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Record W2105003437

An Interdisciplinary Approach to Curriculum in the Middle Years: Summative Evaluation of the IMYM Climate Change Pilot Study

2005· article· en· W2105003437 on OpenAlexaffabout
Cheryl Prokopanko, Karen Fiege

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Tools and Methods
Canadian institutionsBow Valley CollegeGovernment of Manitoba
Fundersnot available
KeywordsRubricSummative assessmentCurriculumInformation and Communications TechnologyMedical educationTechnology integrationPedagogyPsychologyMathematics educationEducational technologyFormative assessmentComputer scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

This paper reports on the Interdisciplinary Middle Years Multimedia (IMYM) Climate Change pilot study carried out by Manitoba Education, Citizenship and Youth (MECY), Province of Manitoba, over the 2003-2004 school year. Fifteen grade 5 pilot teachers were selected from across the province. Participants were provided with various resources, face-to-face professional learning sessions, and an online learning community. This pilot study was based on the IMYM model which blends an interdisciplinary constructivist approach with the integration of Information and Communication Technology (ICT) in an effort to achieve curricular outcomes. Results were derived from the qualitative data of the IMYM pilot teachers’ online reflection journals, threaded discussions and feedback as well as quantitative data from exit slips and rubrics. The pilot study was deemed a success and its findings are relevant to those interested in teacher training, and the integration of ICT into curriculum, teaching, learning, and assessment.

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)0.018
metaresearch head score (Gemma)0.022
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 score0.018
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.351
GPT teacher head0.508
Teacher spread0.157 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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