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Record W2167679573 · doi:10.1002/meet.14505001146

Knowledge artefacts: Lessons learned and Stories as a means to transfer knowledge amongst cohorts of high school students working on an inquiry‐based project

2013· article· en· W2167679573 on OpenAlexaff
Dhary Abuhimed, Jamshid Beheshti, Charles Cole, Mohammed J. AlGhamdi, Isabelle Lamoureux

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsExperiential learningExperiential knowledgeKnowledge transferPsychologyProcess (computing)Exploratory researchMathematics educationKnowledge managementPedagogyComputer scienceSociologySocial scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract This paper briefly outlines the framework of an ongoing exploratory study on knowledge transfer (KT) between student cohorts working on an inquiry‐based project using knowledge artefacts (KA) mediated by a website. Three successive cohorts of grade 8 students completed a history project which led them to build knowledge about an historical topic of interest. While working on their inquiry‐based project, the students encountered obstacles and gained experiential knowledge of the research process. The researcher recorded the lessons learned and stories told by the students' in the form of knowledge artefacts (KA). The goal of this study is to transfer the experiential knowledge from previous cohorts, coded in the form of KAs and mediated by a website, to new students. The research rationale is based on the assumption that the transfer of experiential knowledge may improve the research processes and results of new students working on a similar project.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.004
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.423
Teacher spread0.364 · 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 teacher head, not a consensus.

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

Citations6
Published2013
Admission routes1
Has abstractyes

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