Bibliographic record
Abstract
Hvordan udnytter vi potentialerne i digitale teknologier i undervisningen? Hvor og hvordan kan digitale teknologier bidrage til at tænke og tilrettelægge undervisning på nye måder, så de udvider elevers og studerendes muligheder for at undersøge, løse, skabe, udtrykke sig osv.? Gennem en model for fire typer af digitale læringsrum giver forfatterne bud på, hvordan vi med digitale teknologier kan indrette læringsrum, der styrker den enkelte elevs handlekraft, gruppers kollaborative vidensopbygning, vidensdeling i klassen og interaktion med omverdenen. De fire digitale læringsrum er: Det individuelle rum - Arbejdsgruppe - Interessefællesskab - Åbne forbindelser. I denne 2. udgave er bogen opdateret med nye eksempler på brug af digitale værktøjer og refleksioner over, hvor generativ AI hører til inden for læringsrummene. Den primære målgruppe for bogen er studerende, der er ved at uddanne sig til at undervise andre, fx lærerstuderende på professionshøjskolerne, studerende på diplomuddannelser og universitetsstuderende. Derudover henvender den sig også til lærere, undervisere, pædagogiske konsulenter, uddannelsesledere og it-vejledere på tværs af folkeskole, gymnasiale uddannelser og videregående uddannelser. (Forlagsbeskrivelse)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.314 | 0.186 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".