Virtual learning simulations in high school: Effects on cognitive and non-cognitive outcomes and implications on the development of STEM academic and career choice
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The present study compared the value of using a virtual learning simulation compared to traditional lessons on the topic of evolution, and investigated if the virtual learning simulation could serve as a catalyst for STEM academic and career development, based on social cognitive career theory. The investigation was conducted using a crossover repeated measures design based on a sample of 128 high school biology/biotech students. The results showed that the virtual learning simulation increased knowledge of evolution significantly, compared to the traditional lesson. No significant differences between the simulation and lesson were found in their ability to increase the non-cognitive measures. Both interventions increased self-efficacy significantly, and none of them had a significant effect on motivation. In addition, the results showed that the simulation increased interest in biology related tasks, but not outcome expectations. The findings suggest that virtual learning simulations are at least as efficient in enhancing learning and self-efficacy as traditional lessons, and high schools can thus use them as supplementary educational methods. In addition, the findings indicate that virtual learning simulations may be a useful tool in enhancing student's interest in and goals toward STEM related careers.
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Most previous studies that have compared simulations to more conventional learning methods report a positive main effect of media on self-efficacy.
“Most previous studies that have compared simulations to more conventional learning methods report a positive main e ff ect of media on self-e ffi cacy (Makransky et al., 2016; Thisgaard & Makransky, 2017).”
From Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment (2019) · cites this paper as Thisgaard & Makransky, 2017
The Discussion (Cognitive and Non-cognitive Outcomes of the Two Interventions; page unavailable) says the simulation’s increase in self-efficacy aligns with previous research. But the Results (Research Question 2; page unavailable) find no significant difference in self-efficacy gains between the simulation and traditional lesson. The paper therefore supports increases following simulations, not the claimed positive comparative main effect or the assertion that most previous studies found one. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSeveral studies have argued that VR simulations result in a significant increase in self-efficacy by allowing students to actively interact with the virtual environment.
“Several studies have argued that VR simulations result in a signi fi cant increase in self-e ffi cacy by allowing students to actively interact with the virtual environment (Makransky et al., 2016; Parong & Mayer, 2018; Thisgaard & Makransky, 2017; Thompson & Dass, 2000).”
From Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment (2019) · cites this paper as Thisgaard & Makransky, 2017
The Results (Research Question 2; page not supplied) report a small, statistically significant increase in high-school students’ self-efficacy after the virtual learning simulation. The Introduction suggests positive learning experiences and performance feedback as reasons simulations may increase self-efficacy, but the paper does not establish that active interaction with the virtual environment caused the increase. (LLM, conf=0.87)
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Cites
Ryan, R. M., & Deci, E. L. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.68 · Cited by 27,815 · see citing works
Mayer, R. (2020) · doi:10.1017/9781316941355 · Cited by 7,777 · see citing works
The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior
Heckman, J. J., Stixrud, J., & Urzua, S. (2006) · Journal of Labor Economics · doi:10.1086/504455 · Cited by 4,427 · see citing works
The Declaration of Helsinki and public health
Williams, J. (2008) · Bulletin of the World Health Organization · doi:10.2471/blt.08.050955 · Cited by 986 · see citing works
Makransky, G., Lilleholt, L., & Aaby, A. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.02.066 · Cited by 424 · cited 1× · see citing works
“…Partial Credit Model (PCM; Masters, 1982) within the framework of Item Response Theory (Embretson and Reise, 2000) was assessed for each scale (for more information about validating scales using the PCM see: Pallant and Tennant, 2007; Makransky and Bilenberg, 2014; Makransky et al., 2014, 2017).”
MethodSelf-citationImproving biotech education through gamified laboratory simulations
Bonde, M. T., Makransky, G., Wandall, J., Larsen, M. V., Morsing, M., Jarmer, H., & Sommer, M. O. A. (2014) · Nature Biotechnology · doi:10.1038/nbt.2955 · Cited by 243 · see citing works
Self-citationMakransky, G., Thisgaard, M. W., & Gadegaard, H. (2016) · PLOS ONE · doi:10.1371/journal.pone.0155895 · Cited by 212 · see citing works
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Flagged first, then by citations
Research works printed but not linked: 23
- Bandura A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory, Vol. 1 Upper Saddle River, NJ: Prentice Hall
- Bandura A. (1999). Social cognitive theory: an agentic perspective. Asian J. Soc. Psychol. 2 21– 41. 10.1111/1467-839X.00024 11148297 · DOI · Paper page
- Bric J. D. Lumbard D. C. Frelich M. J. Gould J. C. (2015). Current state of virtual reality simulation in robotic surgery training: a review. Surg. Endosc. 30 2169– 2178. 10.1007/s00464-015-4517-y 26304107 · DOI
- D’Angelo C. Rutstein D. Harris C. Bernard R. Borokhovski E. Haertel G. (2013). Simulations for STEM Learning: Systematic Review and Meta-Analysis Executive Summary. Menlo Park, CA: SRI International.
- de Jong T. (2006). Technological advances in inquiry learning. Science 312 532– 533. 10.1126/science.1127750 16645080 · DOI · Paper page
- de Jong T. Linn M. C. Zacharia Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science (New York, N.Y.) 340 305– 308. 10.1126/science.1230579 23599479 · DOI · Paper page
- Deci E. Eghrari H. Patrick B. Leone D. (1994). Facilitating internalization: the self-determination theory perspective. J. Pers. 62 119– 142. 10.1111/j.1467-6494.1994.tb00797.x 8169757 · DOI · Paper page
- Diegelman N. M. Subich L. M. (2001). Academic and vocational interests as a function of outcome expectancies in social cognitive career theory. J. Vocat. Behav. 59 394– 405. 10.1006/jvbe.2001.1802 · DOI
- Fouad N. A. Smith P. L. (1996). A test of a social cognitive model for middle school students: math and science. J. Couns. Psychol. 43 338– 346. 10.1037/0022-0167.43.3.338 · DOI
- Furtak E. M. Seidel T. Iverson H. Briggs D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: a meta-analysis. Rev. Educ. Res. 82 300– 329. 10.3102/0034654312457206 · DOI · Paper page
- Heckman J. J. Kautz T. (2012). Hard evidence on soft skills. Labour Econ. 19 451– 464. 10.1016/j.labeco.2012.05.014 23559694 PMC3612993 · DOI · Paper page
- Honey M. A. Hilton M. L. (eds). (2011). “Learning science through computer games and simulations,” in Board on Science Education and Division of Behavioral and Social Sciences and Education (Washington, DC: The National Academies Press).
- Lent R. W. Brown S. D. Hackett G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. J. Vocat. Behav. 45 79– 122. 10.1006/jvbe.1994.1027 · DOI · Paper page
- Lent R. W. Lopez A. M. Lopez F. G. Sheu H.-B. (2008). Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. J. Vocat. Behav. 73 52– 62. 10.1016/j.jvb.2008.01.002 · DOI
- Lent R. W. Lopez F. G. Sheu H.-B. Lopez A. M. (2011). Social cognitive predictors of the interests and choices of computing majors: applicability to underrepresented students. J. Vocat. Behav. 78 184– 192. 10.1016/j.jvb.2010.10.006 · DOI
- Makransky G. Bilenberg N. (2014). Psychometric properties of the parent and teacher ADHD rating scale (ADHD-RS): measurement invariance across gender, age, and informant. Assessment 21 694– 705. 10.1177/1073191114535242 24852496 · DOI
- Makransky G. Bonde M. T. Wulff J. S. G. Wandall J. Hood M. Creed P. A. (2016a). Simulation based virtual learning environment in medical genetics counseling: an example of bridging the gap between theory and practice in medical education. BMC Med. Educ. 16 98 10.1186/s12909-016-0620-6 PMC4807545 27012245 · DOI · Paper page
- Makransky G. Rogers M. E. Creed P. A. (2014). Analysis of the construct validity and measurement invariance of the career decision self-efficacy scale: a rasch model approach. J. Career Assess. 2014 1– 16. 10.1177/1069072714553555 · DOI
- Masters G. N. (1982). A rasch model for partial credit scoring. Psychometrika 47 149– 174. 10.1007/BF02296272 · DOI · Paper page
- Pallant J. F. Tennant A. (2007). An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS). Br. J. Clin. Psychol. 46 1– 18. 10.1348/014466506X96931 17472198 · DOI
- Peters M. A. Araya D. (2011). Transforming american education: learning powered by technology. E Learn. Digit. Media 8 102– 105. 10.2304/elea.2011.8.2.102 · DOI
- Smetana L. K. Bell R. L. (2012). Computer simulations to support science instruction and learning: a critical review of the literature. Int. J. Sci. Educ. 34 1337– 1370. 10.1080/09500693.2011.605182 · DOI · Paper page
- Wang C. Y. Wu H. K. Lee S. W. Y. Hwang F. K. Chang H. Y. Wu Y. T. (2014). A review of research on technology-assisted school science laboratories. Educ. Technol. Soc. 17 307– 320.
Cited by
Sorted by citations
Makransky, G., & Lilleholt, L. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9581-2 · Cited by 796 · see citing works
Self-citationMakransky, G., Borre‐Gude, S., & Mayer, R. E. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12375 · Cited by 532 · see citing works
Self-citationMeyer, O. A., Omdahl, M. K., & Makransky, G. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103603 · Cited by 515 · see citing works
Self-citationLønne, T. F., Karlsen, H. R., Langvik, E., & Saksvik-Lehouillier, I. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17196 · Cited by 76 · see citing works
- Authors
- Thisgaard, M., & Makransky, G.
- Affiliations
- University of Southern Denmark (DK)
- Corresponding author
- Makransky, Guido
- ORCID
- Makransky, Guido
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2017-05-30
- Volume
- 8
- Pages
- 805-805
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC5447738
- OpenAlex
- W2618380872
- Cited by
- 91
- References
- 7
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 11 · 2023: 13 · 2022: 11 · 2021: 12 · 2020: 11 · 2019: 8 · 2018: 2 · 2017: 2
- FWCI
- 22.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Markedsmodningsfonden
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Safety Research › Career Development and Diversity
- Social Sciences › Education › Innovative Teaching Methodologies in Social Sciences
- Keywords
- evolution education, high school biology, STEM career choice, social cognitive career theory, self-efficacy, student motivation, outcome expectations, non-cognitive outcomes
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