Enhancing Students' Knowledge of Air Conditioning Processes Using a Psychrometric Simulator

Ega Taqwali Berman; Yayat Yayat; Mutaufiq Mutaufiq

doi:10.17509/jmee.v13i2.106023

Enhancing Students' Knowledge of Air Conditioning Processes Using a Psychrometric Simulator

Ega Taqwali Berman, Yayat Yayat, Mutaufiq Mutaufiq

Abstract

Mastery of psychrometric processes is an important component of air-conditioning education, as students need to understand the relationships among various air properties and changes in air conditions during air-conditioning processes. This study aimed to determine the improvement in students’ knowledge after learning with a psychrometric simulator. A quantitative approach with a One-Group Pretest-Posttest design was employed. The participants were 36 students of the Mechanical Engineering Education Study Program in the Refrigeration and Air Conditioning field who were enrolled in an Air Conditioning course. Data were collected using a conceptual knowledge test covering cognitive levels C1–C4. Learning improvement was analyzed using the Normalized Gain (N-gain). The results showed that the students’ mean score increased from 62.56 on the pre-test to 80.22 on the post-test, with a mean N-gain of 0.45, categorized as moderate. The distribution of N-gain scores indicated that 36.11% of students were in the low category, 33.33% in the moderate category, and 30.56% in the high category. These findings indicate that the psychrometric simulator has the potential to support air-conditioning education by helping students connect changes in air properties with air-conditioning processes through measurement, observation, and psychrometric chart analysis.

Keywords

Air-Conditioning Education; Air-Conditioning Processes; Learning Outcomes; N-Gain; Psychrometric Simulator

Full Text:

PDF (Indonesian)

References

Bhattacharya, Y., & Milne, M. (2009, May). Psychrometric chart tutorial: a tool for under-standing human thermal comfort conditions. In 38th American Solar Energy Society Conference, Buffalo (pp. 11-16).

Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544

Erdélyi, P., & Rajkó, R. (2016). Using interactive psychrometric charts to visualize and explore psychrometric processes. Journal of Chemical Education, 93(2), 391–393. https://doi.org/10.1021/acs.jchemed.5b00779

Gupta, P. K., & Patel, R. N. (2017). A teaching–learning tool for elementary psychrometric processes on psychrometric chart using MATLAB. Computer Applications in Engineering Education, 25(3), 458–467. https://doi.org/10.1002/cae.21813

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

Kresta, S. M., & Ayrancı Tansık, İ. (2018). Psychrometric charts in color: An example of active learning for chemical engineering students and faculty members. Education for Chemical Engineers, 22, 14–19. https://doi.org/10.1016/j.ece.2017.07.003

Karadoğan, E., & Karadoğan, F. (2019). Simulation‐based learning modules for undergraduate engineering dynamics. Computer Applications in Engineering Education, 27(4), 846-862. https://doi.org/10.1002/cae.22114

Mago, P. J., & Luck, R. (2014). Use of computational tools to enhance the study of psychrometric processes for buildings in an air conditioning course. International Journal of Mechanical Engineering Education, 42(3), 251–266. https://doi.org/10.7227/IJMEE.001

Mago, P. J., & Long, W. (2016). Implementation of a computational tool to study psychrometric processes in mechanical engineering courses. International Journal of Mechanical Engineering Education, 44(1), 29–55. https://doi.org/10.1177/0306419015625145

Agustini, H., Nugraha, R. G., & Hanifah, N. (2024). Pengaruh penggunaan media pembelajaran padlet ULIK (Ular Tangga Interaktif Kreatif) terhadap hasil belajar IPAS siswa kelas IV SD. Journal of Education Research, 5(1), 807-814. https://doi.org/10.37985/jer.v5i1.931

Negahban, A. (2024). Simulation in engineering education: The transition from physical experimentation to digital immersive simulated environments. Simulation, 100 (7), 695-708. https://doi.org/10.1177/00375497241229757

Patwardhan, M., & Murthy, S. (2015). When does higher degree of interaction lead to higher learning in visualizations? Exploring the role of ‘Interactivity Enriching Features’. Computers & Education, 82, 292-305. https://doi.org/10.1016/j.compedu.2014.11.018

Rohmah, H. N., Suherman, A., & Utami, I. S. (2021). Penerapan problem based learning berbasis STEM pada materi alat optik untuk meningkatkan kemampuan berpikir kritis peserta didik. Jurnal Penelitian Pembelajaran Fisika, 12(2), 117-123. https://doi.org/10.26877/jp2f.v12i2.7900

Satria, T., Batubara, J., & Deliani, N. (2025). The Factors Affecting the Learning Process and Outcomes. TOFEDU: The Future of Education Journal, 4(8), 4358-4365.

Wahab, A., Junaedi, J., & Azhar, M. (2021). Efektivitas pembelajaran statistika pendidikan menggunakan uji peningkatan N-gain di PGMI. Jurnal Basicedu, 5(2), 1039-1045. https://doi.org/10.31004/basicedu.v5i2.845

Zhang, Y., Wang, P., Jia, W., Zhang, A., & Chen, G. (2025). Dynamic visualization by GeoGebra for mathematics learning: a meta-analysis of 20 years of research. Journal of Research on Technology in Education, 57(2), 437-458. https://doi.org/10.1080/15391523.2023.2250886

DOI: https://doi.org/10.17509/jmee.v13i2.106023