Cognitive scaffolding module in the Vilokka virtual laboratory for coding and artificial intelligence learning
DOI:
https://doi.org/10.59672/ijed.v7i2.7020Keywords:
ADDIE, Cognitive scaffolding, KKA, Local-LLM, N-Gain, Vilokka, Vocational educationAbstract
Indonesia’s Coding and Artificial Intelligence (KKA) curriculum mandate (BSKAP No. 046/H/KR/2025) requires Vocational High School (VHS) students to master Python programming and generative AI, exposing infrastructure inequality and cognitive overload as dual barriers. This study reports five ADDIE phases of developing and evaluating a cognitive scaffolding module integrated with Vilokka, an on-premises virtual laboratory embedding a guardrailed Local Large Language Model (Local-LLM). Participants were recruited through purposive sampling: five KKA teachers (Analysis), three validators (Expert Validation), and 30 VHS students (Implementation and Evaluation). Analysis identified three cognitive barrier patterns: pre-coding abstraction failure, logic-syntax conflation, and AI-induced cognitive passivity. The Design phase produced a 17-module architecture synchronising a three-level scaffolding framework (Sense-making, Process-scaffolding, and Fading) with KKA curriculum elements. The study employed four instruments: a teacher interview guide, an expert validation rubric, pre-test and post-test assessments, and practicality questionnaires. Expert validation yielded a mean validity of 89.72% (Very Valid). The field trial yielded a mean N-Gain of 0.39 (Moderate), confirmed by paired sample t-test (t(29) = 6.385, p < 0.05; Cohen’s d = 1.17). Practicality ratings were Very Practical for teachers (88.12%) and students (86.96%). The findings establish a replicable framework for AI-assisted vocational programming instruction, recommended for schools with infrastructure constraints.
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References
Akbar, S. (2013). Instrumen perangkat pembelajaran (Instruments for learning materials). PT. Remaja Rosdakarya.
Branch, R. M. (2010). Instructional design: The ADDIE approach. In Instructional Design: The ADDIE Approach. Springer US. https://doi.org/10.1007/978-0-387-09506-6
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Christi, S. R. N., & Rajiman, W. (2023). Pentingnya berpikir komputasional dalam pembelajaran matematika (The importance of computational thinking in mathematics learning). Journal on Education, 5(4), 12590–12598. https://doi.org/10.31004/joe.v5i4.2246
Etikan, I. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1. https://doi.org/10.11648/j.ajtas.20160501.11
Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
Hartley, K., Hayak, M., & Ko, U. H. (2024). Artificial intelligence supporting independent student learning: An evaluative case study of ChatGPT and learning to code. Education Sciences, 14(2), 120. https://doi.org/10.3390/educsci14020120
Huang, A. Y. Q., Lin, C. Y., Su, S. Y., & Yang, S. J. H. (2025). The impact of GenAI-enabled coding hints on students’ programming performance and cognitive load in an SRL-based Python course. British Journal of Educational Technology, 56(5), 1942–1972. https://doi.org/10.1111/bjet.13589
I Made Elia Cahaya, I. M. E., Suryaningsih, N. M. A., Parwata, I. M. Y., Poerwati, C. E. (2024). The influence of a guided inquiry learning model in improving students’ creative thinking abilities: A meta-analysis study. (2024). Indonesian Journal of Educational Development (IJED), 5(3), 376–384. https://doi.org/10.59672/ijed.v5i3.4211
Kaenong, H. A., Alexandri, M. B., & Sugandi, Y. S. (2023). Analysis projection of the fulfillment of priority facilities and infrastructures for vocational high school using system dynamic to increase school participation rates in central kalimantan province, indonesia. Sustainability (Switzerland), 15(24), 16696. https://doi.org/10.3390/su152416696
Kemendikdasmen. (2025). Keputusan kepala badan standar, kurikulum, dan asesmen pendidikan kementerian pendidikan dasar dan menengah nomor 046/H/KR/2025 tentang capaian pembelajaran (Decree of the head of the standards, curriculum, and educational assessment board of the ministry of basic and secondary education number 046/H/KR/2025 on learning outcomes). https://guru.kemendikdasmen.go.id/dokumen/74r6Yln0zK
Mallik, S., & Gangopadhyay, A. (2023). Proactive and reactive engagement of artificial intelligence methods for education: A review. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1151391
Mercado, J. C., & Picardal, J. P. (2023). Virtual laboratory simulations in biotechnology: A systematic review. Science Education International, 34(1), 52–57. https://doi.org/10.33828/sei.v34.i1.6
OECD. (2023). Shaping digital education: Enabling factors for quality, equity and efficiency. OECD Publishing. https://doi.org/10.1787/bac4dc9f-en
Ross, E., Kansal, Y., Renzella, J., Vassar, A., & Taylor, A. (2025). Supervised fine-tuning LLMs to behave as pedagogical agents in programming education. http://arxiv.org/abs/2502.20527
Royston, P. (1992). Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing, 2(3), 117–119. https://doi.org/10.1007/BF01891203
Santyadiputra, G. S., & Kustono, D. (2023). An analysis of cybersecurity subject and Vilanets learning media towards vocational school students’ digital skills. Letters in Information Technology Education (LITE), 6(1), 16–21. https://doi.org/10.17977/UM010V6I12023P16-21
Santyadiputra, G. S., Purnomo, Kamdi, W., Patmanthara, S., & Nurhadi, D. (2024). Vilanets: An advanced virtual learning environments to improve higher education students’ learning achievement in computer network course. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2393530
Santyadiputra, G. S., Purnomo, P., Kamdi, W., & Patmanthara, S. (2025). Pengaruh virtual project based learning dan direct instruction terintegrasi SAMR dan TPACK terhadap prestasi belajar dan literasi teknologi peserta didik sekolah menengah kejuruan (The effect of virtual project based learning and direct instruction integrated with SAMR and TPACK on learning achievement and technological literacy of vocational high school students) [Doctoral dissertation]. Universitas Negeri Malang.
Sunil, K., & Thakkar, A. (2025). SocraticAI: Transforming LLMs into guided CS tutors through scaffolded interaction. http://arxiv.org/abs/2512.03501
Surya Abadi, I.B.G., Widiana, I. W., Septiari, N. K., Putu Listyana, I.G.A.A., Ari Rahayu, N. K. (2025). The impact of metacognitive-based learning strategies on enhancing students’ decision-making and cognitive dissonance. (2025). Indonesian Journal of Educational Development (IJED), 6(1), 241–253. https://doi.org/10.59672/ijed.v6i1.4805
Widana, I. W. & Ratnaya, I. G. (2021). Relationship between divergent thinking and digital literacy on teacher ability to develop HOTS assessment. Journal of Educational Research and Evaluation, 5(4), 516-524. https://doi.org/10.23887/jere.v5i4.35128
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59. https://doi.org/10.1186/s40594-022-00377-5
Yan, J., Tian, H., Sun, X., & Song, L. (2025). Role of artificial intelligence in enhancing competency assessment and transforming curriculum in higher vocational education. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1551596
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
Zeitlhofer, I., Hörmann, S., Mann, B., Hallinger, K., & Zumbach, J. (2023). Effects of cognitive and metacognitive prompts on learning performance in digital learning environments. Knowledge, 3(2), 277–292. https://doi.org/10.3390/knowledge3020019
Zhang, Y. (2025). AI-driven transformation of vocational education. International Journal of Knowledge Management, 21(1), 1–20. https://doi.org/10.4018/IJKM.394819
Zuo, M., Kong, S., Ma, Y., Hu, Y., & Xiao, M. (2023). The effects of using scaffolding in online learning: A meta-analysis. Education Sciences, 13(7), 705. https://doi.org/10.3390/educsci13070705
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