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Record W2528590563 · doi:10.7771/1541-5015.1603

Facilitating Problem Framing in Project-Based Learning

2016· article· en· W2528590563 on OpenAlexaff
Vanessa Svihla, Richard Reeve

Bibliographic record

VenueInterdisciplinary Journal of Problem-based Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsQueen's University
FundersNational Academy of EducationSpencer Foundation
KeywordsFraming (construction)Frame problemMathematics educationCharterComputer scienceIterative designPedagogyPsychologyArtificial intelligenceEngineeringPolitical scienceCompatibility (geochemistry)

Abstract

fetched live from OpenAlex

While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated how teachers supported students in taking ownership over the framing of problems in a charter school that serves students who have been underserved by traditional schooling. Data include audio/video records, field notes, interviews, and student work from a nineweek project. Interaction analysis was used to examine ownership and learning over time. Analysis suggests that providing a relevant yet revisable design problem, giving instruction about design process as iterative, and problematizing a model of design process supported students in taking ownership over the framing of the problem; students were motivated to pose questions and gathered information purposefully, thereby learning in the process.

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)0.026
metaresearch head score (Gemma)0.080
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.080
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0050.006
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.340
Teacher spread0.318 · 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 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

Citations52
Published2016
Admission routes1
Has abstractyes

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