Student engagement, assessed using heart rate, shows no reset following active learning sessions in lectures
Autoři:
Diana K. Darnell aff001; Paul A. Krieg aff001
Působiště autorů:
Department of Cellular and Molecular Medicine, University of Arizona College of Medicine, Tucson, Arizona, United States of America
aff001
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225709
Souhrn
Heart rate can be used as a measure of cognitive engagement. We measured average student heart rates during medical school lecture classes using wristwatch-style monitors. Analysis of 42 classes showed a steady decline in heart rate from the beginning to end of a lecture class. Active learning sessions (peer-discussion based problem solving) resulted in a significant uptick in heart rate, but this returned to the average level immediately following the active learning period. This is the first statistically robust assessment of changes in heart rate during the course of college lecture classes and indicates that personal heart rate monitors may be useful tools for assessment of different teaching modalities. The key findings suggest that the value of active learning within the classroom resides in the activity itself and not in an increase in engagement or reset in attention during the didactic period following an active learning session.
Klíčová slova:
Heart rate – Human learning – Learning – Lectures – Medical education – Problem solving – Statistical data – Universities
Zdroje
1. Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, et al. Active learning increases student performance in science, engineering, and mathematics. Proc Natl Acad Sci U S A. 2014;111: 8410–15. doi: 10.1073/pnas.1319030111 24821756
2. Kvam PH. The relationship between active learning and long-term retention in an introductory statistics course. International Statistical Institute. 52nd session. 1999.
3. Felder RM, Woods DR, Stice JE, Rugarcia A. The Future of Engineering Education: II. Teaching Methods that Work. Chemical Engineering Education. 2000;34: 26–39.
4. Pérez-Sabater C, Montero-Fleta B, Pérez-Sabater M, Rising B. Active learning to improve long-term knowledge retention. Proceedings of the XII Simposio Internacional de Comunicación Social. 2011;75–79.
5. Gross D, Pietri ES, Anderson G, Moyano-Camihort K, and Graham MJ. Increased preclass preparation underlies student outcome improvement in the flipped classroom. CBE-Life Sci Educ. 2015;14:ar36,1–8. doi: 10.1187/cbe.15-02-0040 26396151
6. Cavanagh AJ, Aragon OR, Chen X, Couch BA, Durham MF, Bobrownicki A, et al. Student buy-in to active learning in a college science course. CBE-Life Sci Educ. 2017;15:ar76, 1–9.
7. Jensen JL, Kummer TA, d M Godoy PD. Improvements from a flipped classroom may simply be the fruits of active learning. CBE-Life Sci Educ. 2015;14:ar5, 1–12. doi: 10.1187/cbe.14-08-0129 25699543
8. Beatty MJ. Receiver Apprehension as a Function of Cognitive Backlog. Western J of Speech Communication. 1981;45: 277.
9. King PE, Behnke RR. Effects of Communication Load, Affect, and Anxiety on the Performance of Information Processing Tasks. Communication Quarterly. 2000;48: 74–84.
10. Bradbury NA. Attention span during lectures: 8 seconds, 10 minutes, or more? Advances in Physiology Education. 2016;40: 509–513. doi: 10.1152/advan.00109.2016 28145268
11. Johnstone AH, Percival F. Attention breaks in lectures. Education in Chemistry. 1976;13: 49–50.
12. Middendorf J, Kalish A. The “Change-up” in Lectures. TRC Newsletter, 1994;1–13.
13. Middendorf J, Kalish A. The “Change-up” in Lectures. TRC Newsletter, 1996;8: 1–5.
14. Bligh DA. What’s the Use of Lectures? Jossey-Bass Publishers, SF; 1998.
15. Bunce DM, Flens EA, Neiles KY. How long can students pay attention in class? A study of student attention decline using clickers. J Chem Educ. 2010;87: 1438–1443.
16. Pecchinenda A. The affective significance of skin conductance activity during a difficult problem-solving task. Cognition and Emotion. 1996;10: 481–503.
17. Marci CD. A biologically based measure of emotional engagement: Context Matters. J Advert Res. 2006;46: 381–387.
18. McNeal KS, Spry JM, Mitra R, Tipton JL. Measuring Student Engagement, Knowledge, and Perceptions of Climate Change in an introductory environmental geology course. J. Geosci. Educ. 2014;62: 655–667.
19. Maier KJ, Waldstein SR, Synowski SJ. Relation of cognitive appraisal to cardiovascular reactivity, affect, and task engagement. Ann. Behav. Med. 2003;26: 32–41. doi: 10.1207/S15324796ABM2601_05 12867352
20. Bligh DA. What's the Use of Lectures. DA and B Bligh, Publishers, Devon, UK; 1971.
21. Hartley TR, Ginsburg GP, Heffner K. Self presentation and cardiovascular reactivity. International J. Psychophysiology. 1999;32: 75–88
22. Cranford KN, Tiettmeyer JM, Chuprinko BC, and Grove NP. Measuring Load on working memory: The use of heart rate as a means of measuring chemistry students’ cognitive load. J Chem Educ. 2014;91: 641–647.
23. Hess EH, Polt JM. Pupil size in relation to mental activity during simple problem-solving. Science. 1964;143: 1190–1192. doi: 10.1126/science.143.3611.1190 17833905
24. Beatty J. Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin 1982;91: 276–292. 7071262
25. Kahneman D. Attention and Effort. Prentice-Hall; 1973.
26. Kahneman D, Beatty J. Pupil Diameter and Load on Memory. Science. 1966;154: 1583–1585. doi: 10.1126/science.154.3756.1583 5924930
27. Hopstaken JF, van der Linden D, Bakker AB, Kompier MA. A multifaceted investigation of the link between mental fatigue and task disengagement. Psychophysiology. 2015;52: 305–15. doi: 10.1111/psyp.12339 25263028
28. Luque-Casado A, Zabala A, Morales E, Mateo-March M, Sanabria D. Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level. PLoS One. 2013;8: e56935. doi: 10.1371/journal.pone.0056935 23437276
29. Pendleton DM, Sakalik ML, Moore ML, Tomporowski PD. Mental engagement during cognitive and psychomotor tasks: Effects of task type, processing demands, and practice. Int. J. Psychophys. 2016;109: 124–131.
30. Siennicka A, Quintana DS, Fedurek P, Wijata A, Paleczny B, Ponikowska B, et al. Resting heart rate variability, attention and attention maintenance in young adults. Int J, physchophysiology. 2019;143, 126–131.
31. Forte G, Favieri F, Casagrande M. Heart Rate Variability and Cognitive Function: A Systematic Review. Front Neurosci. 2019;13: 710, 1–11. doi: 10.3389/fnins.2019.00710 31354419
32. Tursky B, Shapiro D, Crider A, Kahneman D. Pupillary, heart rate, and skin resistance changes during a mental task. J Exp Psychol. 1969;79: 164–7. doi: 10.1037/h0026952 5785627
33. Julius S. Role of the sympathetic nervous system in the pathophysiology of cardiovascular disease. Am. Heart J. 1987;114: 232–234. doi: 10.1016/0002-8703(87)90970-7 3604881
34. Scholey AB, Moss MC, Neave N. Wesnes K. Cognitive performance, hyperoxia, and heart rate following oxygen administration in healthy young adults. Physiol Behav. 1999;67: 783–789. doi: 10.1016/s0031-9384(99)00183-3 10604851
35. Mulder LJM. Measurement and Analysis Methods of Heart Rate and Respiration for Use in Applied Environments. Bio. Psychol. 1992;34: 205−236.
36. Sosnowski T, Krzywosz-Rynkiewicz B, Roguska J. Program running versus problem solving: Mental task effect on tonic heart rate. Psychophysiology. 2004;41: 467–475. doi: 10.1111/j.1469-8986.2004.00171.x 15102133
37. Fredericks TK, Choi SD, Hart J, Butt SE, Mital A. An investigation of myocardial aerobic capacity as a measure of both physical and cognitive workloads. International Journal of Industrial Ergonomics. 2005;35: 1097–1107.
38. Kahneman D. Thinking, fast and slow. New York: Farrar, Straus and Giroux; 2011.
39. Kennedy DO, Scholey A. Glucose administration, heart rate and cognitive performance: Effects of increasing mental effort. Psychopharmacology 2000;149: 63–71. doi: 10.1007/s002139900335 10789884
40. McLeish J. The lecture method. Cambridge Monographs on Teaching Methods, No 1. Cambridge, UK: Cambridge Institute of Education. 1968.
41. Lloyd DH. A concept of improvement of learning response in the taught lesson. Vis. Educ. 1968; October 23–25.
42. May CP, Hasher L, Stoltzfus ER. Optimal Time of Day and the Magnitude of Age Differences in Memory. Psychological Science, 1993;4: 326–330.
43. DeYoung CG, Hasher L, Djikic M, Criger B, Peterson JB. Morning people are stable people: Circadian rhythm and the higher-order factors of the Big Five. Personality and Individual Differences. 2007;43: 267–276.
44. Mackworth NH. Researches on the measurement of human performance. London. Her Majesty's Stationary Office. 1950.
45. Adams JA. A source of decrement in psychomotor performance. J Exp Psychol. 1955;48: 390–394.
46. Weaver RL Cotrell HW. Mental aerobics: The half-sheet response. Innovative Higher Education. 1985;10: 23–31.
47. Ruhl KL, Suritsky S. The pause procedure and/or an outline: Effect on immediate free recall and lecture notes taken by college students with learning disabilities. Learning Disability Quarterly. 1995;18: 2–11.
48. Freitas AL, Higgins ET. Enjoying Goal-Directed Action: The Role of Regulatory Fit. Psychological Science. 2002;13: 1–6. doi: 10.1111/1467-9280.00401 11892772
49. Degaute JP, van de Borne P, Linkowski P, Van Cauter E. Quantitative analysis of the 24-hour blood pressure and heart rate patterns in young men. Hypertension. 1991;18: 199–210. doi: 10.1161/01.hyp.18.2.199 1885228
50. Vandewalle G, Middleton B, Rajaratnam SM, Stone BM, Thorleifsdottir B, Arendt J, et al. Robust circadian rhythm in heart rate and its variability: influence of exogenous melatonin and photoperiod. J Sleep Res. 2007;16: 148–55. doi: 10.1111/j.1365-2869.2007.00581.x 17542944
51. Heseltine D, Potter JF, Hartley G, MacDonald IA, James OFW. Blood pressure, heart rate and neuroendocrine responses to a high carbohydrate and high fat meal in healthy young subjects. Clinical Science. 1990;79: 517–522. doi: 10.1042/cs0790517 2174321
52. Wilkinson RT. Rest pauses in a task affected by lack of sleep. Ergonomics. 1959;2: 373–380.
53. Prince M. Does active learning really work? A review of the research. J Eng Educ. 2004;93: 223–231.
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy