Sigrid Hackl-Wimmer1*
Helmut Karl Lackner2
Marina Tanja Waltraud Eglmaier2
Lars Eichen3
Manuela Paechter1*- 1Department of Educational Psychology, Institute of Psychology, University of Graz, Graz, Austria
- 2Division of Physiology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria
- 3Department of Education Research and Teacher Education, University of Graz, Graz, Austria
Introduction
Educational psychology has always been focused on improving learning and teaching practices (Münsterberg, 1914) by considering teachers' and learners' experiences, from children to adults. Its thematic focus has shifted over time to capture the growing complexity in learning and instruction. As a result, the range of methods has expanded: in addition to observations, (semi-)projective tests and questionnaires (McClelland et al., 1953; Schunk et al., 2007), psychophysiological measurements (Spangler et al., 2002) are also used in educational psychology to investigate performance and associated processes. This paper focuses on the latter with special consideration of the electrocardiogram (ECG), which provides valuable insights into learning processes.
Added value of psychophysiological measurements
Knowledge of psychophysiology helps us understand processes crucial for learning, which underly psychological constructs such as drive, motivation, attitude, and emotion, and how they influence learning (Ax, 1964). Objectively measured psychophysiological data supports identifying risk factors, individual differences, and changes (over time). Moreover, they enable a more comprehensive understanding of individual responses and ultimately support the validity of the research results. Integrating objective/psychophysiological and subjective/self-reported data allows us to grasp the functional and practical dimensions of human behavior and underlying mental processes.
Non-invasive methods, e.g., functional magnetic resonance (fMRI; Murayama et al., 2010), or electroencephalography (EEG; Papousek et al., 2019), provide information on neural functionality and cortical activity. Methods like ECG (Spangler, 1997), skin conductance response (SCR; Järvenoja et al., 2020), or salivary cortisol (Kärner et al., 2017; Spangler et al., 2002), capture arousal and activation more easily. All these measurements record processes individuals may not be able to report on (Lackner et al., 2015; Wimmer et al., 2019), and cannot be distorted by social desirability (Pekrun, 2023). ECG offers significant advantages concerning high temporal resolution and precision, enabling real-time and highly accurate examination of heart function (Lackner et al., 2014; Vetta et al., 2023). This versatility underlines its importance for interdisciplinary studies, the complex interactions between cardiovascular physiology, autonomic functions (Kreibig, 2010), and emotions, which is crucial for understanding the links between physiology and psychology in learning.
Autonomic nervous system functions
The autonomic nervous system (ANS) regulates vital basic functions such as neuroendocrine processes, organ function, adaptive responses to stress, or cardiovascular dynamics (Benarroch, 1993; Park and Thayer, 2014). The latter also concerns heart rate (HR) as well as heart rate variability (HRV). Whereas HR is quantified in beats per minute (bpm), and demonstrates rapid responsiveness to physiological and cognitive stimuli, HRV is indicative of the heart's capacity to adapt to environmental demands, reflecting alterations in the beat-to-beat rhythm (Berntson et al., 1997). The sympathetic nervous system (SNS) governs arousal states, mobilizing resources in response to stressors, while the parasympathetic branch (PNS) promotes restorative functions during calm periods (Lovallo and Sollers, 2000). Both systems modulate HR and HRV, with sympathetic dominance increasing cardiac activity and parasympathetic dominance decreasing it.
Concerning the analysis, HR is a recognized measure of arousal linked to mental demands, while HRV analysis offers various parameters, including time-domain and frequency-domain variables, reflecting different responses to stimuli or ANS balance (Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996). Different coping processes, such as active coping associated with SNS dominance, and passive coping linked to heightened PNS activity, can be observed through increased HR or the heart's influence on blood pressure (Obrist et al., 1978). Moreover, cardiovascular reactivity reflects stress-induced changes, indicating adaptive resource use (McEwen, 1998; Richter et al., 2008), while recovery shows adaptability through baseline and post-stressor differences (Linden et al., 1997).
The role of ECG in research in educational psychology
The examination of HR or cardiovascular arousal has revealed its association with the allocation of resources for the preparation and execution of actions (McEwen, 1998; Richter et al., 2008). Elevated HR during cognitive tasks indicates increased cerebral blood flow and activation of brain systems that aid in maintaining focus (Carroll et al., 2012; Duschek et al., 2010). A stronger HR response to cognitive demands is linked to greater effort and better task performance. Studies on students' cardiovascular responses found increased HR during oral presentations, with higher intensity observed when presenting to an audience compared to a laboratory setting (Kamarck et al., 2000; Turner et al., 1990). Moreover, research demonstrated higher HR reactivity during exams, with students reporting elevated state anxiety before real performance situations. Additionally, less resilient students exhibited no vagal recovery following an oral exam (Spangler, 1997). Another study found that lower vagal tone at the beginning of the semester predicted higher test anxiety at its end (Grabo et al., 2025), reinforcing the link between autonomic regulation and academic stress.
Further results demonstrate that motivation and (academic) emotions significantly influence students' cognition in typical school situations and regular classroom engagement: Mastromatteo et al. (2022), observed that students who were better able to regulate their psychophysiological responses (i.e., vagal tone) to stressful tasks, also showed higher participation in classroom activities. Thus, an improvement in coping with challenging tasks may also have beneficial effects on active participation in class. Moreover, interactive teaching methods in online learning environments have been shown to increase psychophysiological arousal, reflecting higher levels of engagement and cognitive activation (Gellisch et al., 2023). This underscores the importance of incorporating active learning elements into digital education, especially as distance learning becomes more prevalent. For good educational outcomes and student wellbeing, it is important to maintain student motivation by encouraging both cognitive challenge and active participation through sufficiently challenging tasks (Minkley et al., 2017). Cardiovascular recovery studies further underline the importance of addressing cognitive and emotional links between delayed recovery and ruminative thinking (Radstaak et al., 2011), providing insights into (mal)adaptive coping strategies in students. These findings emphasize the need to teach students effective stress management techniques such as mindfulness or cognitive reframing to reduce rumination and promote recovery from academic challenges. At the same time, they provide information on how to design learning environments that can teach and promote active engagement, healthy self-regulation and self-concept as well as adaptive coping strategies.
Overall, this body of research sheds light on how (learning) stimuli can evoke cognitive patterns reflected in cardiac responses, enhancing the value of ECG data alongside self-reported information. Additionally, the classroom, as a multi-actor system, emphasizes the crucial role of teachers in students' emotions too (Mainhard et al., 2022). Wellbeing in the classroom also includes teachers' psychophysiological data, as their HR reactivity reflects professional commitment. The integration of objective and subjective data contributes to understanding the perspectives of learners and teachers in typical or challenging educational situations, thereby expanding our exploration of learning processes.
Moreover, many conclusions and critical insights into students' stress levels and engagement may not be as clearly highlighted without psychophysiological measures. ECG measurements are widely used in research due to the availability of user-friendly systems and high-precision portable devices (Chan et al., 2022; Vetta et al., 2023). Their use is also beneficial in educational contexts such as classrooms (Kanna et al., 2018) and at university (Wimmer et al., 2019), as they are easy to use, have few limitations and enable real-time monitoring of psychophysiological processes. For use in schools, initial structures need to be established, as ECG protocols necessitate age- and gender-specific reference values, standardized data collection methods, and as far as possible automated analysis. But even more important than comprehensive implementation of ECG measures is that they are available when needed and, that the findings from representative studies are incorporated into teacher training and pedagogical concepts. So far, most research has focused on university students. However, issues such as exam anxiety could be counteracted earlier if there were more studies with large school student samples. Thus, on one hand, schools should be better involved in research and on the other hand, single measurements at schools make especially sense on a needs-oriented basis if targeted (individual) interventions can follow clarification.
Remaining limitations and challenges
Despite the insights gained from ECG data in research, their integration entails increased effort, particularly concerning the investigation, analysis and interpretation, and implementation.
ECG data collection may lead to increased costs for researchers and participants. This includes financial expenses (Pekrun, 2023) for staff and equipment, compensation for participants, and additional time required for data collection and providing study information to each participant.
Analyzing ECG data requires an understanding of the electrophysiology of the heart and the principles of signal processing to correctly interpret the influence of the ANS. Selecting relevant parameters for analysis is crucial to address the research question effectively. Factors such as age, gender (Bonnemeier et al., 2003), the cognitive demand of the stimulus, the stimulus's ability to reflect cardiovascular alterations (Lackner et al., 2014), and the absence of a baseline reference (e.g., Mainhard et al., 2022; Turner et al., 1990) influence ECG characteristics and require careful consideration during technical analysis. Adjustments in HRV conventions and frequency bands may be needed based on sample characteristics (Lackner et al., 2020). Although automated ECG analyses are already quite advanced and efficient, certain expertise is still needed for specific adaptations. Interpreting HRV frequency-domain variables necessitates a deeper understanding of physiological and mathematical principles, with recommended adjustments based on sample age. Thereby, adjusting time resolutions according to expected effects can prevent confounding factors, such as HR mean values obscuring short-term effects in cognitively demanding paradigms (Lackner et al., 2014).
Successful ECG research or implementation in educational context depend—apart from the legal and financial requirements—on acceptance factors as well as on the direct use/knowledge gained of the results. First, awareness concerning the responsibility of educational institutions must be created, then responsibilities need to be clarified and finally training for the people involved is necessary (Cohen et al., 2017). The process of understanding and acceptance requires corresponding structures, time, and intensive communication of the benefits. Especially in schools with limited resources, several challenges may arise concerning study feasibility, cultural and ethical considerations. One major limitation is financial constraints when ECG devices are not funded. The cost of medical-grade ECG machines and consumables, such as electrodes and gels, can further restrict large-scale studies (e.g., Maurizi et al., 2023). Time constraints can complicate participation, as both students and teachers often have demanding schedules (Mukherjee, 2019). Finally, time and financial resources become a problem if the extra work for teachers cannot be compensated.
Ethical considerations
Interpreting and classifying psychophysiological measures require additional information (Friedman et al., 2014), such as on the specific task (e.g., mental stress), the body position (e.g., sitting), age, gender, and health status (e.g., chronic illnesses, regular medication). Participants need to provide informed consent concerning the data collected, and experimenters-in-charge need to handle them responsibly because a data breach could result in the misuse of person related sensitive, as well as heath data. While there are well-established rules of good scientific practice for researchers, who are generally well trained, all of these have yet to be developed for educational context.
In addition to ethical considerations, cultural challenges also arise as ECG measurements involve direct skin contact which may cause skepticism or discomfort (Cohen et al., 2017). Given the potential inconvenience that ECG measurements may cause for participants, the effort for everyone involved, and the fact that a lot of personal data is collected, recordings must be conducted in a well-considered manner, ensuring participant protection, confidentiality, and follow-up medical care if needed.
Conclusion and future perspectives
This opinion paper aims to show how ECG data can enhance educational psychology research by providing insights into psychophysiological processes involved in learning. While self-assessment questionnaires can allocate data to specific personality traits, ECG recordings offer additional information about perceptions and cognitions in the learning context in relation to individual characteristics. The comprehensive understanding that ECG data provide, makes its use in educational psychology highly valuable. We therefore agree with Pekrun (2023) that establishing a consistent knowledge base on psychophysiological data is essential and requires further research to develop uniform standards.
Finally, efforts should not only aim to increase validity, but also to ensure that the findings are used to improve learning processes and teaching practices and are consistent with the principles of educational psychology.
Author contributions
SH-W: Conceptualization, Writing – original draft, Writing – review & editing, Formal analysis, Methodology, Project administration, Resources, Supervision. HL: Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing. ME: Supervision, Writing – review & editing, Conceptualization, Writing – original draft. LE: Supervision, Writing – original draft, Writing – review & editing. MP: Resources, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. The authors acknowledge the financial support by the University of Graz.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Ax, A. F. (1964). Goals and methods of psychophysiology. Psychophysiology 1, 8–25. doi: 10.1111/j.1469-8986.1964.tb02616.x
Benarroch, E. E. (1993). The central autonomic nervous network: functional organization, dysfunction, and perspective. Mayo Clin. Proc. 68, 988–1001. doi: 10.1016/S0025-6196(12)62272-1
Berntson, G. G., Bigger, J. T. Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., et al. (1997). Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34, 623–648. doi: 10.1111/j.1469-8986.1997.tb02140.x
Bonnemeier, H., Richardt, G., Potratz, J., Wiegand, U. K., Brandes, A., Kluge, N., et al. (2003). Circadian profile of cardiac autonomic nervous modulation in healthy subjects: differing effects of aging and gender on heart rate variability. J. Cardiovasc. Electrophysiol. 14, 791–799. doi: 10.1046/j.1540-8167.2003.03078.x
Carroll, D., Phillips, A. C., and Lovallo, W. R. (2012). “The behavioral and health corollaries of blunted physiological reactions to acute psychological stress: Revising the reactivity hypothesis,” in How motivation affects cardiovascular response: Mechanisms and applications, eds. R. A. Wright and G. H. E. Gendolla (New York: American Psychological Association), 243–263. doi: 10.1037/13090-012
Chan, P. Y., Ryan, N. P., Chen, D., McNeil, J., and Hopper, I. (2022). Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis. Anaesthesia 77, 1268–1280. doi: 10.1111/anae.15834
Cohen, L., Manion, L., and Morrison, K. (2017). Research Methods in Education (8th ed.). London: Routledge. doi: 10.4324/9781315456539
Duschek, S., Heiss, H., Schmidt, M. F., Werner, N. S., and Schuepbach, D. (2010). Interactions between systemic hemodynamics and cerebral blood flow during attentional processing. Psychophysiology 47, 1159–1166. doi: 10.1111/j.1469-8986.2010.01020.x
Friedman, B. H., Stephens, C. L., and Thayer, J. F. (2014). Redundancy analysis of autonomic and self-reported, responses to induced emotions. Biol. Psychol. 98, 19–28. doi: 10.1016/j.biopsycho.2013.12.006
Gellisch, M., Morosan-Puopolo, G., Wolf, O. T., Moser, D. A., Zaehres, H., and Brand-Saberi, B. (2023). Interactive teaching enhances students' physiological arousal during online learning. Ann. Anat. Anatomischer Anzeiger 247:152050. doi: 10.1016/j.aanat.2023.152050
Grabo, L. M., Schulz, A., and Bellingrath, S. (2025). Vagally-mediated heart rate variability longitudinally predicts test anxiety in university students. Anxiety Stress Coping 2, 1–14. doi: 10.1080/10615806.2025.2460230
Järvenoja, H., Järvel,ä, S., and Malmberg, J. (2020). Supporting groups' emotion and motivation regulation during collaborative learning. Lear. Instr. 70:101090. doi: 10.1016/j.learninstruc.2017.11.004
Kamarck, T. W., Debski, T. T., and Manuck, S. B. (2000). Enhancing the laboratory-to-life generalizability of cardiovascular reactivity using multiple occasions of measurement. Psychophysiology 37, 533–542. doi: 10.1111/1469-8986.3740533
Kanna, S., von Rosenberg, W., Goverdovsky, V., Constantinides, A. G., and Mandic, D. P. (2018). Bringing wearable sensors into the classroom: a participatory approach. IEEE Signal Process. Mag. 35, 110–130. doi: 10.1109/MSP.2018.2806418
Kärner, T., Sembill, D., Aßmann, C., Friederichs, E., and Carstensen, C. H. (2017). Analysis of person-situation interactions in educational settings via cross-classified multilevel longitudinal modeling: illustrated with the example of students' stress experience. Frontl. Lear. Res. 5, 16–42. doi: 10.14786/flr.v5i1.137
Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: a review. Biol. Psychol. 84, 394–421. doi: 10.1016/j.biopsycho.2010.03.010
Lackner, H. K., Batzel, J. J., Rössler, A., Hinghofer-Szalkay, H., and Papousek, I. (2014). Multi-time scale perspective in analyzing cardiovascular data. Physiol. Res. 63, 439–456. doi: 10.33549/physiolres.932603
Lackner, H. K., Eglmaier, M. T. W., Hackl-Wimmer, S., Paechter, M., Rominger, C., Eichen, L., et al. (2020). How to use heart rate variability: quantification of vagal activity in toddlers and adults in long-term ECG. Sensors 20:5959. doi: 10.3390/s20205959
Lackner, H. K., Gramer, M., Paechter, M., Wimmer, S., Hinghofer-Szalkay, H., and Papousek, I. (2015). Academic goal orientation and cardiovascular reactivity in a performance situation. Appl. Psychophysiol. Biofeedback 40, 189–200. doi: 10.1007/s10484-015-9287-8
Linden, W., Earle, T. L., Gerin, W., and Christenfeld, N. (1997). Physiological stress reactivity and recovery: conceptual siblings separated at birth? J. Psychosom. Res. 42, 117–135. doi: 10.1016/S0022-3999(96)00240-1
Lovallo, W. R., and Sollers, J. J. (2000). “Autonomic nervous system,” in Encyclopedia of Stress, ed. G. Fink (New York: Academic Press).
Mainhard, T., Donker, M. H., and van Gog, T. (2022). When closeness is effortful: Teachers' physiological activation undermines positive effects of their closeness on students. Br. J. Educ. Psychol. 92, 1384–1402. doi: 10.1111/bjep.12506
Mastromatteo, L. Y., Peruzza, M., and Scrimin, S. (2022). Improvement in parasympathetic regulation is associated with engagement in classroom activity in primary school children experiencing poor classroom climate. Br. J. Educ. Psychol. 93, 10–15. doi: 10.1111/bjep.12501
Maurizi, N., Fumagalli, C., Skalidis, I., Muller, O., Armentano, N., Cecchi, F., et al. (2023). Layman electrocardiographic screening using smartphone-based multiple-lead ECG device in school children. Int. J. Cardiol. 373, 142–144. doi: 10.1016/j.ijcard.2022.12.017
McClelland, D. C., Atkinson, J. W., Clark, R. A., and Lowell, E. L. (1953). Century Psychology Series. The Achievement Motive. Norwalk: Appleton-Century-Crofts. doi: 10.1037/11144-000
McEwen, B. S. (1998). Stress, adaptation, and disease. Allostasis and allostatic load. Ann. N. Y. Acad. Sci. 840, 33–44. doi: 10.1111/j.1749-6632.1998.tb09546.x
Minkley, N., Ringeisen, T., Josek, L. B., and Kaerner, T. (2017). Stress and emotions during experiments in biology classes: does the work setting matter? Contemp. Educ. Psychol. 49, 238–249. doi: 10.1016/j.cedpsych.2017.03.002
Mukherjee, S. P. (2019). A Guide to Research Methodology: An Overview of Research Problems, Tasks and Methods (1st ed.). London: CRC Press. doi: 10.1201/9780429289095
Murayama, K., Matsumoto, M., Izuma, K., and Matsumoto, K. (2010). Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proc. Nat. Acad. Sci. 107, 20911–20916. doi: 10.1073/pnas.1013305107
Obrist, P. A., Gaebelein, C. J., Teller, E. S., Langer, A. W., Grignolo, A., Light, K. C., et al. (1978). The relationship among heart rate, carotid dP/dt, and blood pressure in humans as a function of the type of stress. Psychophysiology 15, 102–2115. doi: 10.1111/j.1469-8986.1978.tb01344.x
Papousek, I., Wimmer, S., Lackner, H. K., Schulter, G., Perchtold, C. M., and Paechter, M. (2019). Trait positive affect and students' prefrontal EEG alpha asymmetry responses during a simulated exam situation. Biol. Psychol. 148:107762. doi: 10.1016/j.biopsycho.2019.107762
Park, G., and Thayer, J. F. (2014). From the heart rate to the mind: Cardiac vagal tone modulates topdown and bottom-up visual perception and attention to emotional stimuli. Front. Psychol. 5:278. doi: 10.3389/fpsyg.2014.00278
Pekrun, R. (2023). Mind and body in students' and teachers' engagement: New evidence, challenges, and guidelines for future research. Br. J. Educ. Psychol. 93, 227–238. doi: 10.1111/bjep.12575
Radstaak, M., Geurts, S. A. E., Brosschot, J. F., Cillessen, A. H. N., and Kompier, M. A. J. (2011). The role of affect and rumination in cardiovascular recovery from stress. Int. J. Psychophysiol. 81, 237–244. doi: 10.1016/j.ijpsycho.2011.06.017
Richter, M., Friedrich, A., and Gendolla, G. H. (2008). Task difficulty effects on cardiac activity. Psychophysiology 45, 869–875. doi: 10.1111/j.1469-8986.2008.00688.x
Schunk, D. H., Pintrich, P. R., and Meese, J. (2007). Motivation in Education: Theory, Research, and Applications (3rd ed.). Hoboken: Prentice Hall.
Spangler, G. (1997). Psychological and physiological responses during an exam and their relation to personality characteristics. Psychoneuroendocrinology 22, 423–441. doi: 10.1016/S0306-4530(97)00040-1
Spangler, G., Pekrun, R., Kramer, K., and Hofmann, H. (2002). Students' emotions, physiological reactions, and coping in academic exams. Anxiety Stress Coping 15, 413–432. doi: 10.1080/1061580021000056555
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 93, 1043–1065.
Turner, J., Girdler, S., Sherwood, A., and Light, K. (1990). Cardiovascular responses to behavioral stressors: laboratory-field generalization and inter-task consistency. J. Psychosom. Res. 34, 581–589. doi: 10.1016/0022-3999(90)90033-Z
Vetta, G., Magnocavallo, M., Parlavecchio, A., Caminiti, R., Polselli, M., Sorgente, S., et al. (2023). Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation in primary prevention: a meta-analysis. Europace 25:63. doi: 10.1093/europace/euad122.063
Keywords: ECG, psychophysiological data, educational psychology research, learning processes, motivation, objective/subjective data
Citation: Hackl-Wimmer S, Lackner HK, Eglmaier MTW, Eichen L and Paechter M (2025) Utilizing ECG data in educational psychology research: insights and limitations. Front. Psychol. 16:1440721. doi: 10.3389/fpsyg.2025.1440721
Received: 06 July 2024; Accepted: 23 April 2025;
Published: 09 May 2025.
Edited by:
Douglas F. Kauffman, Consultant, Greater Boston Area, United StatesReviewed by:
Hasan Kazdaǧli, İzmir University of Economics, TürkiyeCopyright © 2025 Hackl-Wimmer, Lackner, Eglmaier, Eichen and Paechter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sigrid Hackl-Wimmer, c2lncmlkLmhhY2tsLXdpbW1lckB1bmktZ3Jhei5hdA==; Manuela Paechter, bWFudWVsYS5wYWVjaHRlckB1bmktZ3Jhei5hdA==