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Record W3009586710 · doi:10.1128/jmbe.v21i1.1919

Promoting Interdisciplinary Learning: A Cross-Course Assignment for Undergraduate Students in Advanced Biology and Drawing Courses

2020· article· en· W3009586710 on OpenAlexaffabout
Trisha Mahtani, Yael S. Brotman, Aarthi Ashok

Bibliographic record

VenueJournal of Microbiology and Biology Education · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCreativityMetacognitionMathematics educationDisciplineThe artsComputer scienceLiberal arts educationPsychologyHigher educationCognitionSociologyVisual arts

Abstract

fetched live from OpenAlex

Exposing students to interdisciplinary learning experiences has many benefits including a richer understanding of complex problems, promoting metacognition, and recognizing how to effectively use their skills. For biology students, immersion in creativity and design-thinking inherent to the arts can aid in their ability to imagine, innovate and communicate science. For art students who have been trained to develop unique visual vocabularies, an important learning experience would be to communicate didactic knowledge of an unfamiliar domain in a way that is visually compelling, imaginatively open-ended and informative. For both cohorts of students, academic training at the undergraduate level has remained somewhat sequestered in disciplinary silos. If we are to truly prepare students for careers in which they innovate, work collaboratively, solve problems and continue to learn, then we must provide authentic inquiry-based assignments that allow them to see the value of such proficiencies. This was the rationale behind designing this interdisciplinary assignment for biology and art students at the University of Toronto Scarborough. Here, we report on the design, implementation and insights from the pilot offering of this assignment. We hope that this provides a useful teaching tool for instructors interested in promoting interdisciplinary teaching and learning at the undergraduate level.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.362

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 studies0.0000.000
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 head0.061
GPT teacher head0.492
Teacher spread0.430 · 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 designObservational
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

Citations1
Published2020
Admission routes2
Has abstractyes

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