The study of complex systems has been highlighted in recent science education policy in the United States and has been the subject of important real-world scientific investigation. Because of this, research on complex systems in K–12 science education has shown a marked increase over the past two decades. In this systematic review, we analyzed 75 empirical studies to determine whether the research (a) collectively represents the goals of educational policy and real-world science, (b) has considered a variety of settings and populations, and (c) has demonstrated systematic investigation of interventions with a view to scale. Results revealed needs in five areas of research: a need to diversify the knowledge domains within which research is conducted, more research on learning about system states, agreement on the essential features of complex systems content, greater focus on contextual factors that support learning including teacher learning, and a need for more comparative research.
AldunateR.NussbaumM. (2013). Teacher adoption of technology. Computers in Human Behavior, 29, 519–524. doi:10.1016/j.chb.2012.10.017
2.
AllisonS. D.MartinyJ. B. H. (2008). Resistance, resilience, and redundancy in microbial communities. Proceedings of the National Academy of Sciences of the United States of America, 100, 11512–11519. doi:10.1073/pnas.0801925105
3.
American Association for the Advancement of Science. (1989). Science for all Americans. New York, NY: Oxford University Press.
4.
American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York, NY: Oxford University Press.
5.
*AssarafO. B.OrionN. (2005). Development of system thinking skills in the context of earth system education. Journal of Research in Science Teaching, 42, 518–560. doi:10.1002/tea.20061
6.
*AssarafO. B.OrionN. (2010). System thinking skills at the elementary school level. Journal of Research in Science Teaching, 47, 540–563. doi:10.1002/tea.20351
7.
*AssarafO. B.OrpazI. (2010). The “life at the poles” study unit: Developing junior high school students’ ability to recognize the relations between earth systems. Research in Science Education, 40, 525–549.
8.
AzevedoR.CromleyJ. G.WintersF. I. (2005). Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science, 33, 381–412. doi:10.1007/s11251-005-1273-8
9.
AzevedoR.MoosD. C.GreeneJ. A.WintersF. I.CromleyJ. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia?Educational Technology Research & Development, 56, 45–72. doi:10.1007/s11423-007-9067-0
10.
AzevedoR.WintersF. I.MoosD. C. (2004). Can students collaboratively use hypermedia to learn science? The dynamics of self-and other-regulatory processes in an ecology classroom. Journal of Educational Computing Research, 31, 215–245. doi:10.2190/hft6-8eb1-tn99-mjvq
11.
Bar-YamY. (2016). From big data to important information. Complexity, 21(Suppl. 2), 73–98. doi:10.1002/cplx.21785
12.
*BasuS.SenguptaP.BiswasG. (2015). A scaffolding framework to support learning of emergent phenomena using multi-agent-based simulation environments. Research in Science Education, 45, 293–324. doi:10.1007/s11165-014-9424-z
13.
*Ben-Zvi-AssarafO.OrionN. (2010). Four case studies, six years later: Developing system thinking skills in junior high school and sustaining them over time. Journal of Research in Science Teaching, 47, 1253–1280. doi:10.1002/tea.20383
14.
BettencourtL. M. A.BrelsfordC. (2014). Industrial ecology the view from complex systems. Journal of Industrial Ecology, 19, 195–197. doi:10.1111/jiec.12243
15.
BhattaS. R.GoelA. (1997). Learning generic mechanisms for innovative strategies in adaptive design. Journal of the Learning Sciences, 6, 367–396.
16.
BradyC.HolbertN.SoyluF.NovakM.WilenskyU. (2015). Sandboxes for model-based inquiry. Journal of Science Education and Technology, 24, 265–286. doi:10.1007/s10956-014-9506-8
17.
BransfordJ. D.BrownA. L.CockingR. R. (1999). How experts differ from novices. In BransfordJ. D.BrownA. L.CockingR. R. (Eds.), How people learn: Brain, mind, experience, and school (pp. 19–38). Washington, DC: National Academies Press.
18.
BrownJ. S.CollinsA.DuguidP. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. doi:10.3102/0013189x018001032
BrykA. S.GomezL. M.GrunowA.LeMahieuP. (2015). Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.
21.
*Calderon-CanalesE.Flores-CamachoF.Gallegos-CazaresL. (2013). Elementary students’ mental models of the solar system. Astronomy Education Review, 12(1). doi:10.3847/aer2012044
22.
CapraF. (1982). The turning point. New York, NY: Bantam Books.
23.
*CarlssonB. (2002). Ecological understanding 2: Transformation—a key to ecological understanding. International Journal of Science Education, 24, 701–715. doi:10.1080/09500690110098877
24.
CascheraF.BedauM. A.BuchananA.CawseJ.de LucreziaD.GazzikamG.. . . PackardN. H. (2011). Coping with complexity: Machine learning optimization of cell-free protein synthesis. Biotechnology and Bioengineering, 108, 2218–2228. doi:10.1002/bit.23178
25.
*ChengM. M. W.GilbertJ. K. (2015). Students’ visualization of diagrams representing the human circulatory system: The use of spatial isomorphism and representational conventions. International Journal of Science Education, 37, 136–161. doi: 10.1080/09500693.2014.969359
26.
*ChiM. T. H.RoscoeR.SlottaJ.RoyM.ChaseM. (2012). Misconceived causal explanations for “emergent” processes. Cognitive Science, 36, 1–61. doi:10.1111/j.1551-6709.2011.01207.x
27.
CreswellJ. W.Plano ClarkV. L. (2011). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.
28.
*DanishJ. 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, 100–148. doi:10.1080/10508406.2013.856793
29.
*DanishJ. A.PepplerK.PhelpsD.WashingtonD. (2011). Life in the hive: Supporting inquiry into complexity within the zone of proximal development. Journal of Science Education and Technology, 20, 454–467. doi:10.1007/s10956-011-9313-4
30.
*DeBoerG. E.QuellmalzE. S.DavenportJ. L.TimmsM. J.Herrmann-AbellC. F.BuckleyB. C.FlanaganJ. C. (2014). Comparing three online testing modalities: Using static, active, and interactive online testing modalities to assess middle school students’ understanding of fundamental ideas and use of inquiry skills related to ecosystems. Journal of Research in Science Teaching, 51, 523–554. doi:10.1002/tea.21145
31.
*DeLeoG.WeidenhammerA.WechtK. (2012). A computer story: Complexity from simplicity. Science Teacher, 79(7), 48–53. doi:10.1057/9781137006226
32.
*EilamB. (2002). Strata of comprehending ecology: Looking through the prism of feeding relations. Science Education, 86, 645–671. doi:10.1002/sce.10041
33.
*EilamB. (2012). System thinking and feeding relations: Learning with a live ecosystem model. Instructional Science, 40, 213–239. doi:10.1007/s11251-011-9175-4
34.
ErtmerP. A.Ottenbreit-LeftwichA. T.SendururE.SendururP. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59, 423–435. doi:10.1016/j.compedu.2012.02.001
35.
*EvagorouM.KorfiatisK.NicolaouC.ConstantinouC. (2009). An investigation of the potential of interactive simulations for developing system thinking skills in elementary school: A case study with fifth-graders and sixth-graders. International Journal of Science Education, 31, 655–674. doi:10.1080/09500690701749313
36.
ForresterJ. W. (1994). Systems dynamics, systems thinking, and soft OR. Systems Dynamics Review, 10(2), 1–14. doi.org/10.1002/sdr.4260100211
37.
FuentesM. A. (2014). Complexity and the emergence of physical properties. Entropy, 16, 4489-4496. doi:10.3390/e16084489
38.
*GazitE.YairY.ChenD. (2005). Emerging conceptual understanding of complex astronomical phenomena by using a virtual solar system. Journal of Science Education and Technology, 14, 459–470. doi:10.1007/s10956-005-0221-3
39.
GerardL. F.VarmaK.CorlissS. B.LinnM. (2011). Professional development for technology-enhanced inquiry science. Review of Educational Research, 81, 408–448. doi:10.3102/0034654311415121
40.
*GinnsI. S.NortonS. J.McrobbieC. J. (2005). Adding value to the teaching and learning of design and technology. International Journal of Technology and Design Education, 15, 47–60. doi:10.1007/s10798-004-6193-4
41.
*GotwalsA. W.SongerN. B. (2010). Reasoning up and down a food chain: Using an assessment framework to investigate students’ middle knowledge. Science Education, 94, 259–280. doi:10.1002/sce.20368
42.
GreeneJ. A.MoosD. C.AzevedoR.WintersF. I. (2008). Exploring differences between gifted and grade-level students’ use of self-regulatory learning processes with hypermedia. Computers & Education, 50, 1069–1083. doi:10.1016/j.compedu.2006.10.004
43.
GreenoJ. (1998). The situativity of knowing, learning, and research. American Psychologist, 53, 5–26. doi:10.1037/0003-066x.53.1.5
44.
*GrotzerT. A.Bell BascaB. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding?Journal of Biological Education, 38, 16–29. doi:10.1080/00219266.2003.9655891
45.
*GrotzerT. A.PowellM. M.DerbiszewskaK. M.CourterC. J.KamarainenA. M.MetcalfS. J.DedeC. J. (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, 43–69. doi:10.1007/s10758-014-9241-5
46.
*GunckelK. L.CovittB. A.SalinasI.AndersonC. W. (2012). A learning progression for water in socio-ecological systems. Journal of Research in Science Teaching, 49, 843–868. doi:10.1002/tea.21024
47.
*HmeloC. E.HoltonD. L.KolodnerJ. L. (2000). Designing to learn about complex systems. Journal of the Learning Sciences, 9247–298. doi:10.1207/s15327809jls0903_2
48.
Hmelo-SilverC. E.AzevedoR. (2006). Understanding complex systems: Some core challenges. Journal of the Learning Sciences, 15, 53–61. doi:10.1207/s15327809jls1501_7
49.
*Hmelo-SilverC.LiuL.GrayS.JordanR. (2015). Using representational tools to learn about complex systems: A tale of two classrooms. Journal of Research in Science Teaching, 52, 6–35. doi:10.1002/tea.21187
50.
*Hmelo-SilverC.MaratheS.LiuL. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16, 307–331. doi:10.1080/10508400701413401
51.
*Hmelo-SilverC.PfefferM. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138. doi:10.1207/s15516709cog2801_7
52.
*HoganK. (2000). Assessing students’ systems reasoning in ecology. Journal of Biological Education, 35(1), 22–28. doi:10.1080/00219266.2000.9655731
53.
*HoganK.ThomasD. (2001). Cognitive comparisons of students’ systems modeling in ecology. Journal of Science Education and Technology, 10, 319–345. doi:10.1023/a:1012243102249
54.
HordijkW.KauffmanS. A.SteelM. (2011). Required levels of catalysis for emergence of autocatalytic sets in models of chemical reaction systems. International Journal of Molecular Sciences, 12, 3085–3101. doi:10.3390/ijms12053085
55.
*IoannidouI. A.ParaskevopoulosS.TzionasP. (2006). An interactive computer graphics interface for the introduction of fuzzy inference in environmental education. Interacting With Computers, 18, 683–708. https://doi.org/10.1016/j.intcom.2005.10.007
56.
*IoannidouA.RepenningA.WebbD.KeyserD.LuhnL.DaetwylerC. (2010). Mr. Vetro: A collective simulation for teaching health science. International Journal of Computer-Supported Collaborative Learning, 5, 141–166. doi:10.1007/s11412-010-9082-8
57.
Institute of Education Sciences & National Science Foundation. (2013). Common guidelines for education research and development. Washington, DC: Author.
58.
JacobsonM. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49. doi:10.1002/cplx.1027
59.
*JordanR.GrayS.DemeterM.LuiL.Hmelo-SilverC. (2009). An assessment of students’ understanding of ecosystem concepts: Conflating ecological systems and cycles. Applied Environmental Education and Communication, 8(1), 40–48. doi:10.1080/15330150902953472
60.
*JordanR. C.BrooksW. R.Hmelo-SilverC.EberbachC.SinhaS. (2014). Balancing broad ideas with context: An evaluation of student accuracy in describing ecosystem processes after a system-level intervention. Journal of Biological Education, 48(2), 57–62. doi:10.1080/00219266.2013.821080
*KeynanA.Ben-Zvi AssarafO.GoldmanD. (2014). The repertory grid as a tool for evaluating the development of students’ ecological system thinking abilities. Studies in Educational Evaluation, 41, 90–105. doi:10.1016/j.stueduc.2013.09.012
63.
*KlopferE.ScheintaubH.HuangW.WendelD.RoqueR. (2009). The simulation cycle: Combining games, simulations, engineering and science using StarLogo TNG. E-Learning, 6, 71–96. doi:10.2304/elea.2009.6.1.71
64.
*KlopferE.YoonS.PerryJ. (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, 285–297. doi:10.1007/s10956-005-7194-0
65.
*KlopferE.YoonS.UmT. (2005). Teaching complex dynamic systems to young students with StarLogo. Journal of Computers in Mathematics and Science Teaching, 24, 157–178. doi:10.1016/s0303-2647(03)00115-1
66.
KolodnerJ. (2004). The learning sciences: Past, present and future. Educational Technology, 44(3), 37–42. doi:10.5040/9781474258029.ch-015
67.
LandisJ. R.KochG. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. doi:10.2307/2529310
*LevyS. T.WilenskyU. (2008). Inventing a “mid level” to make ends meet: Reasoning between the levels of complexity. Cognition and Instruction, 26(1), 1–47. doi:10.1080/07370000701798479
70.
*LevyS. T.WilenskyU. (2009). Students’ learning with the connected chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18, 243–254. doi:10.1007/s10956-009-9145-7
71.
*LevyS. T.WilenskyU. (2011). Mining students’ inquiry actions for understanding of complex systems. Computers & Education, 56, 556–573. doi:10.1016/j.compedu.2010.09.015
72.
*LinC.HuR. (2003). Students’ understanding of energy flow and matter cycling in the context of the food chain, photosynthesis, and respiration. International Journal of Science Education, 25, 1529–1544. doi:10.1080/0950069032000052045
73.
*LiuL.Hmelo-SilverC. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 46, 1023–1040. doi:10.1002/tea.20297
74.
LewisM. (2000). The promise of dynamic systems approaches for an integrated account of human development. Child Development, 71, 36-43. doi:10.1111/1467-8624.00116
MitchellM. (2009). Complexity: A guided tour. New York, NY: Oxford University Press.
77.
*McNealK. S.LibarkinJ. C.LedleyT. S.BardarE.HaddadN.EllinsK.DuttaS. (2014). The role of research in online curriculum development: The case of EarthLabs climate change and earth system modules. Journal of Geoscience Education, 62, 560–577. doi:10.5408/13-060.1
78.
*MohanL.ChenJ.AndersonC. W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46, 675–698. doi:10.1002/tea.20314
79.
The National Academies. (2009). Keck Futures Initiative: Complex Systems: Task group summaries. Washington, DC: National Academies Press.
80.
National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. Committee on a Conceptual Framework for New K–12 Science Education Standards. Board on Science Education, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.
81.
National Research Council. (2011). Successful K–12 STEM education: Identifying effective approaches in science, technology, engineering, and mathematics. Washington, DC: National Academies Press.
82.
National Science Board. (2010). Preparing the next generation of STEM innovators: Identifying and developing our nation’s human capital. Washington, DC: National Science Foundation.
83.
NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: National Academies Press.
ParkerM.LeinhardtG. (1995). Percent: A privileged proportion. Review of Educational Research, 65, 421–481. doi:10.3102/00346543065004421
88.
ParnafesO. (2010). When simple harmonic motion is not that simple: Managing epistemological complexity by using computer-based representations. Journal of Science Education and Technology, 19, 565–579. doi:10.1007/s10956-010-9224-9
89.
President’s Council of Advisors on Science and Technology. (2010). Prepare and inspire: K–12 education in science, technology, engineering, and mathematics (STEM) for America’s future. Washington, DC: Author.
90.
PeaR. D. (2016). The prehistory of the learning sciences. In EvansM. A.PackerM. J.SawyerR. K. (Eds.), Reflections on the learning sciences (pp. 32–58). New York, NY: Cambridge University Press.
91.
*PennerD. E. (2000). Explaining systems: Investigating middle school students’ understanding of emergent phenomena. Journal of Research in Science Teaching, 37, 784–806. doi:10.1002/1098-2736(200010)37:8<784::aid-tea3>3.0.co;2-e
92.
PenuelW.FishmanB. (2012). Large-scale science education intervention research we can use. Journal of Research in Science Teaching, 49, 281–304. doi:10.1002/tea.21001
93.
*PepplerK.DanishJ. A.PhelpsD. (2013). Collaborative gaming: Teaching children about complex systems and collective behavior. Simulation & Gaming, 44, 683–705. doi:10.1177/1046878113501462
94.
*PerkinsD. N.GrotzerT. A. (2005). Dimensions of causal understanding: The role of complex causal models in students’ understanding of science. Studies in Science Education, 41(1), 117–165. doi:10.1080/03057260508560216
95.
*PlateR. (2010). Assessing individuals’ understanding of nonlinear causal structures in complex systems. System Dynamics Review, 26, 19–33. doi:10.1002/sdr.432
96.
*PukT.StibbardsA. (2011). Growth in ecological concept development and conceptual understanding in teacher education: The discerning teacher. International Journal of Environmental and Science Education, 6, 191–211. doi:10.1080/1533015x.2011.549796
97.
RandlerC.BognerF. X. (2009). Efficacy of two different instructional methods involving complex ecological content. International Journal of Science and Mathematics Education, 7, 315–337. doi:10.1007/s10763-007-9117-4
98.
*ReissM. J.TunnicliffeS. D. (2001). Students’ understandings of human organs and organ systems. Research in Science Education, 31, 383–399. doi:10.1023/ a:1013116228261
99.
*RepenningA.IoannidouA.LuhnL.DaetwylerC.RepenningN. (2010). Mr. Vetro: Assessing a collective simulation framework. Journal of Interactive Learning Research, 21, 515–537. doi:10.1007/s11412-010-9082-8
100.
*ResnickM. (1996). Beyond the centralized mindset. Journal of the Learning Sciences, 5(1), 1–22. doi:10.1207/s15327809jls0501_1
101.
*RichardsonM. L.HariJ. (2009). Building ecological complexity in the classroom using pea aphids & components of their community. American Biology Teacher, 71, 286–290. doi:10.2307/27669432
102.
*RiessW.MischoC. (2010). Promoting systems thinking through biology lessons. International Journal of Science Education, 32, 705–725. doi:10.1080/ 09500690902769946
103.
*RodríguezM.KohenR.DelvalJ. (2015). Children and adolescents’ thoughts on pollution: Cognitive abilities required to understand environmental systems. Environmental Education Research, 21(1), 76–91. doi.10.1080/13504622.2013.862613
104.
SabelliN. H. (2006). Complexity, technology, science, and education. Journal of the Learning Sciences, 15, 5–9. doi:10.1207/s15327809jls1501_3
105.
SahP.SinghL. O.ClausetA.BansalS. (2014). Exploring community structure in biological networks with random graphs. BMC Bioinformatics, 15(220). doi:10.1186/1471-2105-15-220
106.
SawyerR. K. (2015). Introduction: The new science of learning. In SawyerK. (Ed.), Cambridge handbook of the learning sciences (pp. 1–19). New York, NY: Cambridge University Press.
107.
ServedioM. R.BrandvainY.DholeS.FitzpatrickC. L.GoldbergE. E.SternC. A.Van CleveJ.YehD. J. (2014). Not just a theory: The utility of mathematical models in evolutionary biology. PLOS Biology, 12(12), 1–5. doi:10.1371/journal.pbio.1002017
108.
SherringtonD. (2010). Physics and complexity. Philosophical Transactions of the Royal Society of London Series A: Mathematical Physical and Engineering Sciences, 368(1914), 1175–1189. doi:10.1098/rsta.2009.0208
109.
SoleR. (2015). Bioengineering the biosphere?Ecological Complexity, 22, 40–49. doi:10.1016/j.ecocom.2015.01.005
110.
*SongerN. B.KelceyB.GotwalsA. W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46, 610–613. doi:10.1002/tea.20313
111.
*StavrouD.DuitR. (2014). Teaching and learning the interplay between chance and determinism in nonlinear systems. International Journal of Science Education, 36, 506–530. doi:10.1080/09500693.2013.802056
112.
*StavrouD.DuitR.KomorekM. (2008). A teaching and learning sequence about the interplay of chance and determinism in nonlinear systems. Physics Education, 43, 417–422. doi:10.1088/0031-9120/43/4/011
113.
*StroupW. M.WilenskyU. (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. doi:10.1007/s10758-014-9218-4
114.
SweeneyL. B.StermanJ. D. (2007). Thinking about systems: Student and teacher conceptions of natural and social systems. Systems Dynamics Review, 23, 285–311. doi.org/10.1002/sdr.366
115.
ThelanE.SmithL. (1994). A dynamic systems approach to the development of cognition and action. Cambridge: MIT Press.
116.
*ThompsonK.ReimannP. (2010). Pattern of use of an agent-based model and a system dynamics model: The application of patterns of use and the impacts on learning outcomes. Computer & Education, 54, 392–403. doi:10.1016/j.compedu .2009.08.020
117.
*TsurusakiB. K.AndersonC. W. (2010). Students’ understanding of connections between human engineered and natural environmental systems. International Journal of Environmental & Science Education, 5, 407–433. doi:10.1002/ 9781118230107.ch14
118.
*VachliotisT.SaltaK.TzougrakiC. (2014). Meaningful understanding and systems thinking in organic chemistry: Validating measurement and exploring relationships. Research in Science Education, 44, 239–266. doi:10.1007/s11165-013-9382-x
119.
*van BorkuloS. P.van JoolingenW. R.SavelsberghE. R.de JongT. (2012). What can be learned from computer modeling? Comparing expository and modeling approaches to teaching dynamic systems behavior. Journal of Science Education and Technology, 21, 267–275. doi:10.1007/s10956-011-9314-3
120.
*VarmaK.LinnM. C. (2012). Using interactive technology to support students’ understanding of the greenhouse effect and global warming. Journal of Science Education and Technology, 21, 453–464. doi:10.1007/s10956-011-9337-9
121.
*VattamS. S.GoelA. K.RugaberS.Hmelo-SilverC.JordanR.GrayS.SinhaS. (2011). Understanding complex natural systems by articulating structure-behavior-function models. Educational Technology & Society, 14, 66–81. doi:10.14786/flr.v1i1.14
122.
*VerhoeffR. P.WaarloA. J.BoersmaK. T. (2008) Systems modeling and the development of coherent understanding of cell biology. International Journal of Science Education, 30, 543–568. doi:10.1080/09500690701237780
123.
WestG. B. (2014). A theoretical physicists journey into biology: From quarks and strings to cells and whales. Physical Biology, 11(5), 1–6. doi:10.1088/1478-3975/11/5/053013
124.
WilenskyU.JacobsonM. J. (2015). Complex systems and the learning sciences. In SawyerK. (Ed.), Cambridge handbook of the learning sciences (pp. 319–338). New York, NY: Cambridge University Press.
125.
*WilenskyU.ResnickM. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19. doi:10.1023/a:1009421303064
126.
*WilenskyU.ReismanK. (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, 171–209. doi:10.1207/s1532690xci2402_1
127.
*Wilkerson-JerdeM. H.WilenskyU. J. (2015). Patterns, probabilities, and people: Making sense of quantitative change in complex systems. Journal of the Learning Sciences, 24, 204–251. doi:10.1080/10508406.2014.976647
128.
WilsonS. M. (2013). Professional development for science teachers. Science, 340, 310–313. doi:10.1126/science.1230725
129.
*WuH. (2010). Modelling a complex system: Using novice-expert analysis for developing an effective technology-enhanced learning environment. International Journal of Science Education, 32, 195–219. doi:10.1080/09500690802478077
130.
*YoonS. A. (2008). An evolutionary approach to harnessing complex systems thinking in the science and technology classroom. International Journal of Science Education, 30(1), 1–32. doi:10.1080/09500690601101672
131.
*YoonS. A. (2011). Using social network graphs as visualization tools to influence peer selection decision-making strategies to access information about complex socioscientific issues. Journal of the Learning Sciences, 20, 549–588. doi:10.1080/10508406.2011.563655
132.
YoonS. A. (in press). Complex systems and the Learning Sciences: Implications for learning, theory, and methodologies. In The international handbook of the learning sciences. New York, NY: Routledge.
133.
YoonS. A.Hmelo-SilverC. E. (2017). What do learning scientists do?Journal of the Learning Sciences, 26, 167–183. doi:10.1080/10508406.2017.1279546
134.
YoonS. A.KlopferE.AndersonE.Koehler-YomJ.SheldonJ.SchoenfeldI.. . . GohS. (2016). Designing computer-supported complex systems curricula for the Next Generation Science Standards in high school science classrooms. Systems, 4(4), 38. doi:10.3390/systems4040038
135.
*YoonS. A.Koehler-YomJ.AndersonE.LinJ.KlopferE. (2015). Using an adaptive expertise lens to understand the quality of teachers’ classroom implementation of computer-supported complex systems curricula in high school science. Research in Science & Technological Education, 33, 237–251. doi:10.1080/02635143.2015.1031099
