Deep learning implementation in mentawai vocational schools: pedagogical readiness, educational technology utilization, and institutional support

Heri Noviko(1), Muhammad Adri(2), M. Giatman(3), Hendra Hidayat(4),
(1) Universitas Negeri Padang  Indonesia
(2) Universitas Negeri Padang  Indonesia
(3) Universitas Negeri Padang  Indonesia
(4) Universitas Negeri Padang  Indonesia

Corresponding Author


DOI : https://doi.org/10.32698/02762

Full Text:    Language : en

Abstract


This study examined the contributions of teachers’ pedagogical readiness and educational technology utilization to deep learning implementation through institutional support in public vocational schools in the Mentawai Islands Regency. An explanatory cross-sectional survey targeted all 70 teachers, with 65 complete responses analyzed using PLS-SEM. Pedagogical readiness contributed significantly to deep learning implementation (β = 0.330, p = 0.034) and institutional support (β = 0.456, p = 0.007). Educational technology utilization did not contribute significantly to deep learning implementation or institutional support. Institutional support also showed no significant direct contribution to deep learning implementation and did not mediate the relationships involving pedagogical readiness or educational technology utilization. These findings identify pedagogical readiness as the most consistent factor in the model. Strengthening teachers’ pedagogical competence, aligning technology use with instructional objectives, and translating institutional support into concrete instructional assistance are important for improving deep learning practices in geographically remote vocational schools.

Keywords


Deep Learning Implementation; Pedagogical Readiness; Educational Technology Utilization; Institutional Support

References


Andriyani, D. A., Prayitno, H. J., Minsih, M., Jamali, A., Damayanti, V. S., Dipsatara, T., & Pradana, F. G. (2025). Opportunities and challenges for the development of deep learning in vocational schools: drivers of learning innovation in the industrial era 4.0. Journal of Deep Learning, 95–108.

Berglund, A., Otermans, P. C. J., & Aditya, D. (2026). GenAI-Enabled AI Teachers and Student Learning Engagement Across International Higher Education Contexts. Education Sciences, 16(4). https://doi.org/10.3390/educsci16040600

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Camacho-Zuñiga, C., Salas-Maxemín, S., Valle-Arce, A. P., Caratozzolo, P., & Chans, G. M. (2025). Toward a continuous learning educational model: insights from the experience of a Mexican private university. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1485034

Cambeses-Franco, C., & Cambeses-Franco, P. (2026). A country-level preparedness index for digital education: infrastructure, teacher scientific-technology competence, and socioeconomic context. Education and Information Technologies. https://doi.org/10.1007/s10639-026-14042-9

Chapman, B. L., Ross, A. S., & Petraki, E. (2026). Teacher readiness for generative AI: A Theory of Planned Behaviour approach. Social Sciences and Humanities Open, 13. https://doi.org/10.1016/j.ssaho.2025.102351

Connolly, C., Carr, E., & Knox, S. (2023). Diving deep into numeracy, cross-curricular professional development. International Journal of Mathematical Education in Science and Technology, 54(6), 1034–1053. https://doi.org/10.1080/0020739X.2021.1986160

Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.

Fitrah, M., Sofroniou, A., Yarmanetti, N., Ismail, I. H., Anggraini, H., Nissa, I. C., Widyaningrum, B., Khotijah, I., Kurniawan, P. D., & Setiawan, D. (2025). Are teachers ready to adopt deep learning pedagogy? The role of technology and 21st-century competencies amid educational policy reform. Education Sciences, 15(10), 1344.

Grustan, K. J. C., Grustan, M. C., & Buniel, J. M. C. (2026). Modeling the Influence of e-Learning Usability on National Competency Assessment Readiness among Technical-Vocational and Industrial Technology Students in the Philippines. International Journal of Information and Education Technology, 16(5), 1186–1195. https://doi.org/10.18178/ijiet.2026.16.5.2587

Halomoan, Hakiki, M., Putra, B. A. W., Hamid, M. A., Utami, R., Saputro, I. N., Azizah, W. A., Sabir, A., Hidayah, Y., & Yassin, A. (2026). Deep Learning Methods Towards a Pedagogical Framework and Implementation Strategy: A Study of Information Technology Education Curriculum Development in Indonesia. Journal of Teaching and Learning, 20(1), 185–205.

Inderanata, R. N., & Sukardi, T. (2023). Investigation study of integrated vocational guidance on work readiness of mechanical engineering vocational school students. Heliyon, 9(2).

Irayanti, I. (2025). Teachers’ readiness and pedagogical change in curriculum reform: why dialogic and critical pedagogy fail to take root in Indonesian primary schools. Asian Education and Development Studies, 1–22. https://doi.org/10.1108/AEDS-11-2025-0593

Isnaeni, F., Budiman, S. A., Nurjaya, N., & Mukhlisin, M. (2025). Analysis of the Readiness for Implementing Deep Learning Curriculum in Madrasah from the Perspective of Educators. Attadrib: Jurnal Pendidikan Guru Madrasah Ibtidaiyah, 8(1), 15–30.

Istiningsih, I. (2022). Impact of ICT integration on the development of vocational high school teacher TPACK in the digital age 4.0. World Journal on Educational Technology: Current Issues, 14(1), 103–116.

Kajan, K., Shi, W., Wanatowski, D., & Ryan, M. (2026). Navigating the Dual-View Phenomenon: Social Ambivalence, Ambivalence Literacy, and Lecturer Role Transformation in AI-Integrated Transnational STEM Education. Education Sciences, 16(4). https://doi.org/10.3390/educsci16040554

Li, Y.-F., Wang, Z., Wang, L., & Guo, F. (2025). Integrating machine learning and big data analytics in an industrial engineering curriculum: Insights from an application-driven course and student feedback. TALE 2025 - 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, Proceedings. https://doi.org/10.1109/TALE66047.2025.11346751

Magat, R. B. (2026). From Plans to Practice: Preservice Mathematics Teachers’ Journey to Conceptual Understanding. Australian Journal of Teacher Education, 51(1), 104–122. https://doi.org/10.14221/1835-517X.7172

Manto, A., Visitacion, M. A., Yee, A. M., Roble, J., Capuyan, S. N., Hermoso, F., Milano, M. L., Gonzales, R., Hallarte, D. K., & Gonzales, G. (2026). AI integration in mathematics education: A systematic review of implementation challenges and pedagogical implications (2017-2025). Social Sciences and Humanities Open, 14. https://doi.org/10.1016/j.ssaho.2026.103147

Marín-Rodríguez, N. J., Morán, B. V. G., & Gerardou, F. S. (2025). Scientometric analysis of active learning and authentic assessment between 2002 and 2024: Recent trends and further research. In The Emerald Handbook of Active Learning For Authentic Assessment. https://doi.org/10.1108/978-1-83797-857-120251002

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Mpuangnan, K. N. (2024). Teacher preparedness and professional development needs for successful technology integration in teacher education. Cogent Education, 11(1), 2408837.

Pahrudin, A., Irwandani, Aridan, M., & Barata, M. F. (2025). Teacher Readiness for Deep Learning in Islamic Education: A Rasch Model Analysis of Challenges and Opportunities. Journal of Teaching and Learning, 19(4), 262–283. https://doi.org/10.22329/jtl.v19i4.9573

Pan, T., & Jiang, L. (2025). Tech-Enhanced Learning: Evaluating General Education in Vocational Colleges Through Technology Integration: T. Pan and L. Jiang. The Asia-Pacific Education Researcher, 34(4), 1245–1255.

Pasi, B. N., & Dhamak, P. (2026). Transforming higher education for the fourth industrial revolution: a strategic review of digital integration and institutional readiness. Journal of Science and Technology Policy Management, 1–29.

Ponnupillai, A., Earnest, B. S. P., Madhavan, P., Phyu, K. P., Sear, L. Y., Ponnupillai, A., & Thein, W. M. (2025). Bridging the gap between AI and medical education: Current implications and future perspectives. AIP Conference Proceedings, 3257(1). https://doi.org/10.1063/5.0264960

Purnama, M. R., Adnyana, I. P. I. K., Sogen, A. T. L., Indrawan, G., & Santosa, M. H. (2025). Teacher’s readiness toward artificial intelligence in the school of North Bali. Jurnal Paedagogy, 12(1), 23–32.

Ramaila, S. (2025). Harnessing Project-Based and Experiential Learning for Competency-Driven Education. In Developing Teaching Competencies for Pedagogical and Curricular Innovation. https://doi.org/10.4018/979-8-3373-3566-7.ch004

Riani, A., & Sujarwati, I. (2025). The preparedness of English teachers to implement deep learning in middle school. Educasia: Jurnal Pendidikan, Pengajaran, Dan Pembelajaran, 10(2), 229–244.

Sardinha, M., & Leite, M. (2026). Implementing a project-based learning framework for teaching additive manufacturing: drivers, enablers and outcomes. Procedia CIRP, 142, 692–697. https://doi.org/10.1016/j.procir.2026.05.331

Subiyantoro, S., Musa, M. Z., & Efendi, A. (2024). Preparing Indonesian primary school teachers for deep learning: Readiness, challenges, and institutional support. Cognitive Development Journal, 2(2), 77–86.

Suherman, A., Supriyadi, T., Safari, I., Saptani, E., Fauzi, R. A., Sudirjo, E., Komiljon O’g’li, T. S., & Abdisamiyevich, X. J. (2025). Bridging Teacher Readiness and Deep Learning-Based Teaching Practice: Assessing the Effectiveness of the ACTIVE Model for Enhancing Teacher Pedagogy. International Research Journal of Multidisciplinary Scope, 6(4), 284–298. https://doi.org/10.47857/irjms.2025.v06i04.05526

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.

Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–22.

Trisnaningsih, S., Sukmawati, F., Santosa, E. B., Ridhani, J., & Qodr, T. S. (2026). Exploring teacher’s acceptance of online assessment to support deep learning pedagogy: An integrated UTAUT2 approach. Multidisciplinary Reviews, 9(5). https://doi.org/10.31893/multirev.2026259

Utomo, J. B., Prayitno, H. J., & Indri, I. (2025). Strategies and Development of the Deep Learning Approach in Vocational High Schools in the Era of Global Computing. Journal of Deep Learning, 1–10.

Wahyuningsih, R., Joyoatmojo, S., Wardani, D. K., & Noviani, L. (2025). Work-integrated learning to improve work readiness of vocational education in school and madrasah. Munaddhomah: Jurnal Manajemen Pendidikan Islam, 6(4), 586–602.

Waloyo, W., Prayitno, H. J., Toharudin, T., & Rahmawati, Y. (2026). Teacher readiness for deep learning implementation in Indonesian education: A systematic narrative review of dimensions, barriers, and enabling factors. Journal of Deep Learning, 67–82.

Yulin, N., & Danso, S. D. (2025). Assessing pedagogical readiness for digital innovation: A mixed-methods study. ArXiv Preprint ArXiv:2502.15781.

Zary, A., & Zary, N. (2025). Artificial intelligence in technical and vocational education and training: Empirical evidence, implementation challenges, and future directions.

Zhong, H., Peng, L., Zhou, X., Lei, F., Ren, H., & Wang, L. (2026). A Project-Based Learning for Intelligent Systems: A Case Study in Developing Integrative Engineering Competencies. Computer Applications in Engineering Education, 34(4). https://doi.org/10.1002/cae.70233


Article Metrics

 Abstract Views : 0 times
 PDF Downloaded : 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.