Adding Behavior to Thinking about Structures &amp; Functions

Jordan, Rebecca; Gray, Steven G.; Demeter, Marylee; Liu, Lei; Hmelo‐Silver, Cindy E.

doi:10.2307/30163290

Cited by 4 publications

(4 citation statements)

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“…Model-based learning ( Gobert and Buckley, 2000 ) is an instructional approach that optimally addresses the cognitive challenges posed by systems. Models, as abstract or conceptual representations of systems, allow learners to focus on the relevant structures and mechanisms operating within a system ( Jordan et al. , 2008 ; Verhoeff et al.…”

Section: Introductionmentioning

confidence: 99%

“…According to SBF theory, all systems are composed of a multitude of individual components (the structures of the system) that interact with one another in a network of interrelationships (the behaviors of the system) to produce an output (the function of a system). SBF theory has been adapted to create conceptual modeling practices suitable for teaching and learning about biological systems at the middle school and college levels ( Hmelo-Silver and Pfeffer, 2004 ; Jordan et al. , 2008 ; Liu and Hmelo-Silver, 2009 ; Goel et al.…”

Section: Introductionmentioning

confidence: 99%

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Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

Reinagel

Speth

2016

LSE

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

Reinagel

Speth

2016

LSE

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“…Like the framework we propose in this article, SBF was first utilized in engineered systems (Goel & Stroulia, 1996) then adapted by Hmelo-Silver and colleagues in biological systems (Hmelo-Silver et al 2007;Hmelo-Silver & Pfeffer 2004;Jordan et al 2008). Another commonality between the SBF model and the framework we propose is that components in our framework mirrors "structures" in SBF representations and is a critical piece for teaching students about complex systems.…”

Section: Discussionmentioning

confidence: 99%

A framework for understanding the characteristics of complexity in biology

Dauer

2016

IJ STEM Ed

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Understanding the functioning of natural systems is not easy, although there is general agreement that understanding complex systems is an important goal for science education. Defining what makes a natural system complex will assist in identifying gaps in research on student reasoning about systems. The goal of this commentary is to propose a framework that explicitly defines the ways in which biological systems are complex and to discuss the potential relevance of these complexity dimensions to conducting research on student reasoning about complexity in biology classrooms. We use an engineering framework for dimensions of complexity and discuss how this framework may also be applied to biological systems, using gene expression as an example. We group dimensions of this framework into components, functional relationships among components, processes, manifestations, and interpretations within biological systems. We explain four steps that discipline-based education researchers can use to apply these dimensions to explore student reasoning about complex biological systems.

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“…Unfortunately, postsecondary students have few and irregular opportunities for modeling in biology despite evidence that modeling has been shown to be an effective way of developing deep disciplinary understanding (Mulder et al 2016;Seel 2017). 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).…”

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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Adding Behavior to Thinking about Structures & Functions

Cited by 4 publications

References 0 publications

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

A framework for understanding the characteristics of complexity in biology

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

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