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Redesigning a Physiology Course Using Core Concepts and Active Learning Methods

2018· article· en· W3174884129 on OpenAlexaff
Murray Jensen, Sarah Malmquist, Kerry Hull

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsBishop's University
Fundersnot available
KeywordsCurriculumActive learning (machine learning)Class (philosophy)Mathematics educationProcess (computing)PsychologyPhysiologyComputer sciencePedagogyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The findings of discipline based education research support the transition away from lecture, towards active learning, and away from details, towards core concepts. In response, a junior level physiology course at the U of Minnesota has been transformed in terms of where it is taught, how it is taught, and what is taught. In 2013 the course was moved from a large auditorium to an active learning classroom. This physical change, however, was not sufficient to induce transformation. Lecture remained the primary mode of instruction, reflecting the instructors' unfamiliarity with teaching methods required to facilitate active learning as well as the lack of active learning curriculum materials for physiology. In August 2017, the course was assigned to two professors who were familiar with Process Oriented Guided Inquiry Learning (POGIL), a well‐established teaching and learning method that utilizes guided inquiry and small cooperative groups. The instructors developed relevant curriculum materials; some were modified forms of existing POGIL curriculum, but most were new. The course now features regular group activities, with minimal class time devoted to traditional lecture. The instructors modified the course content as well as the classroom practices, bypassing the traditional body systems approach in favor of the Core Concepts of Physiology developed by Michael et al. They used core concepts such as homeostasis, flux, and communication to introduce topics such as blood pressure regulation, blood flow dynamics, neuronal physiology, and endocrine regulation. While students took the Homeostasis Concept Inventory on a pre‐ and post‐ basis, the detected learning gains were quite modest because of their strong scores in the pretest. In anonymous open‐ended surveys, students voiced concern about the emphasis on group work over traditional lecture and the lack of reliable study materials. Several expressed their preference for PowerPoint slides over information derived via group activities. Students also remarked that the summative assessments (primarily multiple choice exams) were not reflective of the course practices and content, since they seemed to address information that was not covered in class. While the instructors in the course will continue to use the modified format, they note the importance of better alignment between the core concepts‐based curricular materials and the assessments. The lack of alignment noted by the students largely reflects the difficulty of constructing conceptually‐based assessments. Thus, question databases testing core concepts as well as validated concept inventories are clearly needed to facilitate increased incorporation of core concepts into physiology courses. Support or Funding Information None This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.350
Teacher spread0.310 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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