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The Teacher Readiness Model as a Framework for Measuring Elementary School Teacher Readiness in Implementing Deep Learning-Based Indonesian Language Instruction

Proceedings Academic Universitas Jambi · Vol. 2 No. 3 (2026) · pp. 401–421

DOI
10.22437/proca.v2i3.60013
Published
2026-09-01
Publisher
LPPM Universitas Jambi
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Background: The growing emphasis on learner-centered and meaningful learning has positioned Deep Learning as a promising pedagogical approach in primary education. However, its successful implementation depends largely on teachers' readiness to adopt pedagogical innovation and adapt to evolving professional and institutional demands. Purpose: This study aimed to develop and empirically validate a multidimensional Teacher Readiness Model for implementing Deep Learning-based Indonesian language instruction in primary schools. An explanatory sequential mixed-methods design was employed. Quantitative data were collected from 120 teachers across 31 public and private primary schools in Jambi Province, Indonesia, using proportionate stratified random sampling. Semi-structured interviews with 12 teachers and four school principals were subsequently conducted to explain the quantitative findings. Descriptive statistics and multiple linear regression were used for quantitative analysis, while qualitative data were analyzed thematically. Results: Teachers demonstrated a moderate-to-high level of readiness (M = 3.76). Professional readiness emerged as the strongest dimension, whereas institutional readiness was the weakest. Regression analysis identified professional and institutional readiness as significant predictors of Deep Learning implementation. Qualitative findings further revealed that although teachers were highly motivated to innovate, they continued to face constraints related to limited professional development, inadequate digital infrastructure, and insufficient instructional mentoring. The validated Teacher Readiness Model integrates personal, professional, and institutional readiness as complementary determinants of successful Deep Learning implementation. Conclusion: The model extends the theoretical understanding of teacher readiness while providing evidence-based guidance for teacher professional development, instructional leadership, and educational policy to support sustainable instructional transformation in primary education.

Keywords

Collaboration network of the authors

31 authors · 75 co-authorship links · 2 across different organisations

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  • Universitas Jambi
  • Universitas Tamansiswa Padang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Andi Alimuddin UndeAffiliation unknown1
Muhammad SofwanAffiliation unknown1
Nadila HardiyantiAffiliation unknown1
Agung Prasetiya Budi UtomoAffiliation unknown1
Yogi SetiyokoAffiliation unknown1
, BashoriAffiliation unknown1
Yundi FitrahAffiliation unknown1
Gustina AnggrainiAffiliation unknown1
Darni DarniAffiliation unknown1
Herman BudionoAffiliation unknown1
Maratun SaadahAffiliation unknown1
Nazurty NazurtyAffiliation unknown1
Agustiyara AgustiyaraAffiliation unknown1
Hary Soedarto HarjonoAffiliation unknown1
Mohd. Norma SampoernoAffiliation unknown1
Destrinelli DestrinelliAffiliation unknown1
Bohari MuslimAffiliation unknown1
AndrisyahAffiliation unknown1
Masyunita SiregarAffiliation unknown1
Sri Indriani HarianjaAffiliation unknown1
Asrial AsrialUniversitas Jambi1
Dwi Agus KurniawanUniversitas Jambi1
Faizal ChanAffiliation unknown1
Syahrial SyahrialUniversitas Tamansiswa Padang1
Reza Aditya PratamaAffiliation unknown1
Putut NugrohoAffiliation unknown1
Retno SeptiasariAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/PROCA/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en">The Teacher Readiness Model as a Framework for Measuring Elementary School Teacher Readiness in Implementing Deep Learning-Based Indonesian Language Instruction</dc:title>
	<dc:creator xml:lang="en">Eko Kuntarto</dc:creator>
	<dc:creator xml:lang="en">Ahmad Hariandi</dc:creator>
	<dc:creator xml:lang="en">Ahmad Syarif</dc:creator>
	<dc:creator xml:lang="en">Asih Nur Ismiatun</dc:creator>
	<dc:subject xml:lang="en">Teacher Readiness</dc:subject>
	<dc:subject xml:lang="en">deep learning</dc:subject>
	<dc:subject xml:lang="en">Indonesian language instruction</dc:subject>
	<dc:subject xml:lang="en">Primary Education</dc:subject>
	<dc:subject xml:lang="en">Educational Innovation</dc:subject>
	<dc:subject xml:lang="en">Mixed-methods Research</dc:subject>
	<dc:description xml:lang="en">Background : The growing emphasis on learner-centered and meaningful learning has positioned Deep Learning as a promising pedagogical approach in primary education. However, its successful implementation depends largely on teachers' readiness to adopt pedagogical innovation and adapt to evolving professional and institutional demands. Purpose : This study aimed to develop and empirically validate a multidimensional Teacher Readiness Model for implementing Deep Learning-based Indonesian language instruction in primary schools. An explanatory sequential mixed-methods design was employed. Quantitative data were collected from 120 teachers across 31 public and private primary schools in Jambi Province, Indonesia, using proportionate stratified random sampling. Semi-structured interviews with 12 teachers and four school principals were subsequently conducted to explain the quantitative findings. Descriptive statistics and multiple linear regression were used for quantitative analysis, while qualitative data were analyzed thematically. Results : Teachers demonstrated a moderate-to-high level of readiness (M = 3.76). Professional readiness emerged as the strongest dimension, whereas institutional readiness was the weakest. Regression analysis identified professional and institutional readiness as significant predictors of Deep Learning implementation. Qualitative findings further revealed that although teachers were highly motivated to innovate, they continued to face constraints related to limited professional development, inadequate digital infrastructure, and insufficient instructional mentoring. The validated Teacher Readiness Model integrates personal, professional, and institutional readiness as complementary determinants of successful Deep Learning implementation. Conclusion : The model extends the theoretical understanding of teacher readiness while providing evidence-based guidance for teacher professional development, instructional leadership, and educational policy to support sustainable instructional transformation in primary education.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2026-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/PROCA/article/view/60013</dc:identifier>
	<dc:identifier>10.22437/proca.v2i3.60013</dc:identifier>
	<dc:source xml:lang="en">Proceedings Academic Universitas Jambi; Vol. 2 No. 3 (2026): The 6th Green Development International Conference; 401-421</dc:source>
	<dc:source>10.22437/5c7v5092</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/PROCA/article/view/60013/25562</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Eko Kuntarto, Ahmad Hariandi, Ahmad Syarif, Asih Nur Ismiatun</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>