MODELI UČENJA ODRASLIH I PROFESIONALNI RAZVOJ
Bibliographic record
Abstract
U radu su detaljno prikazani neki modeli učenja odraslih relevantni za profesionalni razvoj. Raspravljene su razvojne perspektive u odrasloj dobi polazeći od teorije životnog raspona. U okviru tog pristupa smatra se da razvoj određen biološkim, psihološkim i sociokulturnim činiteljima u cjeloživotnoj perspektivi istodobno uključuje i rast i opadanje.Opisana su neka obilježja kognitivnog funkcioniranja u odrasloj dobi – intelektualno funkcioniranje, pod Catellovim modelom fluidne i kristalizirane inteligencije, kvalitativne razlike u kognitivnom funkcioniranju u formalnom i postformalnom razdoblju, promjene u ekspertnosti i mudrosti te promjene u pamćenju vezane uz odraslu dob. Promjene u kognitivnom funkcioniranju u zreloj dobi rezultat su kontinuiranog učenja. U nastavku rada opisani su kognitivistički modeli učenja odraslih: procesni model obrade informacija Atkinsona i Shiffrina (1986.), model dubine obrade informacija Craika i Lockharta (1972.) i model socijalnog učenja Alberta Bandure (1978.) te je raspravljena njihova primjena u poučavanju odraslih. Osim spomenutih modela, prikazani su i modeli iskustvenog učenja: Kolbov model iskustvenog učenja (1984.), model s dvostrukom petljom Schöna i Argyrisa (1996.) te model refleksivnog učenja s višestrukim petljama (Cowan, 1993.). Opisana je i primjena ovih modela u poučavanju odraslih.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".