Sistem Informasi Evaluasi Minat Belajar Siswa terhadap Mata Pelajaran TIK Berbasis Web pada SMP Swasta Rangga Rame
DOI:
https://doi.org/10.69693/ijmst.v4i3.14475Keywords:
Sistem Informasi, Minat Belajar, TIK, Kuesioner, Web, PrototypeAbstract
Evaluasi minat belajar siswa terhadap mata pelajaran Teknologi Informasi dan Komunikasi (TIK) merupakan salah satu aspek penting dalam mendukung peningkatan kualitas proses pembelajaran. Pada SMP Swasta Rangga Rame, proses evaluasi minat belajar masih dilakukan secara manual menggunakan kuesioner, sehingga pengumpulan, pengolahan, penyimpanan, dan penyajian hasil membutuhkan waktu serta berpotensi menimbulkan kesalahan. Penelitian ini bertujuan untuk merancang dan mengembangkan Sistem Informasi Evaluasi Minat Belajar Siswa terhadap Mata Pelajaran TIK Berbasis Web yang dapat membantu sekolah melakukan evaluasi secara lebih efektif dan terintegrasi. Metode penelitian menggunakan Research and Development (R&D) dengan model pengembangan Prototype. Tahapan penelitian meliputi identifikasi masalah, analisis kebutuhan, perancangan sistem, pembangunan prototipe, evaluasi, implementasi, dan pengujian sistem. Sistem dirancang dengan fitur pengelolaan data siswa, pengelolaan instrumen kuesioner, pengisian kuesioner secara daring, penyimpanan jawaban, perhitungan skor dan persentase, kategorisasi minat belajar, penyajian hasil dalam bentuk tabel dan grafik, serta pencetakan laporan. Hasil pengolahan data terhadap 220 siswa menunjukkan bahwa 40 siswa (18,18%) berada pada kategori sangat tinggi, 80 siswa (36,36%) kategori tinggi, 30 siswa (13,64%) kategori sedang, 30 siswa (13,64%) kategori rendah, dan 40 siswa (18,18%) kategori sangat rendah. Hasil tersebut menunjukkan bahwa kategori tinggi memiliki jumlah siswa paling banyak. Sistem yang dikembangkan dapat membantu guru dan sekolah memperoleh informasi hasil evaluasi minat belajar secara lebih cepat, terstruktur, dan terdokumentasi serta dapat digunakan sebagai pendukung evaluasi dan pengambilan keputusan dalam pembelajaran TIK.
References
1. Barthakur, A., Joksimovic, S., Kovanovic, V., Richey, M., & Pardo, A. (2022). Aligning objectives with assessment in online courses: Integrating learning analytics and measurement theory. Computers & Education, 190, 104603.
2. Bergdahl, N., Bond, M., Sjöberg, J., et al. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, 63.
3. Bjarnason, E., Lang, F., & Mjöberg, A. (2023). An empirically based model of software prototyping: A mapping study and a multi-case study. Empirical Software Engineering, 28, 115.
4. Dermentzi, E., Zotou, M., Tambouris, E., & Tarabanis, K. (2022). Using the problem based learning method and educational technologies to teach open data: A design-based research approach. Education and Information Technologies, 27, 8859–8882.
5. Dong, H., Chen, X., & Shen, J. (2026). Unpacking the impact mechanism of generative AI-based learning analytics technology on student learning engagement: The mediating role of community of inquiry and the moderating role of digital literacy. International Journal of Educational Technology in Higher Education, 23, 14.
6. Gherghel, C., Yasuda, S., & Kita, Y. (2023). Interaction during online classes fosters engagement with learning and self-directed study both in the first and second years of the COVID-19 pandemic. Computers & Education, 200, 104795.
7. Guo, L., Du, J., & Zheng, Q. (2023). Understanding the evolution of cognitive engagement with interaction levels in online learning environments: Insights from learning analytics and epistemic network analysis. Journal of Computer Assisted Learning, 39(3), 984–1001.
8. Gürcan, F., Erdogdu, F., Cagiltay, N. E., & Çağıltay, K. (2023). Student engagement research trends of past 10 years: A machine learning-based analysis of 42,000 research articles. Education and Information Technologies, 28(11), 15067–15091.
9. Kaliisa, R., Jivet, I., & Prinsloo, P. (2023). A checklist to guide the planning, designing, implementation, and evaluation of learning analytics dashboards. International Journal of Educational Technology in Higher Education, 20, 28.
10. Kujala, S., et al. (2022). Influences of UX factors in the Agile UX context of software startups. Information and Software Technology, 152, 107041.
11. Lee, J., & Kim, D. (2025). From awareness to empowerment: Self-determination theory-informed learning analytics dashboards to enhance student engagement in asynchronous online courses. Journal of Computing in Higher Education, 37, 1078–1118.
12. Li, Q., Xu, D., Baker, R., Holton, A., & Warschauer, M. (2022). Can student-facing analytics improve online students’ effort and success by affecting how they explain the cause of past performance? Computers & Education, 189, 104517.
13. Liao, H., Zhang, Q., Yang, L., et al. (2023). Investigating relationships among regulated learning, teaching presence and student engagement in blended learning: An experience sampling analysis. Education and Information Technologies, 28, 12997–13025.
14. Maglyas, A., Nikula, U., & Smolander, K. (2022). Improving agile software development using user-centered design and lean startup. Information and Software Technology, 141, 106718.
15. Mendoza, N. B., Yan, Z., & King, R. B. (2023). Supporting students’ intrinsic motivation for online learning tasks: The effect of need-supportive task instructions on motivation, self-assessment, and task performance. Computers & Education, 193, 104663.
16. Palanci, A., Yılmaz, R. M., & Turan, Z. (2024). Learning analytics in distance education: A systematic review study. Education and Information Technologies, 29, 22629–22650.
17. Paulsen, L., & Lindsay, E. (2024). Learning analytics dashboards are increasingly becoming about learning and not just analytics—A systematic review. Education and Information Technologies, 29, 14279–14308.
18. RapiTest: Continuous black-box testing of RESTful Web APIs. (2023). Procedia Computer Science, 219, 537–545.
19. Sakr, A., & Abdullah, T. (2024). Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review. Education and Information Technologies, 29, 19913–19962.
20. Silva, T. S., et al. (2023). Lessons learned to improve the UX practices in agile projects involving data science and process automation. Information and Software Technology, 155, 107106.
21. Social presence and teacher involvement: The link with expectancy, task value, and engagement. (2022). The Internet and Higher Education, 55, 100869.
22. Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19, 12.
23. Wang, J., Tigelaar, D. E. H., Luo, J., & Admiraal, W. (2022). Teacher beliefs, classroom process quality, and student engagement in the smart classroom learning environment: A multilevel analysis. Computers & Education, 183, 104501.
24. Zhu, W., Zhu, G., & Hua, Y. (2024). Enhancing undergraduates’ engagement in a learning community by including their voices in the technological and instructional design. Computers & Education, 214, 105026.
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