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Record W2549614618 · doi:10.3991/ijep.v6i4.5965

Pioneering STEM Education for Pre-Service Teachers

2016· article· en· W2549614618 on OpenAlexaffabout
Armando Paulino Preciado Babb, Miwa Aoki Takeuchi, Gabriela Alnoso Yáñez, Krista Francis, Dianne Gereluk, Sharon Friesen

Bibliographic record

VenueInternational Journal of Engineering Pedagogy (iJEP) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBachelorContext (archaeology)NarrativeTeacher educationPedagogyEngineering educationMathematics educationMedical educationEngineering ethicsPolitical scienceSociologyEngineeringPsychologyEngineering managementMedicine

Abstract

fetched live from OpenAlex

While there have been numerous initiatives to promote and recruit students into postsecondary studies in science, technology, engineering and mathematics (STEM) around the world, traditional programs of studies for both K to 12 school and teacher education still lack an integrative approach to these disciplines. Addressing this concern, the Werklund School of Education of the University of Calgary started to offer a course in STEM education for the undergraduate Bachelor of Education program. The purpose of this article is to document the first iterations of this course. We draw from narratives of four instructors, including the coordinator of the course, and administrators who were actively involved in creation and approval of the course. We describe the course and its connection to the philosophy of the program, examine the context in which this course was conceived—including both national and provincial policy—and address some challenges and possibilities experienced by administrators, instructors and students during the creation and implementation of the course.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.413
Teacher spread0.369 · 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 designNot applicable
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

Citations10
Published2016
Admission routes2
Has abstractyes

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