Skip to main content
Log in

Examining the effects of tempo in background music on adolescent learners’ reading comprehension performance: employing a multimodal approach

  • Original Research
  • Published:
Instructional Science Aims and scope Submit manuscript

Abstract

This present study examines the psycho-emotional and psychophysiological effects that variations in the tempo of background music have on learners who are completing reading comprehension tests while being monitored used multi-modal computer technology. Results of seventy-four (N = 74) participants indicated that listening to fast tempo music (150 bpm) predicted lower reading comprehension scores, increased emotional expressions of fear, joy and contempt, and higher skin conductance responses (SCRs). Results indicated that participants were more likely to produce higher scores while listening to slow tempo music (110 bpm), but such findings were not connected to significant differences in facial emotion expressions or psychophysiological responses. Contrasting these were control/no-music conditions in which participants exhibited moderated scores. Results from the fast-tempo condition can possibly be attributed in part to an affective valence of emotions and psychophysiological responses, as the multimodal data suggests that a combined regulatory mechanism may be at play while engaged in a learning task. This paper raises several questions regarding the use and effects of background music in performance-based learning settings and the role of affective-stimuli on cognitive regulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Almeida, F. A. M., Nunes, R. F. H., Ferreira, S. S., Krinski, K., Elsangedy, H. M., Buzzachera, C. F., et al. (2015). Effects of musical tempo on physiological, affective, and perceptual variables and performance of self-selected walking pace. Journal of Physical Therapy Science, 27(6), 1709–1712. https://doi.org/10.1589/jpts.27.1709

    Article  PubMed  PubMed Central  Google Scholar 

  • Anyanwu, E. G. (2015). Background music in the dissection laboratory: Impact on stress associated with the dissection experience. Advances in Physiology Education, 39(2), 96–101. https://doi.org/10.1152/advan.00057.2014

    Article  PubMed  Google Scholar 

  • Azevedo, R., Mudrick, N., Taub, M., & Wortha, F. (2017). Coupling between metacognition and emotions during STEM learning with advanced learning technologies: A critical analysis, implications for future research, and design of learning systems. Teachers College Record, 119(13), 1–18.

    Google Scholar 

  • Azevedo, R., Taub, M., & Mudrick, N. V. (2018). Using multi-channel trace data to infer and foster self regulated learning between humans and advanced learning technologies. In D. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 254–270). Routledge.

    Google Scholar 

  • Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028

    Article  PubMed  PubMed Central  Google Scholar 

  • Bieleke, M., Gogol, K., Goetz, T., Daniels, L., & Pekrun, R. (2021). The AEQ-S: A short version of the Achievement Emotions Questionnaire. Contemporary Educational Psychology, 65, 101940. https://doi.org/10.1016/j.cedpsych.2020.101940

    Article  Google Scholar 

  • Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer.

    Book  Google Scholar 

  • Bramley, S., Dibben, N., & Rowe, R. (2016). Investigating the influence of music tempo on arousal and behaviour in laboratory virtual roulette. Psychology of Music44(6), 1389–1403. https://doi.org/10.1177%2F0305735616632897

  • Broekens, J., Jacobs, E., & Jonker, C. M. (2015). A reinforcement learning model of joy, distress, hope and fear. Connection Science, 27(3), 215–233. https://doi.org/10.1080/09540091.2015.1031081

    Article  ADS  Google Scholar 

  • Brown, J. A., Fishco, V. V., & Hanna, G. (1993). Nelson-Denny reading test: Manual for scoring and interpretation, forms G & H. Riverside Publishing.

    Google Scholar 

  • Chang, J., Lin, P., & Hoffman, E. (2020). Music major, affects, and positive music listening experience. Psychology of Music. https://doi.org/10.1177/0305735619901151

    Article  Google Scholar 

  • Chevrier, M., Muis, K. R., Trevors, G. J., Pekrun, R., & Sinatra, G. M. (2019). Exploring the antecedents and consequences of epistemic emotions. Learning and Instruction, 63, 101209. https://doi.org/10.1016/j.learninstruc.2019.05.006

    Article  Google Scholar 

  • Choi, H.-H., van Merriënboer, J. J. G., & Paas, F. (2014). Effects of the physical environment on cognitive load and learning: Towards a new model of cognitive load. Educational Psychology Review, 26(2), 225–244. https://doi.org/10.1007/s10648-014-9262-6

    Article  Google Scholar 

  • Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. https://doi.org/10.1037/0033-2909.112.1.155

    Article  CAS  PubMed  Google Scholar 

  • den Uyl, M. J., & Van Kuilenburg, H. (2005). The FaceReader: Online facial expression recognition. In Proceedings of measuring behavior (Vol. 30, pp. 589–590). Citeseer.

  • D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29(Complete), 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003

    Article  Google Scholar 

  • Dindar, M., Sanna, J., & Hanna, J. (2020). Interplay of metacognitive experiences and performance in collaborative problem solving. Computers & Education. https://doi.org/10.1016/j.compedu.2020.103922

    Article  Google Scholar 

  • Ekman, P., & Friesen, W. (1978). Manual for the facial action coding system. Consulting Psychologists Press.

    Google Scholar 

  • Fassbender, E., Richards, D., Bilgin, A., Thompson, W. F., & Heiden, W. (2012). VirSchool: The effect of background music and immersive display systems on memory for facts learned in an educational virtual environment. Computers & Education, 58(1), 490–500. https://doi.org/10.1016/j.compedu.2011.09.002

    Article  Google Scholar 

  • Fastrich, G. M., Kerr, T., Castel, A. D., & Murayama, K. (2018). The role of interest in memory for trivia questions: An investigation with a large-scale database. Motivation Science. https://doi.org/10.1037%2Fmot0000087

  • Feng, H., Golshan, H. M., & Mahoor, M. H. (2018). A wavelet-based approach to emotion classification using EDA signals. Expert Systems with Applications, 112(Complete), 77–86. https://doi.org/10.1016/j.eswa.2018.06.014

    Article  Google Scholar 

  • Feng, S., Suri, R., & Bell, M. (2014). Does classical music relieve math anxiety? Role of tempo on price computation avoidance. Psychology & Marketing, 31(7), 489–499. https://doi.org/10.1002/mar.20710

    Article  Google Scholar 

  • Fernández-Sotos, A., Fernández-Caballero, A., & Latorre-Postigo, J. M. (2016). Influence of tempo and rhythmic unit in musical emotion regulation. Frontiers in Computational Neuroscience. https://doi.org/10.3389/fncom.2016.00080

    Article  PubMed  PubMed Central  Google Scholar 

  • Fiedler, K., & Beier, S. (2014). Affect and cognitive processes in educational contexts. In L. Linnenbrink-Garcia & R. Pekrun (Eds.), International handbook of emotions in education (pp. 36–55). Routledge.

    Google Scholar 

  • Frenzel, A., & Stephens, E. (2013). Emotions. In N. C. Hall & T. Götz (Eds.), Emotion, motivation, and self regulation: A handbook for teachers (1st ed.). Emerald.

    Google Scholar 

  • Gabrielsson, A., & Juslin, P. N. (2003). Emotional expression in music. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 503–534). Oxford University Press.

    Google Scholar 

  • Gagnon, L., & Peretz, I. (2003). Mode and tempo relative contributions to “happy-sad” judgements in equitone melodies. Cognition and Emotion, 17(1), 25–40. https://doi.org/10.1080/02699930302279

    Article  PubMed  Google Scholar 

  • Gervais, M., & Fessler, D. (2017). On the deep structure of social affect: Attitudes, emotions, sentiments, and the case of “contempt.” Behavioral and Brain Sciences, 40, E225. https://doi.org/10.1017/S0140525X16000352

    Article  PubMed  Google Scholar 

  • Gillis, A. (2010). Effect of background music on reading comprehension and self-report of college students (Unpublished Masters Thesis). Florida State University, Tallahassee, USA.

  • Goetz, T., Zirngibl, A., Pekrun, R., & Hall, N. (2003). Emotions, learning and Achievement from an educational-psychological perspective. In P. Mayring & C. von Rhoeneck (Eds.), Learning emotions: the influence of affective factors on classroom learning. Frankfurt am Main: Peter Lang.

    Google Scholar 

  • Gosselin, K. P., Holland, B., Mulcahy, A., Williamson, S., & Widacki, A. (2016). Music for anxiety reduction and performance enhancement in nursing simulation. Clinical Simulation in Nursing, 12(1), 16–23. https://doi.org/10.1016/j.ecns.2015.12.002

    Article  Google Scholar 

  • Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. (2015). A multi componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48(Complete), 615–625. https://doi.org/10.1016/j.chb.2015.02.013

    Article  Google Scholar 

  • Harley, J. M., Jarrell, A., & Lajoie, S. P. (2019). Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation. Instructional Science, 47(2), 151–180. https://doi.org/10.1007/s11251-018-09480-z

    Article  Google Scholar 

  • Heagerty, P. J., & Zeger, S. L. (2000). Marginalized multilevel models and likelihood inference. Statistical Science, 15(1), 1–26. http://www.jstor.org/stable/2676670

  • Husain, G., Thompson, W. F., & Schellenberg, E. G. (2002). Effects of musical tempo and mode on arousal, mood, and spatial abilities. Music Perception: An Interdisciplinary Journal, 20(2), 151–171. https://doi.org/10.1525/mp.2002.20.2.151

    Article  Google Scholar 

  • Jarrell, A., & Lajoie, S. P. (2017). The regulation of achievements emotions: Implications for research and practice. Canadian Psychology, 58(3), 276–287.

    Article  Google Scholar 

  • Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really Need? Educational Psychology Review, 23(1), 1–19. https://doi.org/10.1007/s10648-010-9150-7

    Article  Google Scholar 

  • Kämpfe, J., Sedlmeier, P., & Renkewitz, F. (2011). The impact of background music on adult listeners: A meta analysis. Psychology of Music39(4), 424–448. https://doi.org/10.1177%2F0305735610376261

  • Kim, J., & Andre, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067–2083. https://doi.org/10.1109/tpami.2008.26

    Article  PubMed  Google Scholar 

  • Kreibig, S. D., Samson, A. C., & Gross, J. J. (2015). The psychophysiology of mixed emotional states: Internal and external replicability analysis of a direct replication study. Psychophysiology, 52(7), 873–886. https://doi.org/10.1111/psyp.12425

    Article  PubMed  Google Scholar 

  • Kuribayashi, R., & Nittono, H. (2015). Speeding up the tempo of background sounds accelerates the pace of behavior. Psychology of Music43(6), 808–817. https://doi.org/10.1177%2F0305735614543216

  • Kwon, J., Kim, D., Park, W., & Kim, L. (2016). A wearable device for emotional recognition using facial expression and physiological response. In 2016 38th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5765–5768).

  • Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2020). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 70, 101272. https://doi.org/10.1016/j.learninstruc.2019.101272

    Article  Google Scholar 

  • Lehmann, J. A. M., & Seufert, T. (2017). The influence of background music on learning in the light of different theoretical perspectives and the role of working memory capacity. Frontiers in Psychology, 8, 1902. https://doi.org/10.3389/fpsyg.2017.01902

    Article  PubMed  PubMed Central  Google Scholar 

  • Lerner, J. S., Dahl, R. E., Hariri, A. R., & Taylor, S. E. (2006). Facial expressions of emotion reveal neuroendocrine and cardiovascular stress responses. Biological Psychiatry, 61, 253–260. https://doi.org/10.1016/j.biopsych.2006.08.016

    Article  CAS  PubMed  Google Scholar 

  • Li, Z., & McKeague, I. W. (2013). Power and sample size calculations for generalized estimating equations via local asymptotics. Statistica Sinica, 23(1), 231–250. https://doi.org/10.5705/ss.2011.081

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  • Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., & Bartlett, M. (2011). The Computer Expression Recognition Toolbox (CERT). In IEEE international conference on automatic face and gesture recognition, Santa Barbara, 21–25 March 2011, 298–305. https://doi.org/10.1109/fg.2011.5771414

  • Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002

  • Lorah, J. (2018). Effect size measures for multilevel models: Definition, interpretation, and TIMSS example. Large-Scale Assess Educ, 6, 8. https://doi.org/10.1186/s40536-018-0061-2

    Article  Google Scholar 

  • Luauté, J., Dubois, A., Heine, L., Guironnet, C., Juliat, A., Gaveau, V., et al. (2018). Electrodermal reactivity to emotional stimuli in healthy subjects and patients with disorders of consciousness. Annals of Physical and Rehabilitation Medicine, 61(6), 401–406. https://doi.org/10.1016/j.rehab.2018.04.007

    Article  PubMed  Google Scholar 

  • Magdin, M., Benko, Ľ, & Koprda, Š. (2019). A case study of facial emotion classification using Affdex. Sensors, 19(9), 2140. https://doi.org/10.3390/s19092140

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Mason, L., Scrimin, S., Tornatora, M. C., & Zaccoletti, S. (2017). Emotional reactivity and comprehension of multiple online texts. Learning and Individual Differences, 58, 10–21. https://doi.org/10.1016/j.lindif.2017.07.002

    Article  Google Scholar 

  • McAuley, J. D., Henry, M. J., & Tkach, J. (2012). Tempo mediates the involvement of motor areas in beat perception. Annals of the New York Academy of Sciences, 1252(1), 77–84. https://doi.org/10.1111/j.1749-6632.2011.06433.x

    Article  PubMed  ADS  Google Scholar 

  • McRae, K., & Gross, J. J. (2020). Emotion regulation. Emotion, 20, 1–9.

    Article  PubMed  Google Scholar 

  • Muis, K. R., Pekrun, R., Sinatra, G. M., Azevedo, R., Trevors, G., Meier, E., & Heddy, B. C. (2015). The curious case of climate change: Testing a theoretical model of epistemic beliefs, epistemic emotions, and complex learning. Learning and Instruction, 39(Complete), 168–183. https://doi.org/10.1016/j.learninstruc.2015.06.003

    Article  Google Scholar 

  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

    Article  Google Scholar 

  • Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In L. Linnenbrink-Garcia & R. Pekrun (Eds.), International handbook of emotions in education (pp. 120–141). Routledge.

    Chapter  Google Scholar 

  • Picard, R. W., Fedor, S., & Ayzenberg, Y. (2016). Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review8(1), 62–75. https://doi.org/10.1177%2F1754073914565517

  • Preece, D. A., Becerra, R., Robinson, K., & Gross, J. J. (2019). The Emotion Regulation Questionnaire: Psychometric properties in general community samples. Journal of Personality Assessment, 102, 348–356. https://doi.org/10.1080/00223891.2018.1564319

    Article  PubMed  Google Scholar 

  • Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714

    Article  Google Scholar 

  • Sahebdel, S., & Khodadust, M. (2014). The effect of background music while silent reading on EFL learners’ reading comprehension. Journal of Applied Linguistics, 7(14), 102–119.

    Google Scholar 

  • Stöckli, S., Schulte-Mecklenbeck, M., Borer, S., & Samson, A. C. (2018). Facial expression analysis with AFFDEX and FACET: A validation study. Behavior Research Methods, 50(4), 1446–1460. https://doi.org/10.3758/s13428-017-0996-1

    Article  PubMed  Google Scholar 

  • Su, Y.-N., Kao, C.-C., Hsu, C.-C., Pan, L.-C., Cheng, S.-C., & Huang, Y.-M. (2017). How does Mozart’s music affect children’s reading? The evidence from learning anxiety and reading rates with e-Books. Journal of Educational Technology & Society20(2), 101–112. http://www.jstor.org/stable/90002167

  • Thompson, W., Schellenberg, E., & Letnic, A. (2011). Fast and loud background music disrupts reading comprehension. Psychology of Music40(6), 700–708. https://doi.org/10.1177%2F0305735611400173

  • Ünal, A. B., de Waard, D., Epstude, K., & Steg, L. (2013). Driving with music: Effects on arousal and performance. Transportation Research Part f: Psychology and Behaviour, 21(Complete), 52–65. https://doi.org/10.1016/j.trf.2013.09.004

    Article  Google Scholar 

  • Vasilev, M. R., Kirkby, J. A., & Angele, B. (2018). Auditory distraction during reading: A bayesian meta-analysis of a continuing controversy. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 13(5), 567–597. https://doi.org/10.1177/1745691617747398

    Article  PubMed  Google Scholar 

  • Villanueva, I., Campbell, B. D., Raikes, A. C., Jones, S. H., & Putney, L. G. (2018). A multimodal exploration of engineering students’ emotions and electrodermal activity in design activities. Journal of Engineering Education, 107(3), 414–441. https://doi.org/10.1002/jee.20225

    Article  Google Scholar 

  • Vogl, E., Pekrun, R., Murayama, K., & Loderer, K. (2020). Surprised–curious–confused: Epistemic emotions and knowledge exploration. Emotion, 20(4), 625–641. https://doi.org/10.1037/emo0000578

    Article  PubMed  Google Scholar 

  • Völker, J. (2019). Personalising music for more effective mood induction: Exploring activation, underlying mechanisms, emotional intelligence, and motives in mood regulation. Musicae Scientiae. https://doi.org/10.1177/1029864919876315

    Article  Google Scholar 

  • Watson, D., & Clark, L. A. (1994). The PANAS-X: Manual for the positive and negative affect schedule – Expanded form. https://doi.org/10.17077/48vt-m4t2

  • Wolfe, D. E. (1983). Effects of music loudness on task performance and self-report of college-aged students. Journal of Research in Music Education31(3), 191–201. https://doi.org/10.2307%2F3345172

  • Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121–130. https://doi.org/10.2307/2531248

    Article  CAS  PubMed  Google Scholar 

  • Zeger, S. L., & Liang, K. (1992). An overview of methods for the analysis of longitudinal data. Statistics in Medicine, 11(14–15), 1825–1839. https://doi.org/10.1002/sim.4780111406

    Article  CAS  PubMed  Google Scholar 

  • Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics, 44(4), 1049–1060. https://doi.org/10.2307/2531734

    Article  MathSciNet  CAS  PubMed  Google Scholar 

Download references

Funding

Funding was provided by Social Sciences and Humanities Research Council of Canada (Grant No. 72050390)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthew Moreno.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial conflicts of interest to disclose that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 88 kb)

Appendix

Appendix

Wolfe/Gillis Questionnaire

  1. 1.

    Did the musical selection interfere with your reading?

  2. 2.

    How much did you like the musical selection that was played?

  3. 3.

    How often do you listen to music while working/studying?

  4. 4.

    I performed better on my tasks when I had music

  5. 5.

    I find listening to music while working/studying to be distracting

  6. 6.

    Do you enjoy listening to music while working/studying?

  7. 7.

    I prefer listening to fast music while working/studying

  8. 8.

    I do not prefer listening to fast music while working/studying

  9. 9.

    I performed better on tasks when I was listening to slow music

  10. 10.

    I performed poorly on tasks when I was listening to fast music

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moreno, M., Woodruff, E. Examining the effects of tempo in background music on adolescent learners’ reading comprehension performance: employing a multimodal approach. Instr Sci 52, 71–88 (2024). https://doi.org/10.1007/s11251-023-09639-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-023-09639-3

Keywords

Navigation