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The effect of classroom wall color on learning performance: A virtual reality experiment

  • Research Article
  • Architecture and Human Behavior
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Abstract

The wall color has the certain impact on the learning performance of college students in the classroom. To find the suitable wall color to improve the learning performance, this study used the virtual reality (VR) technology to build five virtual classrooms with yellow, red, white, blue, and green walls, while the subjective evaluation and objective physiological indicators of college students were collected. The subjective survey showed the cold-colored walls such as blue and green had the highest levels of relaxation and pleasure, while the warm-colored walls such as yellow and red had the better attention and learning performance. And the white-walled classroom had the lowest subjective evaluation and the worst learning performance, but the white wall was widely used in the present class room. Physiological test results showed the yellow wall had the higher HRV-nLF/nHF and low-β & high-β frequencies in the FP2 channel in the frontal, but the white wall had the lowest scores. Moreover, the correlation analysis had confirmed electroencephalography (EEG) and electrocardiogram (ECG) indicators could be employed to evaluate the learning performance. These findings provide an effective reference for the spatial design of university classrooms and a basis for the study of physiological indicators.

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References

  • Abd-Alhamid F, Kent M, Bennett C, et al. (2019). Developing an innovative method for visual perception evaluation in a physical-based virtual environment. Building and Environment, 162: 106278.

    Article  Google Scholar 

  • Abd-Alhamid F, Kent M, Calautit J, et al. (2020). Evaluating the impact of viewing location on view perception using a virtual environment. Building and Environment, 180: 106932.

    Article  Google Scholar 

  • Adams R, Finn P, Moes E, et al. (2009). Distractibility in attention/deficit/hyperactivity disorder (ADHD): the virtual reality classroom. Child Neuropsychology, 15: 120–135.

    Article  Google Scholar 

  • Anter KF, Billger M (2010). Colour research with architectural relevance: How can different approaches gain from each other? Color Research and Application, 35: 145–152.

    Google Scholar 

  • Barrett P, Zhang Y, Moffat J, et al. (2013). A holistic, multi-level analysis identifying the impact of classroom design on pupils’ learning. Building and Environment, 59: 678–689.

    Article  Google Scholar 

  • Barrett P, Davies F, Zhang Y, et al. (2015). The impact of classroom design on pupils’ learning: Final results of a holistic, multi-level analysis. Building and Environment, 89: 118–133.

    Article  Google Scholar 

  • Berntson GG, Thomas Bigger J, JR, Eckberg DL, et al. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34: 623–648.

    Article  Google Scholar 

  • Camm AJ, Malik M, Bigger JT, et al. (1996). Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93: 1043–1065.

    Article  Google Scholar 

  • Díaz HM, Cid FM, Otárola J, et al. (2019). EEG Beta band frequency domain evaluation for assessing stress and anxiety in resting, eyes closed, basal conditions. Procedia Computer Science, 162: 974–981.

    Article  Google Scholar 

  • Egner T, Gruzelier JH (2001). Learned self-regulation of EEG frequency components affects attention and event-related brain potentials in humans. Neuroreport, 12: 4155–4159.

    Article  Google Scholar 

  • Elliot AJ, Maier MA, Moller AC, et al. (2007). Color and psychological functioning: The effect of red on performance attainment. Journal of Experimental Psychology: General, 136: 154–168.

    Article  Google Scholar 

  • Farley KMJ, Veitch JA (2001). A room with a view: A review of the effects of windows on work and well-being. Research Report, No. RR-136. National Research Council of Canada.

  • Ferguson CJ (2009). An effect size primer: a guide for clinicians and researchers. Professional Psychology: Research and Practice, 40: 532–538.

    Article  Google Scholar 

  • Fisher K (2005). Research into identifying effective learning environments. OECD/PEB Experts’ Group Meeting on Evaluating Quality in Educational Facilities.

  • Fuchs T, Birbaumer N, Lutzenberger W, et al. (2003). Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Applied Psychophysiology and Biofeedback, 28: 1–12.

    Article  Google Scholar 

  • Gao C, Zhang S (2020). The restorative quality of patient ward environment: Tests of six dominant design characteristics. Building and Environment, 180: 107039.

    Article  Google Scholar 

  • Goldman MJ (1986). Principles of Clinical Electrocardiography, 12th edn. Los Altos, CA, USA: Lange Medical Publications.

    Google Scholar 

  • Grangaard EM (1995). Color and light effects on learning. Association for Childhood Education International Study Conference and Exhibition.

  • Hamid PN, Newport AG (1989). Effect of colour on physical strength and mood in children. Perceptual and Motor Skills, 69: 179–185.

    Article  Google Scholar 

  • Hong T, Lee M, Yeom S, et al. (2019). Occupant responses on satisfaction with window size in physical and virtual built environments. Building and Environment, 166: 106409.

    Article  Google Scholar 

  • Humphrey NK, Keeble GR (1977). Do monkeys’ subjective clocks run faster in red light than in blue? Perception, 6: 7–14.

    Article  Google Scholar 

  • Jacobs KW, Hustmyer FE, Jr (1974). Effects of four psychological primary colors on GSR, heart rate and respiration rate. Perceptual and Motor Skills, 38: 763–766.

    Article  Google Scholar 

  • Knez I, Kers C (2000). Effects of indoor lighting, gender, and age on mood and cognitive performance. Environment and Behavior, 32: 817–831.

    Article  Google Scholar 

  • Küller R, Ballal S, Laike T, et al. (2006). The impact of light and colour on psychological mood: a cross-cultural study of indoor work environments. Ergonomics, 49: 1496–1507.

    Article  Google Scholar 

  • Küller R, Mikellides B, Janssens J (2009). Color, arousal, and performance-A comparison of three experiments. Color Research and Application, 34: 141–152.

    Article  Google Scholar 

  • Kwallek N, Lewis CM, Lin-Hsiao JWD, et al. (1996). Effects of nine monochromatic office interior colors on clerical tasks and worker mood. Color Research and Application, 21: 448–458.

    Article  Google Scholar 

  • Kwallek N, Woodson H, Lewis CM, et al. (1997). Impact of three interior color schemes on worker mood and performance relative to individual environmental sensitivity. Color Research and Application, 22: 121–132.

    Article  Google Scholar 

  • Kwallek N, Soon K, Lewis CM (2007). Work week productivity, visual complexity, and individual environmental sensitivity in three offices of different color interiors. Color Research and Application, 32: 130–143.

    Article  Google Scholar 

  • Li J, Wu W, Jin Y, et al. (2021a). Research on environmental comfort and cognitive performance based on EEG+VR+LEC evaluation method in underground space. Building and Environment, 198: 107886.

    Article  Google Scholar 

  • Li Z, Ba M, Kang J (2021b). Physiological indicators and subjective restorativeness with audio-visual interactions in urban soundscapes. Sustainable Cities and Society, 75: 103360.

    Article  Google Scholar 

  • Llinares C, Higuera-Trujillo JL, Serra J (2021). Cold and warm coloured classrooms. Effects on students’ attention and memory measured through psychological and neurophysiological responses. Building and Environment, 196: 107726.

    Article  Google Scholar 

  • Maffei L, Masullo M, Pascale A, et al. (2016). Immersive virtual reality in community planning: Acoustic and visual congruence of simulated vs real world. Sustainable Cities and Society, 27: 338–345.

    Article  Google Scholar 

  • Mokhtarmanesh S, Ghomeishi M (2019). Participatory design for a sustainable environment: Integrating school design using students’ preferences. Sustainable Cities and Society, 51: 101762.

    Article  Google Scholar 

  • Nakshian JS (1964). The effects of red and green surroundings on behavior. The Journal of General Psychology, 70: 143–161.

    Article  Google Scholar 

  • Parsons TD, Bowerly T, Buckwalter JG, et al. (2007). A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods. Child Neuropsychology, 13: 363–381.

    Article  Google Scholar 

  • Picard RW, Vyzas E, Healey J (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23: 1175–1191.

    Article  Google Scholar 

  • Smets G (1969). Time expression of red and blue. Perceptual and Motor Skills, 29: 511–514.

    Article  Google Scholar 

  • Stone NJ (1998). Windows and environmental cues on performance and mood. Environment and Behavior, 30: 306–321.

    Article  Google Scholar 

  • Stone NJ (2001). Designing effective study environments. Journal of Environmental Psychology, 21: 179–190.

    Article  Google Scholar 

  • Stone NJ (2003). Environmental view and color for a simulated telemarketing task. Journal of Environmental Psychology, 23: 63–78.

    Article  Google Scholar 

  • Torres F (1983). Electroencephalography: basic principles, clinical applications and related fields. Archives of Neurology, 40: 191–192.

    Article  Google Scholar 

  • Vernon D, Egner T, Cooper N, et al. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International Journal of Psychophysiology, 47: 75–85.

    Article  Google Scholar 

  • Wang FX, Mao AH, Li HL, et al. (2013). Quality measurement and regional difference of urbanization in Shandong Province based on the entropy method. Scientia Geographica Sinica, 33(11): 1323–1329. (in Chinese)

    Google Scholar 

  • Yeom S, Kim H, Hong T, et al. (2020). Determining the optimal window size of office buildings considering the workers’ task performance and the building’s energy consumption. Building and Environment, 177: 106872.

    Article  Google Scholar 

  • Yildirim K, Cagatay K, Ayalp N (2015). Effect of wall colour on the perception of classrooms. Indoor and Built Environment, 24: 607–616.

    Article  Google Scholar 

  • Zhu MH, Li AH (2006). Entropy-weight-based integrative assessment on the science and technology capability for the western. Mathematics in Practice and Theory, 36(12): 120–125. (in Chinese)

    Google Scholar 

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Acknowledgements

We would like to thank all participants for their time.

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Authors and Affiliations

  1. Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of Technology, Qingdao, 266033, China

    Chao Liu & Weijun Gao

  2. College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao, 266033, China

    Yalin Zhang, Limei Sun, Qiuyun Zang & Jiaxin Li

Authors
  1. Chao Liu
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  2. Yalin Zhang
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  3. Limei Sun
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  4. Weijun Gao
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  5. Qiuyun Zang
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  6. Jiaxin Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chao Liu, Yalin Zhang, Limei Sun, Qiuyun Zang, and Jiaxin Li. The first draft of the manuscript was written by Chao Liu, Yalin Zhang and Weijun Gao. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yalin Zhang or Weijun Gao.

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Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.

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Liu, C., Zhang, Y., Sun, L. et al. The effect of classroom wall color on learning performance: A virtual reality experiment. Build. Simul. 15, 2019–2030 (2022). https://doi.org/10.1007/s12273-022-0923-y

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  • DOI: https://doi.org/10.1007/s12273-022-0923-y

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