Internet of Things-Enabled Smart Classroom Infrastructure for Maritime Education: Real-Time Learning Analytics and Student Engagement Optimization at Maritime Institute

Authors

  • Marlin Marlin Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Fitri Mulyana Sekolah Tinggi Ilmu Pelayaran Jakarta
  • Eriza I. Ulmi Sekolah Tinggi Ilmu Pelayaran Jakarta

DOI:

https://doi.org/10.55123/ijisit.v2i2.54

Keywords:

Internet of Things, Learning Analytics, Maritime Education, Smart Classrooms, Student Engagement

Abstract

The integration of Internet of Things (IoT) technologies into educational infrastructure represents a transformative opportunity for enhancing student engagement, optimizing learning outcomes, and enabling data-driven pedagogical decision-making in maritime education contexts where technical complexity and high-stakes professional competency requirements create strong imperatives for instructional effectiveness. This sequential mixed-methods study investigates the impact of IoT-enabled smart classroom infrastructure on student engagement and learning outcomes at Sekolah Tinggi Ilmu Pelayaran (STIP) Jakarta through convergent analysis of structured questionnaire data from maritime academy students (n=180) and instructors (n=35) experiencing both IoT-equipped and traditional classrooms, complemented by Focus Group Discussions exploring pedagogical mechanisms and implementation experiences. Quantitative findings demonstrate that IoT smart classrooms significantly improve student engagement by 36.5 percent (p < .001, Cohen's d = 1.23) and learning outcomes by 28.1 percent (p < .001, d = 0.94) relative to traditional classroom baselines, with real-time feedback effectiveness showing largest improvement at 54.6 percent. Correlation analysis reveals engagement-learning outcome relationships are substantially stronger in smart classrooms (r = 0.81) than traditional settings (r = 0.59), indicating IoT technologies enhance both engagement levels and pedagogical conversion of engagement into learning gains. Qualitative FGD analysis identifies visibility-accountability effects and instructor responsiveness enabled by real-time learning analytics as primary improvement mechanisms. The study proposes an IoT Smart Classroom Integration Framework incorporating response systems, analytics dashboards, environmental optimization, and faculty development for data-driven pedagogy.

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References

[1] Z. T. Zhu, M. H. Yu, and P. Riezebos, "A research framework of smart education," Smart Learning Environments, vol. 3, no. 1, pp. 1–17, 2016.

[2] M. Alavi, B. C. Wheeler, and J. S. Valacich, "Using IT to reengineer business education: An exploratory investigation of collaborative telelearning," MIS Quarterly, vol. 19, no. 3, pp. 293–312, 1995.

[3] C. M. Chen, Y. C. Yang, and G. J. Hwang, "An augmented reality-based mobile learning system to improve students' learning achievements and motivations in natural science inquiry activities," Journal of Educational Technology & Society, vol. 20, no. 4, pp. 60–75, 2017.

[4] G. J. Hwang, "Definition, framework and research issues of smart learning environments: A context-aware ubiquitous learning perspective," Smart Learning Environments, vol. 1, no. 1, pp. 1–14, 2014.

[5] N. Streitz, C. Rocker, T. Prante, D. van Alphen, R. Stenzel, and C. Magerkurth, "Designing smart artifacts for smart environments," Computer, vol. 38, no. 3, pp. 41–49, 2005.

[6] D. H. Schunk and B. J. Zimmerman, "Influencing children's self-efficacy and self-regulation of reading and writing through modeling," Reading & Writing Quarterly, vol. 23, no. 1, pp. 7–25, 2007.

[7] S. J. H. Yang, H. Ogata, T. Matsui, and N. S. Chen, "Human-centered artificial intelligence in education: Seeing the invisible through the visible," Computers and Education: Artificial Intelligence, vol. 2, p. 100008, 2021.

[8] M. K. Lützhöft and S. W. Dekker, "On your watch: Automation on the bridge," Journal of Navigation, vol. 55, no. 1, pp. 83–96, 2002.

[9] H. Xie, H. C. Chu, G. J. Hwang, and C. C. Wang, "Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017," Computers & Education, vol. 140, p. 103599, 2019.

[10] S. J. H. Yang, J. Ogata, T. Matsui, and N. S. Chen, "Opportunities and challenges for next-generation smart learning environments," Smart Learning Environments, vol. 8, no. 1, pp. 1–13, 2021.

[11] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic review of research on artificial intelligence applications in higher education: Where are the educators?" International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 1–27, 2019.

[12] B. J. Zimmerman and D. H. Schunk, "Self-regulated learning and academic achievement: Theoretical perspectives," in Handbook of Self-Regulation of Learning and Performance, B. J. Zimmerman and D. H. Schunk, Eds. New York, NY: Routledge, 2011, pp. 1–12.

[13] T. Bastiaens, J. Van Braak, M. Brown, L. Cantoni, M. Castro, R. Christensen, G. Davidson-Shivers, K. DePryck, M. Ebner, M. Fominykh, et al., "Emerging technologies and pedagogical implications for higher education," Educational Technology Research and Development, vol. 68, no. 5, pp. 2229–2257, 2020.

[14] P. A. Ertmer, "Addressing first- and second-order barriers to change: Strategies for technology integration," Educational Technology Research and Development, vol. 47, no. 4, pp. 47–61, 1999.

[15] L. Cuban, H. Kirkpatrick, and C. Peck, "High access and low use of technologies in high school classrooms: Explaining an apparent paradox," American Educational Research Journal, vol. 38, no. 4, pp. 813–834, 2001.

[16] J. W. Creswell and V. L. Plano Clark, Designing and Conducting Mixed Methods Research, 3rd ed. Thousand Oaks, CA: SAGE Publications, 2018.

[17] V. Braun and V. Clarke, "Using thematic analysis in psychology," Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, 2006.

[18] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.

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Published

2025-12-30

How to Cite

Marlin, M., Fitri Mulyana, & Eriza I. Ulmi. (2025). Internet of Things-Enabled Smart Classroom Infrastructure for Maritime Education: Real-Time Learning Analytics and Student Engagement Optimization at Maritime Institute. IJISIT: International Journal of Computer Science and Information Technology, 2(2), 153–162. https://doi.org/10.55123/ijisit.v2i2.54

Issue

Vol. 2 No. 2 (2025): December 2025

Section

Articles

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Copyright (c) 2026 Marlin Marlin, Fitri Mulyana, Eriza I. Ulmi

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