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Subways and role‐plays: Using analogies to understand matter and energy flow

2017· article· en· W3112646683 on OpenAlexaff
Kerry Hull, P. Marx, Murray Jensen

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsBishop's University
Fundersnot available
KeywordsActive transportMembraneFacilitated diffusionChemistryTransporterFlow (mathematics)Secondary flowNarrative reviewMathematics educationBiophysicsPsychologyComputer scienceBiochemistryMembrane transportBiologyMechanicsPhysicsGene

Abstract

fetched live from OpenAlex

The multidimensional and dynamic nature of flow, be it the flow of solutes across membranes or of reagents and energy through chemical pathways, can render these topics difficult to transmit using traditional approaches such as lecture or Powerpoint slides. This study explored the potential of different types of analogies to promote understanding of two examples of flow: secondary active transport of glucose and the metabolic reactions involved in nutrient processing. Secondary active transport was modeled by asking students in an intermediate physiology course to play the roles of solutes and transporters. The classroom was divided into the gut lumen, the intestinal cell, and the interstitial fluid (ISF), and students without roles (the “advisory group”) determined the initial placement of the transporters and solutes based on a textbook figure. The advisers also provided guidance as to the force promoting the movement of each solute at the luminal and basolateral membranes; self‐propelled (facilitated diffusion), moved by a self‐propelled student representing a different solute (secondary active transport), or “pulled across” by the transporter itself (primary active transport). The simulation was then repeated to give more students the opportunity to participate and all students the opportunity to observe. The percentage of students able to identify the nature of the force (ATP or a gradient) driving the flow of each solute increased from 44% before the simulation to 77% after the simulation, and the performance on a relevant exam question (86%) was higher than that of the exam average (76%). Analysis of narrative answers did not reveal any overt misconceptions introduced by the analogy. Flow in metabolic pathways was modeled using a simplified version of the London Underground Film Map, which names subway stops based on nearby filming locations. The map served as the basis for the introductory section of a guided inquiry learning problem set investigating insulin action that was used both in an undergraduate physiology course and in a high school biology course. Once students were comfortable tracking routes through the interlocking and branching subway system, they used the same process to analyze the flow of carbohydrates, proteins, and fats through biochemical pathways in hepatocytes. The final stage asked students to predict which metabolic pathways would be promoted by insulin, both in hepatocytes and in other insulin target sites. While the first two modules effectively engaged both student populations, the final stage proved too difficult for high school students. The upper‐year undergraduates, however, were able to successfully complete the module and performed well on the relevant exam question (75%, compared to a 68% exam average). In conclusion, both analogies appeared to effectively engage students and, depending on the student population, promote conceptual understanding. Because the concept of flow within metabolic pathways is central to a robust understating of physiology, a modified version of the final section will be developed to better accommodate the high school student audience. Support or Funding Information Senate Research Committee, Bishop's University

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0040.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.210
GPT teacher head0.381
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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