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Record W2243220016

A Strategic Partnership to Understand the Ecosystem, Adaptability and Transfer of Digital Skills - a Focus on the Educational System

2015· article· en· W2243220016 on OpenAlexaff
Manon Mireille LeBlanc, Michel Léger, Jeanne Godin, Viktor Freiman, Xavier Robichaud, François Larose, Roman Chukalovskyy, Yves Bourgeois

Bibliographic record

VenueSociety for Information Technology & Teacher Education International Conference · 2015
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsUniversity of New BrunswickUniversité de SherbrookeUniversité de Moncton
Fundersnot available
KeywordsAdaptabilityCompetence (human resources)Delphi methodKnowledge managementDelphiGeneral partnership21st century skillsKey (lock)PsychologyPublic relationsPedagogyEngineering ethicsComputer scienceBusinessEngineeringPolitical scienceManagementSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

This paper is a report on the findings of a study on the development of digital skills. Discourse analysis techniques were used to examine the resulting transcript of interviews with experts in technopedagogy for evidence of digital skill development from school and postsecondary contexts. Using the DELPHI method, six experts were consulted about key digital competence. Qualities such as resilience, adaptability and open-mindedness were identified as key. Findings also indicate that digital skill can be defined by one's ability to use technologies and adapt positively to challenges during use. From an educational perspective, our results show that the digital skills needed to succeed in a technological world are not necessarily the ones developed in schools and colleges. For instance, experts agree that educational institutions looking to foster digital skills should move beyond teaching mainly technical ability, focusing instead on developing more analytical or critical ability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.046
GPT teacher head0.296
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2015
Admission routes1
Has abstractyes

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