Uporaba geodrevesa in sudokuja kot aktivnosti za razvoj računalniškega mišljenja v 4. razredu osnovne šole
Bibliographic record
Abstract
Razumevanje temeljnih računalniških znanj je v sodobni družbi, kjer informacijsko-komunikacijske tehnologije igrajo ključno vlogo na vseh področjih življenja, postalo nepogrešljivo. Zato je nujno, da učencem v osnovni šoli pomagamo razvijati osnovna znanja računalništva in informatike, ki jih bodo kasneje nadgrajevali do te mere, da bodo znali IKT pravilno in učinkovito uporabljati. Poleg tega jim moramo pomagati razvijati vztrajnost pri soočanju z neuspehom, sposobnost učinkovitega reševanja problemov in izzivov ter algoritmično in kritično razmišljanje. Namen prispevka je predstaviti aktivnosti računalništva brez računalnika, s katerimi učencem 4. razreda osnovne šole omogočamo učne priložnosti za pridobivanje in razvijanje temeljnih znanj računalništva in informatike. Cilj je spodbujati razvoj računalniškega mišljenja, kar smo poskušali doseči s sistematičnim pristopom ter uporabo konkretnih praktičnih iger in izzivov, kot sta geodrevo in sudoku. Ti primeri aktivnosti spodbujajo algoritmično razmišljanje učencev, sodelovalno reševanje nalog, razumevanje algoritmov ter urjenje v reševanju problemov. Z metodo opazovanja, glede na viden in zaznan napredek, ter refleksijo učencev, smo ugotovili, da so učenci učne priložnosti dobro izkoristili, saj so začeli uporabljati opisani način razmišljanja tudi v drugih učnih situacijah. V prispevku, ki je namenjen predvsem učiteljem v osnovni šoli, predstavljamo primere dobre prakse, ki predstavljajo ključni gradnik za nadaljnje učenje in pripravo na tehnološke izzive prihodnosti.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".