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Record W3208680762

INTEGRATING VISUAL ART AND LANGUAGE, SOCIAL SCIENCE AND SCIENCE: UNIT PLANS FOR A TEACHING ARTIST

2019· article· en· W3208680762 on OpenAlexaboutno aff
Varshiini Ravishankar

Bibliographic record

VenueQSpace (Queen's University Library) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsUnit (ring theory)Visual artsVisual languagePsychologyMathematics educationComputer scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

The broad purpose of the project was to explore what an arts partnership could look like. An arts partnership generally involves an artist—or “teaching artist”—from the community spending time in a classroom, working with the teacher and students on art projects. I begin the project with a review of literature that describes what an arts partnership is, the benefits they provide, different ways they are structured, some of the issues or challenges with arts partnerships, and who supports arts partnerships in Ontario. The second part of the project presents curriculum designed for a teaching artist to use in schools with students in grades six to eight. I begin the section by describing the guiding principles I used to design the teaching artist programs. I then provide detailed lesson plans. Program 1 (8 weeks) integrates visual art with language. Program 2 (4 weeks) integrates visual art with social studies (geography). Program 3 (4 weeks) integrates visual art with science. Within the programs I intentionally created resources for the teaching artist to use that emphasize visual and oral communication instead of relying only on written communication. For example, I created a number Power Point presentations to visually enhance communication of information and ideas and to show students examples of art work. I also created videos to demonstrate art-making techniques. Further, I created exemplars of all the assignments to show students the kinds of things they are expected to produce.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.009
GPT teacher head0.230
Teacher spread0.220 · 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
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
Published2019
Admission routes1
Has abstractyes

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