Design of Learning Media: Modeling & Simulation of Building Thermal Comfort Optimization System in Building Physics Course

E. K. Wati, N. Widiansyah


The use of instructional media is something that can support the teaching and learning process; therefore, a lecturer must have the ability to create and develop learning media. This study aims to improve student learning outcomes in building physics course by using simulation learning media and models to help students understand thermal comfort material. Making modelling and simulation media is done using MATLAB software. The subjects of this study were physics engineering students who took Building Physics course. At the beginning of the study, students are given material and then in groups discuss thermal contents and then given a pretest test with an average score of 70.27, and for an average grade of 71.3 assignments. At the meeting next week, using the Student-Centered Learning (SCL) method and using problem-based learning in groups, students take temperature measurements in several rooms in the Building at the UNAS Physical Engineering Laboratory. The measurement results show that the room does not have thermal requirements (PERGUB No. 38/2012), so students have the task of conducting experiments using models that have been created by researchers to create learning media to improve comfort in using thermal buildings. Simulation results carried out by students, that is, can produce rooms with thermal conditions at 21-25°C (PERGUB No. 38/2012). This simulation is also able to provide the score of building energy efficiency. After students succeed in conducting the test, the assessment test or posttest is carried out with an average score obtained 80.55, and an average score of 80 assignments. The results of the pretest, assignment 1, assignment 2, and posttest show an increase in students’ scores of 14.6% for the Test and Task Score of 12.20%. Based on the hypothesis test, for both variables showed t-count < t-table and significance < 0.05. It shows there are significant differences in student learning outcomes both test scores and assignment scores before and after using a simulated media. Thus, the system and simulation model designed can be used as learning media that can improve student learning outcomes.


learning media; simulation; thermal condition

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Ahonen, A. K., Häkkinen, P., & Pöysä-Tarhonen, J. (2018). Collaborative problem solving in Finnish pre-service teacher education: A case study. In Assessment and Teaching of 21st Century Skills (pp. 119-130). Springer, Cham.

Al-Hmouz, A., Shen, J., Al-Hmouz, R., & Yan, J. (2012). Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions on Learning Technologies, 5(3), 226-237.

Ardabili, S. F., Mahmoudi, A., & Gundoshmian, T. M. (2016). Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers. Journal of Building Engineering, 6, 301-308.

Aryuntini, N., Astuti, I., & Yuliana, Y. (2018). Development of Learning Media Based on VideoScribe to Improve Writing Skill for Descriptive Text of English Language Study. Journal of Education, Teaching and Learning, 3(2), 187-194.

Batlolona, J. R., Baskar, C., Kurnaz, M. A., & Leasa, M. (2018). The improvement of problem-solving skills and physics concept mastery on temperature and heat topic. Jurnal Pendidikan IPA Indonesia, 7(3), 273-279.

Bronack, S. C. (2011). The role of immersive media in online education. The Journal of Continuing Higher Education, 59(2), 113-117.

Budaiwi, I., & Abdou, A. (2013). HVAC system operational strategies for reduced energy consumption in buildings with intermittent occupancy: The case of mosques. Energy conversion and management, 73, 37-50.

Chen, Y., & Treado, S. (2014). Development of a simulation platform based on dynamic models for HVAC control analysis. Energy and Buildings, 68, 376-386.

Cheng, K. H., & Tsai, C. C. (2013). Affordances of augmented reality in science learning: Suggestions for future research. Journal of science education and technology, 22(4), 449-462.

Cukurova, M., Avramides, K., Spikol, D., Luckin, R., & Mavrikis, M. (2016, April). An analysis framework for collaborative problem solving in practice-based learning activities: a mixedmethod approach. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 84-88).

De Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305-308.

Dunn, A. M., Hofmann, O. S., Waters, B., & Witchel, E. (2011, August). Cloaking Malware with the Trusted Platform Module. In USENIX Security Symposium.

Gibson, A., Kitto, K., & Willis, J. (2014, March). A cognitive processing framework for learning analytics. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 212-216).

Greiff, S., Wüstenberg, S., Csapó, B., Demetriou, A., Hautamäki, J., Graesser, A. C., & Martin, R. (2014). Domain-general problem solving skills and education in the 21st century. Educational Research Review, (13), 74-83.

Guillaud, X., Faruque, M. O., Teninge, A., Hariri, A. H., Vanfretti, L., Paolone, M., Dinavahi, V., Mitra, P., Lauss, G., Dufour, C., Forsyth, P., Srivastava, A. K., Strunz, K., Strasser, T., & Davoudi, A. (2015). Applications of Real-Time Simulation Technologies in Power and Energy Systems. IEEE Power and Energy Technology Systems Journal, 2(3), 103–115.

Hegde, B., & Meera, B. N. (2012). How do they solve it? An insight into the learner’s approach to the mechanism of physics problem solving. Physical Review Special Topics-Physics Education Research, 8(1), 010109.

Ibanez, M. B., Di-Serio, A., Villaran-Molina, D., & Delgado-Kloos, C. (2016). Support for Augmented Reality Simulation Systems: The Effects of Scaffolding on Learning Outcomes and Behavior Patterns. IEEE Transactions on Learning Technologies, 9(1), 46-56.

Jaakkola, T., Nurmi, S., & Veermans, K. (2011). A comparison of students’ conceptual understanding of electric circuits in simulation only and simulationâ€laboratory contexts. Journal of research in science teaching, 48(1), 71-93.

Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 Horizon Report. New Media Consortium.

Kassas, M. (2015). Modeling and Simulation of Residential HVAC Systems Energy Consumption. Procedia Computer Science, 52, 754-763.

Luckin, R., Mavrikis, M., Avramides, K., & Cukurova, M. (2015, January). Analysing Project Based Learning Scenarios to Inform the Design of Learning Analytics: Learning from Related Concepts. In AIED Workshops.

Prima, E. C., Putri, A. R., & Rustaman, N. (2018). Learning Solar System Using PhET Simulation to Improve Students’ Understanding and Motivation. Journal of Science Learning, 1(2), 60-70.

Requena-Carrión, J., Alonso-Atienza, F., GuerreroCurieses, A., & Rodríguez-González, A. B. (2010, April). A student-centered collaborative learning environment for developing communication skills in engineering education. In IEEE EDUCON 2010 Conference (pp. 783-786). IEEE.

Scheffel, M., Drachsler, H., & Specht, M. (2015, March). Developing an evaluation framework of quality indicators for learning analytics.

In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 16-20).

Schneider, B., Jermann, P., Zufferey, G., & Dillenbourg, P. (2010). Benefits of a tangible interface for collaborative learning and interaction. IEEE Transactions on Learning Technologies, 4(3), 222-

Smetana, L. K., & Bell, R. L. (2012). Computer simulations to support science instruction and learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337-1370.

Vidergor, H. E. (2018). Effectiveness of the multidimensional curriculum model in developing higher-order thinking skills in elementary and secondary students. The Curriculum Journal, 29(1), 95-115.

Wang, Y., Kuckelkorn, J., Zhao, F. Y., Liu, D., Kirschbaum, A., & Zhang, J. L. (2015). Evaluation on classroom thermal comfort and energy performance of passive school building by optimizing HVAC control systems. Building and Environment, 89, 86-106.

Worsley, M., & Blikstein, P. (2014). Analyzing engineering design through the lens of computation. Journal of Learning Analytics, 1(2), 151-186.

Yang, Y., Wang, B., & Zhou, Q. (2017). Air Conditioning System Design using Free Cooling Technology and Running Mode of a Data Center in Jinan. Procedia Engineering, 205, 3545-3549.


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