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Record W2622728419

Why Do Teachers Get To Learn The Most

2004· article· en· W2622728419 on OpenAlex
Terry Anderson, Norine Wark

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAUSpace (Athabasca University) · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMathematics educationComputer sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

A common report from anecdotal writing over many generations of educators is that it is the teacher
\nwho usually learns the most during the process of gathering content materials, designing, teaching
\nand evaluating student performance. In this project we address this issue by developing an
\ninnovative instructional design in which collaborative groups of students working at distance create, share
\nand assess learning content (in the form of learning objects) with their peers through online learning
\nportals. The results of this process are assessed via surveys, discussions, reflective essays and
\npeer evaluations. We conclude that instructional models based upon student construction of content
\nand orchestration of learning activities can reduce instructor workload, provide opportunity for students
\nto acquire new skills while increasing their subject content knowledge, and create a lasting legacy of reusable
\nlearning objects.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.316
Teacher spread0.280 · 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