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Record W2736655297

Teaching and Learning in an Integrated Curriculum Setting: A Case Study of Classroom Practices

2012· book· en· W2736655297 on OpenAlexaboutno aff
Sheryl MacMath

Bibliographic record

VenueTSpace · 2012
Typebook
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMathematics educationComputer sciencePedagogyPsychology
DOInot available

Abstract

fetched live from OpenAlex

Curriculum integration, while a commonly used educational term, remains a challenging concept to define and examine both in research and in classroom practice. Numerous types and definitions of curriculum integration exist in educational research, while, in comparison, teachers tend to focus on curriculum integration simply as a mixing of subject areas. To better understand curriculum integration in practice, this thesis details a case study that examines both teacher and student perspectives regarding a grade nine integrated unit on energy. Set in a public secondary school in Ontario, Canada, I comprehensively describe and analyze teacher understandings of, and challenges with, the implementation of an integrated unit, while also examining student perspectives and academic learning. My participants consisted of two high school teachers, a geography teacher and a science teacher, and their twenty-three students. Using data gathered from interviews before, during, and after the implementation of a 16-lesson unit, as well as observations throughout, I completed a case description and thematic analysis. My results illustrate the importance of examining why teachers choose to implement an integrated unit and the planning and scheduling challenges that exist. In addition, while the students in this study were academically successful, clarification is needed regarding whether student success can be linked to the integration of these two subjects or the types of activities these two teachers utilized.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.146
GPT teacher head0.470
Teacher spread0.323 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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