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Record W4380324628 · doi:10.30557/qw000064

Student reflections on the integration of Knowledge Forum as ‘equipment’ for knowledge building practice

2023· article· en· W4380324628 on OpenAlexafffund
Dina Soliman, Andrew Whitworth, Steven Priddis

Bibliographic record

VenueQwerty · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoUniversity of Manchester
KeywordsDiversity (politics)PerceptionCommunity of practiceKnowledge buildingStudent engagementPedagogyDigital literacyPsychologyMathematics educationSociologyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

This study investigates the digital, media and information literacy (DMIL) practices that student developed through engagement with Knowledge Forum (KF), a platform designed to facilitate knowledge building dialogues. Participants included 73 students enrolled in a DMIL course in a University in the UK. The dataset comprised reflective essays submitted by students, analyzed thematically to examine perceptions and patterns of engagement with KF. Findings show that students appreciated the mesh structure of KF views, andhow it facilitated idea diversity. Findings also show that students demonstrated higher levels of community discourse around design ideas, particularly in comparison with previous course iterations. Evidence of how students came to understand knowledge building principles through the way they integrated KF into their practice is discussed.

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)0.019
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0100.006
Open science0.0020.008
Research integrity0.0030.006
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 head0.209
GPT teacher head0.557
Teacher spread0.348 · 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 designQualitative
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

Citations0
Published2023
Admission routes2
Has abstractyes

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