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Record W2134498181 · doi:10.21432/t2c01s

Early Development of Graphical Literacy through Knowledge Building

2010· article· en· W2134498181 on OpenAlexfundvenueno aff
Yongcheng Gan, Marlene Scardamalia, Huang‐Yao Hong, Jianwei Zhang

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2010
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsGraphicsVisual literacyMathematics educationScientific literacyComputer scienceReciprocalLiteracyComputer graphicsKnowledge levelQuality (philosophy)MultimediaScience educationPsychologyPedagogyComputer graphics (images)

Abstract

fetched live from OpenAlex

This study examined growth in graphical literacy for students contributing to an online, multimedia, communal environment as they advanced their understanding of biology, history and optics. Their science and history studies started early in Grade 3 and continued to the end of Grade 4; students did not receive instruction in graphics production, nor were they required to produce graphics. Results show that students spontaneously produced graphics that advanced along seven dimensions, including effective representation of complex ideas, use of source information and captions, and aesthetic quality. On average, the scores for the seven dimensions were higher for Grade 4 students with two years of experience with Knowledge Building pedagogy and technology (Knowledge Forum®) than for Grade 6 students with one year of experience. The overall pattern of results suggests reciprocal enhancement of graphical, textual, digital, and scientific literacy, with students exceeding expectations by available norms, and performance enhanced through extended Knowledge Building experience.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.019
GPT teacher head0.360
Teacher spread0.340 · 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 designObservational
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

Citations18
Published2010
Admission routes2
Has abstractyes

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