Teaching climate change is complex because it requires a system-level understanding of many science disciplines and also because students may have preconceptions about climate change. Previous work shows students learn and retain science content better when they are engaged in the learning process. Active learning strategies engage students in learning science, but the engagement impact of active learning has not yet been assessed in a controlled environment using both biometric and self-reporting tools. Here, we analyze 52 university students’ engagement during several common active learning strategies in a controlled research setting. We collected biometric data from all participants with hand sensors that measured changes in skin conductance as a proxy for engagement. Participants self-reported their engagement as a control. The combined biometric and self-reported data show that skin conductance data matched self-reported engagement, confirming that skin conductance is a robust proxy for engagement. Overall, dialog was the most engaging activity, with engagement levels about 165% above baseline. Non-science majors had higher average engagement than science majors (137% vs. 53% above baseline, respectively). Notably, skin conductance data showed no statistically significant differences based on participants’ political or religious affiliations. In summary, our results demonstrate biometric sensors’ potential to measure and monitor engagement in a learning environment. Relevant for climate education, in-class dialog increases student engagement in learning climate science and is especially effective for non-science majors.
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Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am J Public Health 86(5):726–728
Aksit O, McNeal KS, Gold AU, Libarkin JC, Harris S (2018) The influence of instruction, prior knowledge, and values on climate change risk perception among undergraduates. J Res Sci Teach 55:550–572
Benedek M, Kaernbach C (2010) A continuous measure of phasic electrodermal activity. J Neurosci Methods 190:80–91
Blasco-Arcas L, Buil I, Hernández-Ortega B, Sese FJ (2013) Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Comp Educ 62:102–110
Bonwell CC, Eisen JA (1991) Active learning: creating excitement in the classroom. ASHEERIC Higher Education Report No 1. George Washington University, Washington, DC
Boucsein W (1992) Electrodermal activity. Plenum Press, New York, NY
Chi MTH (2009) Active-constructive-interactive: a conceptual framework for differentiating learning activities. Top Cogn Sci 1:73–105
Critchley HD (2002) Electrodermal responses: what happens in the brain. Neurosci 8(2):132–142
Dengler M (2008) Classroom active learning complemented by an online discussion forum to teach sustainability. J Geogr High Educ 32(3):481–494
Di Lascio E, Gashi S, Santini S (2018) Unobtrusive assessment of students’ emotional engagement during lectures using electrodermal activity sensors. P ACM Interact Mob Wear Ubiq Tech 2 103(3):2–21
Dolgin E (2014) Technology: dressed to detect. Nature 511:S16–S17
Finn JD, Zimmer KS (2012) Student engagement: what is it? Why does it matter? In: Christenson S, Reschly A, Wylie C (eds) Handbook of research on subject engagement springer. MA, Boston, pp 97–131
Fosnot CT, Perry RS (1996) Constructivism: a psychological theory of learning. In: Fosnot CT (ed) Constructivism: theory, perspectives, and practice. Teachers College Press, Teachers College, Columbia University, New York, NY
Fredricks JA, Blumenfeld PC, Paris AH (2004) Student engagement: potential of the concept, state of the evidence. Rev Educ Res 74(1):59–109
Freeman S, O’Connor E, Parks JW et al (2007) Prescribed active learning increases performance in introductory biology. Life Sci Educ 6:132–139
Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, Wenderoth MP (2014) Active learning increases student performance in science, engineering, and mathematics. P Natl Acad Sci USA 111(23):8410–8415
Gilbert LA, Stempien J, McConnell DA et al (2012) Not just “rocks for jocks”: who are introductory geology students and why are they here? J Geosci Educ 6:360–371
Graesser AC, Person NK (1994) Question asking during tutoring. Am Educ Res J 31:104–137
Haak DC, HilleRisLambers J, Pitre E, Freeman S (2011) Increased structure and active learning reduce the achievement gap in introductory biology. Science 332:1213–1217
Jensen JL, Kummer TA, Godoy PDDM (2015) Improvements from a flipped classroom may simply be the fruits of active learning. Life Sciences Education 14:1–12
Johnson M, Sinatra GM (2012) Use of task-value instructional inductions for facilitating engagement and conceptual change. Contemp Educ Psychol 30:51–63. https://doi.org/10.1016/j.cedpsych.20120.9003
Kontra C, Lyons DJ, Fischer SM, Beilock SL (2015) Physical experience enhances science learning. Psychol Sci 26(6):737–749. https://doi.org/10.1177/0956797615569355
Leiserowitz A, Maibach E, Roser-Renouf C, Feinberg G, Rosenthal S (2015) Climate change in the American mind: October, 2015. Yale Program on Climate Change Communication, Yale University and George Mason University, New Haven, CT
Libarkin JC, Gold AU, Harris SE, McNeal KS, Bowles RP (2018) A new, valid measure of climate change associations with risk perception. Clim Chang 150:403–416. https://doi.org/10.0007/s10584-018-2279-y
Linnenbrink-Garcia L, Rogat TK, Koskey KLK (2011) Affect and engagement during small group instruction. Contemp Educ Psychol 36:13–24
McConnell DA, Steer DN, Owens KD (2003) Assessment and active learning strategies for introductory geology courses. J Geosci Educ 51:205–216
McNeal K, Spry JM, Mitra R, Tipton JL (2014) Measuring subject engagement, knowledge, and perceptions of climate change in an introductory environmental geology course. J Geosci Educ 62:655–667
Morrison M, Duncan R, Parton K (2015) Religion does matter for climate change attitudes and behavior. PLoS One 10(8):e0134868
National Center for Education Statistics (2010) Classification of instructional programs. Washington, DC
Pekrun R (2006) The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ Psychol Rev 18:315–341
Poh M, Swenson NC, Picard RW (2010) A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE T Bio-Med Eng 57(5):1243–1252
Poh M-Z, Loddenkemper T, Reinsberger C, Swenson NC, Goyal S, Sabtala MC, Madsen JR, Picard RW (2012) Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia 53(5):e93–e97
Prince M (2004) Does active learning work? A review of the research. J Eng Educ 93(3):223–232
Sato K, Kang WH, Saga K, Sato KT (1989) Biology of sweat glands and their disorders I. Normal sweat gland function. J Am Acad Dermatol 20(4):537–563
Sinatra GM, Heddy BC, Lombardi D (2015) The challenges of defining and measuring subject engagement in science. Educ Psychol 50(1):1–13. https://doi.org/10.1080/00461520.2014.1002924
Smith MK, Vinson EL, Smith JA, Lewin JD, Stetzer MR (2014) A campus-wide study of STEM courses: new perspectives on teaching practices and perceptions. CBE-Life Sci 13:624–635
Tanner KD (2009) Talking to learn: why biology students should be talking in classrooms and how to make it happen. CBE-Life Sci Educ 8(2):89–94
Tytler R, Osborne J (2012) Student attitudes and aspirations towards science. In: Fraser BJ, Tobin K, McRobbie CJ (eds) Second international handbook of science education. Springer International, New York, pp 597–625
University of Colorado Boulder Data Analytics (2018) CU Boulder fall enrollment – campus total summary. https://www.colorado.edu/oda/institutional-research/student-data/enrollment/fall-census. Accessed 12 June 2019
Unsworth KL, Fielding KS (2014) It’s political: how the salience of one’s political identity change climate change beliefs and policy support. Glob Environ Chang 27:131–137
Van der Hoeven Kraft KJ, Srogi L, Husman J, Semken S, Fuhrman M (2011) Engaging students to learn through the affective domain: a new framework for teaching in the geosciences. J Geosci Educ 59(2):71–84
Watkins J, Mazur E (2013) Retaining students in science, technology, engineering, and mathematics (STEM) majors. J Coll Sci Teach 42(5):36–41
Yuretich RF, Khan SA, Leckie RM, Clement JJ (2001) Active-learning methods to improve student performance and scientific interest in a large introductory oceanography course. J Geosci Educ 49(2):111–119
Funding for ALM, JEK, and AUG was provided by the National Science Foundation CAREER award (number 1554659). SR was funded by the National Science Foundation Research Experience for Undergraduates (award numbers EAR 1757930 and EAR 1461281) and the Boulder Critical Zone Observatory (award number EAR 1331828).
This study received IRB approval from the University of Colorado Boulder (IRB #18-0360). All participants provided consent to participate in the study.
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Morrison, A.L., Rozak, S., Gold, A.U. et al. Quantifying student engagement in learning about climate change using galvanic hand sensors in a controlled educational setting. Climatic Change (2019). https://doi.org/10.1007/s10584-019-02576-6
- Active learning
- Climate change
- Higher education
- Geoscience education
- Student engagement