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Record W4405530942 · doi:10.7146/lt.v10i15.146879

Fra Fysisk til Virtuel Undervisning

2024· article· da· W4405530942 on OpenAlexaff
Kaspar Kjemtrup

Bibliographic record

VenueLearning Tech · 2024
Typearticle
Languageda
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical scienceArt

Abstract

fetched live from OpenAlex

Mens flere og flere online skoler varetager undervisningen af grundskoleelever, komplicerer onlineundervisning deltagelse og socialt samspil, som er centralt for at læring kan finde sted. Dette studie udforsker, hvilke didaktiske metoder der kan styrke deltagelse og interaktion i online undervisning samt hvilken rolle digitale interaktionsværktøjer spiller. Data blev indsamlet netnografisk på to 100% online skoler og bestod af 6 lærerinterviews, lederinterview og 19 undervisningsobservationer, som dækkede en bred vifte af fag fra 2.-9. klassetrin. Resultaterne indikerer adskillige betydningsfulde sammenhænge for sociale interaktioner i anvendelsen af kameraet og digitale interaktionsværktøjer i praksisfællesskaber. Onlineundervisning, hvor det centrale var at lave noget sammen gennem inddragelse af fysiske genstande i rummet uden for skærmen, fremviste større tendens til sociale interaktioner, mens en lavere grad af samspil blev observeret i undervisning, hvor kameraet var slukket. Disse fund bør inkorporeres og nøje overvejes i onlineundervisning for at understøtte elev- og lærerengagement.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1090.026

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 head0.010
GPT teacher head0.275
Teacher spread0.265 · 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 designNot applicable
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".

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Citations0
Published2024
Admission routes1
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