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Record W73676122

Analyzing collaborative knowledge construction in secondary school biology

2010· article· en· W73676122 on OpenAlex
Vanessa Peters, James D. Slotta

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Conference of Learning Sciences · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumKnowledge baseMathematics educationResource (disambiguation)Test (biology)Knowledge managementCollaborative learningComputer sciencePedagogyPsychologyWorld Wide WebBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

This research investigates student collaboration in a high school biology curriculum that was based on the Knowledge Community and Inquiry (KCI) model. Using co-design, the researchers collaborated with three high school science teachers to design a curriculum where 112 grade-ten biology students collaboratively developed a community wiki about Canadian ecozones and biodiversity issues. Students then used the wiki as the primary resource for a subsequent inquiry activity. This paper analyzes students' contributions to the knowledge base, test performance, and student satisfaction to evaluate the efficacy of the KCI model. A new method of analysis for collaborative wiki artifacts was developed to measure student interaction in the wiki. We found that students who were higher contributors to the knowledge base performed better on a post-test than students who were measured as low contributors. Our findings suggest that the KCI model is a promising mechanism for supporting both collaborative and individual learning.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.390
Teacher spread0.360 · 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