Efektivitas pembelajaran blended learning terhadap kompetensi elektronika otomotif: studi kuasi-eksperimental
-
Published: September 28, 2026
-
Page: 194-199
Abstract
Penelitian ini bertujuan untuk mengukur sejauhmana tingkat keberhasilan dan penguasaan peserta didik terhadap kompetensi dalam pembelajaran Listrik Elektronika Otomotif dengan menggunakan metode eksperimen. Subjek dalam penelitian ini adalah mahasiswa mata kuliah Listrik Elektronika Otomotif dengan jumlah sampelnya sebanyak 30 mahasiswa yang terdiri dari kelas kontrol (kelas A) dan kelas eksperimen (kelas B) sebagai data primer. Teknik analisis data uji persyaratan analisis yaitu uji normalitas menggunakan program SPSS dengan uji Kolmogorov-Smirnov dan uji homogenitas yang kemudian selanjutnya dilakukan T-Test Independent untuk mengukur sejauhmana tingkat keberhasilan dan penguasaan peserta didik terhadap kompetensi dalam pembelajaran Listrik Elektronika Otomotif. Berdasarkan data yang telah diperoleh, evaluasi hasil belajar mahasiswa dengan pendekatan blended learning berbasis e-learning UNP mampu mengukur sejauhmana tingkat keberhasilan dan penguasaan peserta didik terhadap kompetensi dalam pembelajaran Listrik Elektronika Otomotif sehingga dapat memberikan gambaran bagi dosen untuk merencanakan strategi pembelajaran pada pembelajaran lainnya.
- Blended learning, Evaluasi, Hasil belajar

This work is licensed under a Creative Commons Attribution 4.0 International License.
- Akmalia, R., Oktapia, D., Hasibuan, E. E., Hasibuan, I. T. D., Azzahrah, N., & Harahap, T. S. A. (2023). Pentingnya Evaluasi Peserta Didik dalam Proses Pembelajaran. Jurnal Pendidikan dan Konseling (JPDK), 5(1), 4089-4092.
- Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. New York, NY: Longman.
- Arikunto, S. (2013). Prosedur penelitian: Suatu pendekatan praktik (Rev. ed.). Jakarta, Indonesia: Rineka Cipta.
- Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. doi:10.1007/s12528-013-9077-3
- Billett, S. (2011). Vocational education: Purposes, traditions and prospects. Dordrecht, The Netherlands: Springer.
- Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. doi:10.1080/0969595980050102
- Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.
- Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th ed.). Hoboken, NJ: Wiley.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
- Dewi. C., Yanto, D. T. P.,and Hastuti, H,2020. “The Development of Power Electronics Training Kits for Electrical Engineering Students: A Validity Test Analysis,” Jurnal Pendidikan Teknologi Kejuruan, vol. 3, no. 2, pp. 114–120.
- Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15, Article 3. doi:10.1186/s41239-017-0087-5
- Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). London, England: Sage.
- Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. doi:10.1016/j.iheduc.2004.02.001
- Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning: Global perspectives, local designs (pp. 3–21). San Francisco, CA: Pfeiffer.
- Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20, 20–34. doi:10.1016/j.iheduc.2013.09.004
- Harrow, A. J. (1972). A taxonomy of the psychomotor domain: A guide for developing behavioral objectives. New York, NY: David McKay.
- Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London, England: Routledge.
- Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of educational objectives: The classification of educational goals. Handbook II: Affective domain. New York, NY: David McKay.
- Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863. doi:10.3389/fpsyg.2013.00863
- Leader, B., Martias, & Wagino. (2018). Penerapan Model Pembelajaran Inkuiri untuk Meningkatkan Keaktifan dan Prestasi Belajar Siswa pada Pembelajaran Sistem Pengapian Kelas XI TKR SMKN 2 Muara Bungo. Automotive Engineering Education Journals, 7(1), 1-8.
- Levene, H. (1960). Robust tests for equality of variances. In I. Olkin, S. G. Ghurye, W. Hoeffding, W. G. Madow, & H. B. Mann (Eds.), Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford, CA: Stanford University Press.
- López-Pérez, M. V., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students' perceptions and their relation to outcomes. Computers & Education, 56(3), 818–826. doi:10.1016/j.compedu.2010.10.023
- Magdalena, I., Fauzi, H. N., & Putri, R. (2020). Pentingnya evaluasi dalam pembelajaran dan akibat memanipulasinya. Bintang, 2(2), 244-257.
- Massey, F. J., Jr. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78. doi:10.1080/01621459.1951.10500769
- Means, B., Toyama, Y., Murphy, R., & Bakia, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.
- Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21–33.
- Rusman. (2011). Model-model pembelajaran: Mengembangkan profesionalisme guru. Jakarta, Indonesia: Rajawali Pers.
- Sawaluddin, S., & Muhammad, S. (2020). Langkah-langkah dan teknik evaluasi hasil belajar Pendidikan Agama Islam. Jurnal PTK Dan Pendidikan, 6(1).
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
- Sinar. (2018). Metode Active Learning (Upaya Meningkatkan Keaktifan dan Hasil Belajar Siswa). Yogyakarta: Budi Utama.
- Sjukur, S. B. (2012). Pengaruh blended learning terhadap motivasi belajar dan hasil belajar siswa di tingkat SMK. Jurnal Pendidikan Vokasi, 2(3), 368–378. doi:10.21831/jpv.v2i3.1043
- Sudijono, A. (2011). Pengantar evaluasi pendidikan. Jakarta, Indonesia: Rajawali Pers.
- Sudjana, N. (2013). Penilaian hasil proses belajar mengajar. Bandung, Indonesia: Remaja Rosdakarya.
- Sugiyono. (2017). Metode penelitian kuantitatif, kualitatif, dan R&D. Bandung, Indonesia: Alfabeta.
- Sukidal, N., Marlina, D., & Anawati, S. (2022). Meninjau Kembali Inovasi Dan Hakikat Pembelajaran Akidah Akhlak. An-Nahdhah| Jurnal Ilmiah Keagamaan dan Kemasyarakatan, 15(1), 23-37.
- Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. doi:10.1016/j.compedu.2006.11.007
- Susanto, A. (2013). Teori Belajar dan Pembelajaran di Sekolah Dasar. Jakarta: Kencana Prenada Media Group.
- Thobroni, M. (2016). Belajar dan Pembelajaran: Teori dan Praktek. Jakarta: Ar-Ruzz Media.
- Vo, H. M., Zhu, C., & Diep, N. A. (2017). The effect of blended learning on student performance at course-level in higher education: A meta-analysis. Studies in Educational Evaluation, 53, 17–28. doi:10.1016/j.stueduc.2017.01.002
- UNESCO-UNEVOC. (2022). World TVET database. UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training.
- Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
- Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating communities of practice: A guide to managing knowledge. Harvard Business School Press.
- Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education, 12, 351–384.
- Wu, Z., Tang, Y., & Ericson, B. J. (2025). Learner and instructor needs in AI-supported programming learning tools: Design implications for features and adaptive control. In Proceedings of the 26th International Conference on Artificial Intelligence in Education (pp. 146–161). Springer. https://doi.org/10.48550/arXiv.2503.00144
- Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
- Xie, W., Shi, H., Li, Y., & Liu, J. (2025). STAP: A Socratic tutor for adaptive programming with pedagogical scaffolding. In Proceedings of the 2025 2nd International Symposium on Artificial Intelligence for Education (ISAIE 2025). ACM. https://doi.org/10.1145/3775073.3775165
- Yan, H., Lin, F., & Kinshuk. (2024). An AI-learner shared control model design for adaptive practicing. In Generative intelligence and intelligent tutoring systems (ITS 2024, Lecture Notes in Computer Science, Vol. 14798). Springer. https://doi.org/10.1007/978-3-031-63028-6_21
- Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
- Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629