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Record W4409355499 · doi:10.1187/cbe.22-05-0089

Beyond Technical Skills: The Value of Perspective-Taking in STEM Education

2025· article· en· W4409355499 on OpenAlexaff
Jason Xi, Sarah McLean

Bibliographic record

VenueCBE—Life Sciences Education · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)Inclusion (mineral)CurriculumEngineering ethicsValue (mathematics)Stem cellPsychologyPedagogyMedical educationMathematics educationMedicineEngineeringComputer scienceBiologySocial psychologyCell biology

Abstract

fetched live from OpenAlex

With the heavy focus institutions place on the content mastery of undergraduate STEM (science, technology, engineering, math) students, qualitative skillsets equally important to the future success of STEM students, namely perspective-taking (PT), are being left at the wayside. In response, this essay will highlight reasons PT should be included in undergraduate STEM curricula. To further strengthen the rationale for the inclusion of PT in the curriculum, we will demonstrate that evidence suggests that PT skills can be taught and that there are sociocultural elements that educators should address to effectively implement PT into the curriculum. Finally, we suggest activities that educators can use to foster a learning environment that encourages strong PT skill development among undergraduate STEM learners.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.411
Teacher spread0.381 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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