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Record W2143292250

Collaborative teaching of an integrated methods course

2011· article· en· W2143292250 on OpenAlexaff
George Zhou, Jinyoung Kim, Judit Kerekes

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCourse (navigation)Computer scienceMathematics educationMedical educationPsychologyEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

With an increasing diversity in American schools, teachers need to be able to collaborate in teaching. University courses are widely considered as a stage to demonstrate or model the ways of collaboration. To respond to this call, three authors team taught an integrated methods course at an urban public university in the city of New York. Following a qualitative research design, this study explored both instructors‟ and pre-service teachers‟ experienceswith this course. Study findings indicate that collaborative teaching of an integrated methods course is feasible and beneficial to both instructors and pre-service teachers. For instructors,this collaborative teaching was a reciprocal learning process where they were engaged in thinking about teaching in a broader and innovative way. For pre-service teachers, this collaborative course not only helped them understand how three different subjects could berelated to each other, but also provided opportunities for them to actually see how collaboration could take place in teaching. Their understanding of collaborative teaching was enhanced after 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 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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.300
GPT teacher head0.634
Teacher spread0.334 · 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 designQualitative
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

Citations12
Published2011
Admission routes1
Has abstractyes

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