A framework for understanding the characteristics of complexity in biology

Dauer, Joseph T.; Dauer, Jenny M.

doi:10.1186/s40594-016-0047-y

Cited by 25 publications

(16 citation statements)

References 42 publications

(62 reference statements)

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“…System Thinking Skills (STS) is one of the skills developed in the 21st century (Bybee, 2010). STS is in line with the objectives of science education including in Biology education by providing students with an understanding of the complexity of the environment around it (Boersma, Arend, & Kees, 2011;Dauer & Dauer, 2016). STS is essential to provide to students and society so that they are accustomed to making decisions in solving complex life problems (Dawidowicz, 2012;Habron, Lissy, & Laurie, 2012).…”

Section: Introductionmentioning

confidence: 94%

“…To show the urgency of STS so that STS learning in various countries begins to be trained to students from the secondary to tertiary level including teachers to support sustainable development (Eilam, 2012;Habron, et al, 2012;Assaraf, Jeff, & Jaklin, 2013;Junge, Wilhelm, & Hofstetter, 2014;Raved & Yarden, 2014;Karaman, 2014;Zion & Sara, 2014;Cheng, Hung, & Liu, 2015). Biology learning in particular, has a complex study so that it is appropriate to give STS (Nursani, 2014;Dauer & Dauer, 2016;Agustina, Rustaman, Riandi, & Purwianingsih, 2017a). Notably, the provision of STS at the Scientiae Educatia: Jurnal Pendidikan Sains (2018), Vol 7 (2), 197-217 DOI: http://dx.doi.org/10.24235/sc.educatia.v7i2.3099 Published by Tadris IPA Biologi, IAIN Syekh Nurjati Cirebon, Indonesia.…”

Section: Introductionmentioning

confidence: 99%

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Traditional Biotechnology Content as a Media in Engaging Students with System Thinking Skills

Agustina

Rustaman

Riandi³

et al. 2019

sceducatia

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Traditional Biotechnology Content as a Media in Engaging Students with System Thinking Skills. A study about how to engage students with System Thinking Skills as part of 21 stcentury skills into Traditional Biotechnology content was conducted using Science-Technology-Religion-Engineering-Arts-Mathematics approach through one group pretestposttest design. The study involved 43 students from a 5 th semester in one University determined purposively. Data were collected through a system thinking test and interview format. After being received through instruments mentioned before, data were then processed using percentage criterion n-gain on a test and analyzed the result of student's interview. Research results show 60.47% of n-gain (moderate) with the best achievement is in the ability to explain the function of each component, while the weakest performance is in the ability to analyze the energy cycle. The result of the interview shows that the students can follow the lecture stages due to the repetitive and continuous assignment. The constraints found are in managing assignments within lecture time and less explored on religious aspects. Thus, Traditional Biotechnology content can facilitate students on system thinking skills.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Traditional Biotechnology Content as a Media in Engaging Students with System Thinking Skills

Agustina

Rustaman

Riandi³

et al. 2019

sceducatia

View full text Add to dashboard Cite

show abstract

“…Attempts to apply the theory and practical means of complexity science for ecology of infectious diseases have been very limited (Arch‐Tirado & Rosado‐Muñoz, ; Casadevall et al., ; Kosoy, ; Krakauer, ). To comprehend the novelty of this approach while avoiding a long review, consider a framework for understanding the characteristics of complexity in biology proposed by Dauer and Dauer () based on the compilation of a variety of definitions from other sources: (i) components are diverse and have diverse responses, (ii) functional relationships are nonlinear, continuous, and interactive, (iii) processes are simultaneous, dynamic, and emergent, (iv) manifestations are conditional and irregular, and (v) interpretation allows multiple representations.…”

Section: Disease Ecology and Science Of Complexitymentioning

confidence: 99%

Complexity and biosemiotics in evolutionary ecology of zoonotic infectious agents

Kosoy

2017

Evolutionary Applications

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More is not automatically better. Generation and accumulation of information reflecting the complexity of zoonotic diseases as ecological systems do not necessarily lead to improved interpretation of the obtained information and understanding of these complex systems. The traditional conceptual framework for analysis of diseases ecology is neither designed for, nor adaptable enough, to absorb the mass of diverse sources of relevant information. The multidirectional and multidimensional approaches to analyses form an inevitable part in defining a role of zoonotic pathogens and animal hosts considering the complexity of their inter‐relations. And the more data we have, the more involved the interpretation needs to be. The keyword for defining the roles of microbes as pathogens, animals as hosts, and environmental parameters as infection drivers is “functional importance.” Microbes can act as pathogens toward their host only if/when they recognize the animal organism as the target. The same is true when the host recognizes the microbe as a pathogen rather than harmless symbiont based on the context of its occurrence in that host. Here, we propose conceptual tools developed in the realm of the interdisciplinary sciences of complexity and biosemiotics for extending beyond the currently dominant mindset in ecology and evolution of infectious diseases. We also consider four distinct hierarchical levels of perception guiding how investigators can approach zoonotic agents, as a subject of their research, representing differences in emphasizing particular elements and their relations versus more unified systemic approaches.

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“…Importantly, constructing and interpreting conceptual models focuses student attention on the relationships within the model rather than the structures in isolation (Dauer et al 2013;Hmelo-Silver et al 2007;Jordan et al 2008). With student attention focused on relationships, instructors can challenge students to consider the system dynamics and complexity that emerges from variation in the structures, network-like relationships, and dynamics (Dauer and Dauer 2016;Williams and Clement 2015). When the relationships among structures can be described mathematically or computationally in a model, the dynamics of the system can be revealed through simulation (Abou-Jaoudé et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Changes in students’ mental models from computational modeling of gene regulatory networks

et al. 2019

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Background: Computational modeling is an increasingly common practice for disciplinary experts and therefore necessitates integration into science curricula. Computational models afford an opportunity for students to investigate the dynamics of biological systems, but there is significant gap in our knowledge of how these activities impact student knowledge of the structures, relationships, and dynamics of the system. We investigated how a computational modeling activity affected introductory biology students' mental models of a prokaryotic gene regulatory system (lac operon) by analyzing conceptual models created before and after the activity. Results: Students' pre-lesson conceptual models consisted of provided, system-general structures (e.g., activator, repressor) connected with predominantly incorrect relationships, representing an incomplete mental model of gene regulation. Students' post-lesson conceptual models included more context-specific structures (e.g., cAMP, lac repressor) and increased in total number of structures and relationships. Student conceptual models also included higher quality relationships among structures, indicating they learned about these context-specific structures through integration with their expanding mental model rather than in isolation. Conclusions: Student mental models meshed structures in a manner indicative of knowledge accretion while they were productively reconstructing their understanding of gene regulation. Conceptual models can inform instructors about how students are relating system structures and whether students are developing more sophisticated models of system-general and system-specific dynamics.

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A framework for understanding the characteristics of complexity in biology

Cited by 25 publications

References 42 publications

Traditional Biotechnology Content as a Media in Engaging Students with System Thinking Skills

Traditional Biotechnology Content as a Media in Engaging Students with System Thinking Skills

Complexity and biosemiotics in evolutionary ecology of zoonotic infectious agents

Changes in students’ mental models from computational modeling of gene regulatory networks

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