13.3.15},
  language = {en}
}
RIS
TY  - JOUR
AU  - Maknun, Churun L.
AU  - Rosjanuardi, Rizky
AU  - Dasari, Dadan
TI  - A Machine Learning Approach to Predict Technology Integration Among Mathematics Teacher Educators
PY  - 2025
JO  - IAFOR Journal of Education
VL  - 13
IS  - 3
SP  - 259
EP  - 290
DO  - 10.22492/ije.13.3.15
LA  - en
ER  -

Referência gerada automaticamente a partir dos metadados das fontes; confira com o documento original. Baixar BibTeX · Baixar RIS

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