Abstract
Prior literature has begun to demonstrate that even young children can learn about complex systems using participatory simulations. This study disentangles the impacts of third-person perspectives (offered by traditional simulations) and first-person perspectives (offered by participatory simulations) on children’s development of such systems thinking in the context of the emergent complexity of honeybee nectar foraging. Specifically, we worked with three first-grade classrooms assigned to one of three conditions—instruction through use of a first-person perspective only, third-person perspective only, and integrated instruction—to engage ideas of complex systems thinking. In each condition, systems concepts were targeted through instruction and assessment. The integrated and third-person classrooms demonstrated significant gains while the first-person classroom showed gains that were not statistically significant, suggesting that third-person perspectives play a critical role in how children learn systems thinking. This work also puts forth a novel assessment design for young children using multiple-choice questions.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11251-020-09511-8/MediaObjects/11251_2020_9511_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11251-020-09511-8/MediaObjects/11251_2020_9511_Fig2_HTML.png)
Similar content being viewed by others
Notes
Previously, this was referred to as the object-oriented approach. Here we have changed to the object-directed approach to avoid confusion with the computer science notion of object-orientedness, which is unrelated.
We use the term “ability” here as part of a statistical term known as an “ability estimate” that allows the assessment results of individuals to be compared. This is not used as a reference to students’ dis/ability, and we take for granted that multiple choice scores are only one small factor in understanding an individual’s learning.
References
Assaraf, O. B.-Z., & Orion, N. (2010). System thinking skills at the elementary school level. Journal of Research in Science Teaching,47(5), 540–563.
Bergan-Roller, H. E., Galt, N. J., Chizinski, C. J., Helikar, T., & Dauer, J. T. (2018). Simulated computational model lesson improves foundational systems thinking skills and conceptual knowledge in biology students. BioScience,68, 612–621.
Berkson, J. (1994). Application of the logistic function to bio-assy. Journal of the American Statistical Association,39(227), 357–365.
Blikstein, P., Fuhrmann, T., & Salehi, S. (2016). Using the bifocal modeling framework to resolve “discrepant events” between physical experiments and virtual models in biology. Journal of Science Education and Technology,25(4), 513–526.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: L. Lawrence Earlbaum Associates.
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Belknap Press.
Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. Journal of the Learning Sciences,9(4), 471–500.
Danish, J. A. (2014). Applying an activity theory lens to designing instruction for learning about the structure, behavior, and function of a honeybee system. Journal of the Learning Sciences, 23(2), 100–148.
Danish, J. (2009). BeeSign: A design experiment to teach Kindergarten and first grade students about honeybees from a complex systems perspective. In Annual Meeting of the American Educational Research Association.
Danish, J., Peppler, K., & Phelps, D. (2010). BeeSign: Designing to support mediated group inquiry of complex science by early elementary students. In Proceedings of the 9th International Conference on Interaction Design and Children, Barcelona, Spain.
Danish, J., Peppler, K., Phelps, D., & Washington, D. (2011). Life in the hive: Supporting inquiry into complexity within the zone of proximal development. Journal of Science Education and Technology, 20(5), 454–467.
Danish, J., Saleh, A., Andrade, A., & Bryan, B. (2017). Observing complex systems thinking in the zone of proximal development. Instructional Science, 45(1), 5–24.
DeLiema, D., Saleh, A., Lee, C., Enyedy, N., Danish, J., Illum, R., Dahn, M., Humburg, M., & Mahoney, C. (2016). Blending play and inquiry in augmented reality: A comparison of playing a video game to playing within a participatory model. In C. K. Looi, J. L. Polman, U. Cress, & P. Reimann (Eds.). Transforming Learning, Empowering Learners: The International Conference of the Learning Sciences (ICLS) 2016 (Vol. 1). Singapore: International Society of the Learning Sciences.
Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5-8, 35-37. Retrieved November 16, 2017 from http://www.designbasedresearch.org/reppubs/DBRC2003.pdf.
Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit Oy.
Engeström, Y. (2008). From teams to knots: Activity-theoretical studies of collaboration and learning at work. Cambridge: Cambridge University Press.
Goldstone, R. L., & Wilensky, U. (2008). Promoting Transfer by Grounding Complex Systems Principles. Journal of the Learning Sciences, 17(4), 465–516. https://doi.org/10.1080/10508400802394898.
Greeno, J. G. (2006). Learning in activity. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 79–96). New York, NY: Cambridge University Press.
Grotzer, T. A., & Bell Basca, B. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding? Journal of Biological Education,38, 16–29. https://doi.org/10.1080/00219266.2003.9655891.
Grotzer, T. A., Powell, M. M., Derbiszewska, K. M., Courter, C. J., Kamarainen, A. M., Metcalf, S. J., et al. (2015). Turning transfer inside out: The affordances of virtual worlds and mobile devices in real world contexts for teaching about causality across time and distance in ecosystems. Technology, Knowledge and Learning,20(1), 43–69.
Grotzer, T. A., Solis, S. L., Tutwiler, M. S., & Cuzzolino, M. P. (2017). A study of students’ reasoning about probabilistic causality: Implications for understanding complex systems and for instructional design. Instructional Science,45(1), 25–52.
Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. Journal of the Learning Sciences,15, 53–62.
Hmelo-Silver, C. E., Eberbach, C., & Jordan, R. (2014). Technology-supported inquiry for learning about aquatic ecosystems. Eurasia Journal of Mathematics, Science & Technology Education,10(5), 405–413.
Hmelo-Silver, C. E., Jordan, R., Eberbach, C., & Sinha, S. (2017). Systems learning with a conceptual representation: A quasi-experimental study. Instructional Science,45(1), 53–72.
Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences,16(3), 307–331. https://doi.org/10.1080/10508400701413401.
Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), 127–138. https://doi.org/10.1207/s15516709cog2801_7.
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34. https://doi.org/10.1207/s15327809jls1501_4.
Klopfer, E., Yoon, S., & Perry, J. (2005). Using palm technology in participatory simulations of complex systems: A new take on ubiquitous and accessible mobile computing. Journal of Science Education and Technology,14(3), 285–297.
Nelson, D. (2004). Design based learning delivers required standards in all subjects, K12. Journal of Interdisciplinary Studies.
Neulight, N., Kafai, Y. B., Kao, L., Foley, B., & Galas, C. (2007). Children's participation in a virtual epidemic in the science classroom: Making connections to natural infectious diseases. Journal of Science Education and Technology,16(1), 47–58.
Peppler, K., Danish, J., Zaitlen, B., Glosson, D., Jacobs, A., & Phelps, D. (2010). BeeSim: Leveraging wearable computers in participatory simulations with young children. In Proceedings of the 9th International Conference on Interaction Design and Children, Barcelona, Spain.
Peppler, K., Thompson, N., Danish, J., & Moczek, A. (2018). Comparing first- and third-person perspectives in early elementary learning of honeybee systems. In J. Kay & R. Luckin (Eds.), Rethinking learning in the digital age: Making the Learning Sciences count: The International Conference of the Learning Sciences (ICLS) 2018 (Vol. 3, pp. 512–518). London, UK: International Society of the Learning Sciences. ISBN: 978-1-7324672-2-4.
Resnick, M. (1999). Decentralized modeling and decentralized thinking. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in precollege science and mathematics (pp. 114–137). New York: Springer.
Roth, W.-M. (2007). On mediation: Toward a cultural-historical understanding. Theory & Psychology,17(5), 655–680.
Sandoval, W. (2014). Conjecture mapping: An approach to systematic educational design research. Journal of the Learning Sciences,23(1), 18–36.
Sandoval, W. A. (2004). Developing learning theory by refining conjectures embodied in educational designs. Educational psychologist,39(4), 213–223.
Seeley, T. D. (1995). The wisdom of the hive: The social physiology of honey bee colonies. Cambridge: Harvard University Press.
Skrondal, A., & Rabe-Hasketh, S. (2004). Generalized latent variable modeling. Interdisciplinary statistics series.
Stroup, W. M., & Wilensky, U. (2014). On the embedded complementarity of agent-based and aggregate reasoning in students' developing understanding of dynamic systems. Technology, Knowledge and Learning,19(1–2), 19–52.
Thompson, N., Peppler, K., & Danish, J. (2017). Designing BioSim: Playfully encouraging systems thinking in young children. In R. Zheng & M. Gardner (Eds.), Handbook of research on serious games for educational applications (Ch.7, pp. 149–167). Hershey, PA: IGI Global.
Vygotsky, L. S. (1978). Mind in society: The development of higher mental process. Cambridge, MA: Harvard University Press.
Wertsch, J. V. (2017). Mediated action. In W. Bechtel & G. Graham (Eds.), A companion to cognitive science (pp. 518–525). Malden, MA: Blackwell Publishing Ltd.
Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—An embodied modeling approach. Cognition and Instruction,24(2), 171–209.
Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology,8(1), 3–19.
Wilensky, U., & Stroup, W. (1999). Learning through participatory simulations: Network-based design for systems learning in classrooms. Paper presented at the Computer Support for Collaborative Learning (CSCL) 1999 Conference, Stanford University, Palo Alto, CA.
Wilson, M. (2004). Constructing measures: An item response modeling approach. New York: Routledge.
Witte, S. P., & Haas, C. (2005). Research in activity: An analysis of speed bumps as mediational means. Written Communication,22(2), 127–165.
Yoon, S. A., Goh, S.-E., & Park, M. (2018). Teaching and learning about complex systems in K–12 science education: A review of empirical studies 1995–2015. Review of Educational Research. https://doi.org/10.3102/0034654317746090.
Youngquist, J., & Pataray-Ching, J. (2004). Revisiting “play”: Analyzing and articulating acts of inquiry. Early Childhood Education Journal, 31(3), 171–178. https://doi.org/10.1023/B:ECEJ.0000012135.73710.0c.
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 1324047 awarded to Kylie Peppler, Joshua Danish, and Armin Moczek. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Thank you to Janis Watson as well as the many teachers and students who made this work possible. An earlier version of this paper was published in the 2018 International Conference of the Learning Sciences Proceedings.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no financial or other conflicts of interest involved in this study.
Ethical approval
This study was approved by the Institutional Review Board at Indiana University. Informed consent was received from all parents and informed assent was received from all youth participants included in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Multiple-choice items and simple/complex categories
Item | Category |
---|---|
1. This forager bee just came out of the beehive. Its job is to collect nectar. What will it do next? a. Fly behind another bee b. Look for a flower with nectar c. Visit a nearby picnic | Simple |
2. Why would bees go to this flower? a. It’s pretty b. They saw a waggle dance to this flower c. The queen told them to go there | Simple |
3. What will the bee do with the nectar it finds? a. Store it away b. Eat it c. Put it in another flower | Simple |
4. How does a bee know where to find nectar? a. The queen tells them b. By watching a waggle dance c. They have to guess every time | Simple |
5. This bee just found nectar at this flower! No other bees have found nectar. What will the bee do next? a. Take the nectar to the hive and come back for more b. Bring the nectar to the hive and tell the others where she found it c. Eat the nectar and look for more | Complex |
6. This bee visited a flower that doesn’t have nectar any more, then returned to the hive. What would the bee do? a. Tell others the flower is empty b. Watch a waggle dance c. Ask the queen | Simple |
7. This bee went to this flower, but there’s a spider nearby! The bee got away, what will the bee do next? a. Leave and find another flower b. Make up a new “don’t go there” signal c. Fly as far away as possible | Complex |
8. There are two flowers with nectar: Pink and Orange. This bee visited the orange flower, got nectar, and returned to the hive. Which flower will more bees go to over time? a. Pink, because it’s closer b. Orange, because it has better nectar c. Orange, because this bee will tell others about this flower | Complex |
9. This bee saw a waggle dance that said this flower had a lot of great nectar, but all the nectar was gone when it got there! If other bees saw the same waggle dance, what would they do? a. Another bee will stop them and tell them where a new flower is b. Go wherever the queen tells them to go c. Come to this flower because of the waggle dance. Then they’ll need to find a new one | Complex |
10. Why is it important for bees to collect nectar quickly? a. It’s actually not important to be fast b. The more nectar they collect, the more food they will have for the whole hive c. They need to keep the nectar away from other insects and animals | Complex |
11. What can make it hard for bees to find nectar? a. Predators might eat them b.They have to search all over c. All of the above | Simple |
12. This bee went to this flower, but there’s a spider nearby! The bee got away, will other bees go to this same flower with the spider nearby? a. No, the other bee signaled not to go there b. Yes, because they will fight the spider c. Maybe, but only if they had seen a waggle dance for that flower earlier | Complex |
13. This bee followed a waggle dance and got lots of great nectar! Would it also waggle dance when it got back to the hive? a. Yes! More waggle dances means more bees find the flower b. No! Only the first bee should waggle dance c. No! It would just go back to the flower by itself | Complex |
14. Which of these things in a bee’s surroundings might make it harder for a bee to find nectar? a. Trees and grass b. Butterflies and hummingbirds c. Strong winds and spiders | Simple |
15. Which of these things on a bee’s body might make it harder for a bee to find nectar? a. Breakable wings and small bodies b. Big head and long antennae c. Full thorax and heavy abdomen | Simple |
16. What is one thing we can tell by looking at patterns of lots of bees flying around? a. Their favorite colors b. When bees keep or stop waggle dancing for certain flowers c. If bees are trying to get away from predators | Complex |
17. What is one thing we can tell by watching one individual forager bee? a. Challenges the bee has to deal with b. How much nectar the bee has found in its lifetime c. Which other bees it spends time with | Complex |
18. Why would bees that are flying all over the place all start going to the same flower? a. They each decided on their own that flower looked the best b. More and more bees started waggle dancing for that flower c. All the other flowers died | Complex |
19. Which of these is a way we can tell a waggle-dancing honey beehive apart from a hive with bees that don’t waggle dance? a. Bees from a waggle dancing hive would collect more nectar, faster b. A waggle dancing hive would be bigger c. Bees from a not-dancing hive would have more energy | Complex |
20. Why is it important to learn about systems? a. Because bees are very interesting b. Because scientists told us that we should c. Because systems are everywhere, so it is important to explore how they work | Complex |
Rights and permissions
About this article
Cite this article
Peppler, K., Thompson, N., Danish, J. et al. Comparing first- and third-person perspectives in early elementary learning of honeybee systems. Instr Sci 48, 291–312 (2020). https://doi.org/10.1007/s11251-020-09511-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11251-020-09511-8