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Record W2724530065

Interdisciplinary Education in the Elementary Curriculum: Exploring Teacher Perceptions and Practices

2017· other· en· W2724530065 on OpenAlexaffabout
А. Р. Герке

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typeother
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumPedagogyMathematics educationPerceptionSociologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

This research study focused on interdisciplinary education practices in elementary schools and classrooms. The main research question was: how does a sample of Ontario elementary educators design and implement interdisciplinary lessons in the classroom, and what outcomes do they observe from students? This question was investigated using semi-structured interviews with two elementary educators working in schools in Ontario. Findings of this study suggest that a variety of subjects can be combined in an interdisciplinary curriculum, but certain subjects should be taught in isolation. Furthermore, educators identified external frameworks and published materials that were useful resources for educators planning interdisciplinary curriculum. Another finding was that participants identified clear starting points for planning and assessing an interdisciplinary curriculum. Recommendations arising from this research are that teacher education should explicitly address interdisciplinary education. A whole school approach is also more supportive of teachers in their interdisciplinary practices and consequently, administrators may want to consider adopting school-wide interdisciplinary practices. Finally, teachers looking to implement interdisciplinary practices in their classroom should seek out opportunities to collaborate with other educators in order to plan a cohesive interdisciplinary curriculum.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.628
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.000

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 head0.095
GPT teacher head0.437
Teacher spread0.342 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes2
Has abstractyes

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