Enhancing conceptual understanding and retention in thermodynamics through haptic-enhanced immersive simulations: a quasi-experimental study

Keywords: haptic feedback, immersive learning, thermodynamics, embodied cognition, cognitive load

Abstract

Immersive technologies are increasingly used in science education, yet the role of embodied interaction – particularly haptic feedback – in promoting conceptual understanding remains underexplored. This study investigated the effectiveness of Haptic + Visual Immersive Simulations (H+VISs) compared to Visual-only Immersive Simulations (VOISs) in teaching thermodynamics. A quasi-experimental design was employed with 130 secondary students, who completed pre-, immediate post-, and delayed post-tests using a validated Thermodynamics Concept Test. Results showed that the HVIS group significantly outperformed the VIS group in both post-tests, indicating improved learning gains and retention. The HVIS group also scored higher on the Embodied Thermodynamics Scale and reported lower cognitive load, as measured by the Paas scale. Repeated measures analysis of variance revealed significant main effects for time and group, as well as a significant interaction, favoring the HVIS condition. National Aeronautics and Space Administration (NASA-TLX) ratings indicated that the HVIS group experienced higher perceived performance and lower effort and frustration. Path analysis further revealed that embodied learning partially mediated the effect of instructional modality on retention. These findings support the integration of haptic feedback in immersive Science and Technology, Engineering and Mathematics (STEM) instruction, emphasizing the role of multisensory engagement in fostering deeper learning and reducing cognitive effort in abstract domains such as thermodynamics.

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References


Bhowmik, A. K. (2024). Virtual and augmented reality: Human sensory-perceptual requirements and trends for immersive spatial computing experiences. Journal of the Society for Information Display, 32(8), 605–646. https://doi.org/10.1002/jsid.2001




Brown, P. L., & Bybee, R. W. (2023). Promoting sensemaking through an impactful instructional sequence. The Science Teacher, 90(6), 22–27. https://doi.org/10.1080/00368555.2023.12315951




Brundage, M. J., Meltzer, D. E., & Singh, C. (2024). Investigating introductory and advanced students’ difficulties with entropy and the second law of thermodynamics using a validated instrument. Physical Review Physics Education Research, 20(2), 020110. https://doi.org/10.1103/physrevphyseducres.20.020110




Bybee, R. W. (2013). Next generation science standards: By states, for states. Volume 1, the standards – Arranged by disciplinary core ideas and by topics. The National Academics Press.




Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative & mixed methods approaches (6th ed.). Sage.




Dejene, W., & Chen, D. (2019). The practice of modularized curriculum in higher education institution: Active learning and continuous assessment in focus. Cogent Education, 6(1), 1–16. https://doi.org/10.1080/2331186x.2019.1611052




Demate, R. B. et al. (2025). Teacher perspectives on MATATAG curriculum: Challenges and collaborative solutions. Multidisciplinary Reviews, 8(11), 2025351–2025351. https://doi.org/10.31893/multirev.2025351




Faul, F. et al. (2009). Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/brm.41.4.1149




Han, I., & Black, J. B. (2011). Incorporating haptic feedback in simulation for learning physics. Computers & Education, 57(4), 2281–2290. https://doi.org/10.1016/j.compedu.2011.06.012




Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (task load index): Results of empirical and theoretical research. Advances in Psychology, 52, 139–183. https://doi.org/10.1016/s0166-4115(08)62386-9




Huang, Y. et al. (2022). Recent advances in multi-mode haptic feedback technologies towards wearable interfaces. Materials Today Physics, 22, 100602. https://doi.org/10.1016/j.mtphys.2021.100602




Ignatow, G. (2007). Theories of embodied knowledge: New directions for cultural and cognitive sociology? Journal for the Theory of Social Behaviour, 37(2), 115–135. https://doi.org/10.1111/j.1468-5914.2007.00328.x




Johnson-Glenberg, M. C. et al. (2014). Collaborative embodied learning in mixed reality motion-capture environments: Two science studies. Journal of Educational Psychology, 106(1), 86–104. https://doi.org/10.1037/a0034008




Jones, M. G. et al. (2005). Haptic augmentation of science instruction: Does touch matter? Science Education, 90(1), 111–123. https://doi.org/10.1002/sce.20086




Lapitan, L. (2021). An effective blended online teaching and learning strategy during the COVID-19 pandemic. Education for Chemical Engineers, 35(35), 116–131. https://doi.org/10.1016/j.ece.2021.01.012




Latchem, C., & Jung, I. (2009). Distance and blended learning in Asia. Routledge eBooks. Informa. https://doi.org/10.4324/9780203878774




Lécuyer, A. (2009). Simulating haptic feedback using vision: A survey of research and applications of pseudo-haptic feedback. Presence: Teleoperators and Virtual Environments, 18(1), 39–53. https://doi.org/10.1162/pres.18.1.39




Lee, J. et al. (2020). Thermo-haptic materials and devices for wearable virtual and augmented reality. Advanced Functional Materials, 31(39), 2007376. https://doi.org/10.1002/adfm.202007376




Lindgren, R. et al. (2016). Enhancing learning and engagement through embodied interaction within a mixed reality simulation. Computers & Education, 95, 174–187. https://doi.org/10.1016/j.compedu.2016.01.001




Linn, M. C., & Songer, N. B. (1991). Teaching thermodynamics to middle school students: What are appropriate cognitive demands? Journal of Research in Science Teaching, 28(10), 885–918. https://doi.org/10.1002/tea.3660281003




Loverude, M. (2015). Identifying student resources in reasoning about entropy and the approach to thermal equilibrium. Physical Review Special Topics – Physics Education Research, 11(2), 020118. https://doi.org/10.1103/physrevstper.11.020118




MacLean, K.E. (2000, April 1). Designing with haptic feedback. In, IEEE Xplore. IEEE. https://doi.org/10.1109/ROBOT.2000.844146




Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007




Mercado, J. C. (2020). Development of laboratory manual in physics for engineers. Online Submission, 9(10), 200–210.




Minogue, J., & Borland, D. (2015). Investigating students’ ideas about buoyancy and the influence of haptic feedback. Journal of Science Education and Technology, 25(2), 187–202. https://doi.org/10.1007/s10956-015-9585-1




Orleans, A. V. (2007). The condition of secondary school physics education in the Philippines: Recent developments and remaining challenges for substantive improvements. The Australian Educational Researcher, 34(1), 33–54. https://doi.org/10.1007/bf03216849




Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32(1), 1–8. https://doi.org/10.10023/B:TRUC.0000021806.17516.d0




Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. https://doi.org/10.1007/s10648-011-9179-2




Paas, F. G. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429




Peiris, R. L. et al. (2017). ThermoVR. In, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2 May 2017 pp. 5452–5456. Association for Computing Machinery. https://doi.org/10.1145/3025453.3025824




Pellas, N., Dengel, A., & Christopoulos, A. (2020). A scoping review of immersive virtual reality in STEM education. IEEE Transactions on Learning Technologies, 13(4), 1. https://doi.org/10.1109/tlt.2020.3019405




Perry, S., Bridges, S. M., & Burrow, M. F. (2015). A review of the use of simulation in dental education. Simulation in Healthcare: Journal of the Society for Simulation in Healthcare, 10(1), 31–37. https://doi.org/10.1097/sih.0000000000000059




Reyes, R. L. et al. (2024). Enhancing experiential science learning with virtual labs: A narrative account of merits, challenges, and implementation strategies. Journal of Computer Assisted Learning, 40(6), 3167–3186. https://doi.org/10.1111/jcal.13061




Shapiro, L. (2019aa). Embodied cognition. Routledge. https://doi.org/10.4324/9781315180380




Shapiro, L., & Spaulding, S. (Eds.). (2014). The Routledge handbook of embodied cognition. Routledge. https://doi.org/10.4324/9781315775845




Shapiro, L. A. (2019b). Embodied cognition. Routledge.




Smith, T. I. et al. (2015). Identifying student difficulties with entropy, heat engines, and the Carnot cycle. Physical Review Special Topics – Physics Education Research, 11(2), 020116. https://doi.org/10.1103/physrevstper.11.020116




Sokrat, H. et al. (2014). Difficulties of students from the faculty of science with regard to understanding the concepts of chemical thermodynamics. Procedia – Social and Behavioral Sciences, 116, 368–372. https://doi.org/10.1016/j.sbspro.2014.01.223




Sun, Z. et al. (2022). Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. Nature Communications, 13(1), 5224. https://doi.org/10.1038/s41467-022-32745-8




Sweller, J. (2011, January 1). Cognitive load theory. In, Mestre, J. P., & Ross, B. H. (Eds.). ScienceDirect, Academic Press. Retrieved from https://www.sciencedirect.com/science/article/pii/B9780123876911000028




Talbot, C., & Davison, C. (2023). Chemistry for the IB diploma. 3rd ed. Hodder Education.




Trelease, R. B. (2016). From chalkboard, slides, and paper to e-learning: How computing technologies have transformed anatomical sciences education. Anatomical Sciences Education, 9(6), 583–602. https://doi.org/10.1002/ase.1620




van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0




Wenk, N. et al. (2021). Effect of immersive visualization technologies on cognitive load, motivation, usability, and embodiment. Virtual Reality, 27(1), 307–331. https://doi.org/10.1007/s10055-021-00565-8
Published
2025-10-31
How to Cite
Purigay , J. P. D. (2025). Enhancing conceptual understanding and retention in thermodynamics through haptic-enhanced immersive simulations: a quasi-experimental study. Research in Learning Technology, 33. https://doi.org/10.25304/rlt.v33.3587
Section
Original Research Articles