MétaCan
Menu
Back to cohort
Record W2561970431

Evaluation by Grade 5 and 6 Students of the Promisingness of Ideas in Knowledge-Building Discourse.

2011· article· en· W2561970431 on OpenAlex

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

VenueComputer Supported Collaborative Learning · 2011
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSelection (genetic algorithm)Computer scienceMathematics educationDiscourse analysisBig dataSociologyPsychologyPedagogyLinguisticsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Knowledge creation requires identifying and pursuing promising ideas—ideas that in their nascent form may not seem like much but that with development could grow into something big. The goal of our research is to develop a tool to explore the concept of promisingness and “big ideas,” especially elementary school students’ ability to make “promisingness judgments” regarding ideas in peer discourse. Toward this end we developed a “Big Ideas” tool to facilitate students’ selection of the ideas they thought were promising in their online discourse. A study conducted in two Grade 5/6 classes examined the nature of “big ideas” selected from the online discourse of younger Grade 4 students. A preliminary analysis indicated that students tended to identify as promising important facts and questions in the Grade 4 discourse. This study will inform future work in designing tools, language, and techniques to facilitate the concept of promisingness.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.417
Teacher spread0.363 · 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