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Record W7066434482

Guided inquiry-based lab in cancer biology designed to support student understanding of biomedical research.

2025· article· en· W7066434482 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2025
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsnot available
Fundersnot available
KeywordsWorkflowActive learning (machine learning)Value (mathematics)Problem-based learningInterpretation (philosophy)Graduate studentsClass (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Undergraduate programs in biomedical science generally balance the teaching of content with practical skill competencies in the laboratory. Traditionally this is approached through method-centered lab sessions that may not be interconnected and do not reflect how lab science is genuinely practiced. Refereed to as “cook book” labs these are very common in large cohort classes and are of limited educational value since they foster passive learning. In contrast inquiry-based laboratories are process-centered and are designed to promote engagement, self-directed learning and critical thinking. There are many reasons why inquiry-based labs are not adopted more widely, including funding, limited resources, skilled personnel, instructor training, among many other barriers. Here we show a guided inquiry-based lab-course that meets our program objectives and supports cross-disciplinary learning. The creation of this guided inquiry-based biomedical laboratory course is intended to enhance deeper learning practises among undergraduate Translational and Molecular Medicine (TMM) students at the University of Ottawa. The course focuses on genetic changes associated with tumorigenesis and tumor suppressor reactivation, immersing students in authentic research experiences. It is designed to support students to pose a hypothesis and then implement an experimental workflow to address this question over multiple lab sessions. This includes experimental design and data interpretation workshops every week to help orient students in a research environment. Although the lab is not completely open ended, individual students need to make multiple choices to make their project unique. We consider this lab to be a good balance between available resources and optimal learning outcomes. Student expereinces of the from a survey will be shared. This work received an exemption from REB review from the University of Ottawa Office of Research Ethics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.006

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 head0.439
GPT teacher head0.486
Teacher spread0.046 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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