Decision Making in Interactive Learning Environments Towards an Integrated Model
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Bibliographic record
Abstract
Abstract Experimental research on decision making and learning in dynamic tasks with the use of computer-simulation-based interactive learning environments as DSS is analyzed. A conceptual model encompassing key elements of decision making: decision, decision maker, and decision making process is constructed. The model draws on two sources: (1) the dynamic decision making literature and (2) a relevant learning theory- cognitive apprenticeship. Departing from traditional dynamic decision making research focus on how poorly subjects perform in dynamic tasks, our model, through acquisition-of-expertise hypothesis, attempts to increase our understanding of the way in which expertise on dynamic decision making could be acquired through training with computer-simulation-based interactive learning environments. Cet article s'intéresse aux recherches menées sur la prise de décision et l'apprentissage assistés par ordinateur dans les situations dynamiques. Un modèle conceptuel rassemblant les éléments-clés de la prise de décision est proposé. Ce modèle est basé sur deux sources : 1) l'état de l'art dans le domaine de la décision dynamique et 2) les théories de l'apprentissage qui sont pertinentes dans ce cas. S'éloignant des etudes traditionnelles dans le domaine de la décision dynamique, qui sont basées sur l'idée que les acteurs ne savant pas bien se débrouiller dans ces situations, notre modèle cherche à prouver comment une expertise spécifique à de telles situations peut être acquise à l'aide d'une formation spécifique assistée par des outils informatiques interactifs utilisant la simulation. Keywords: Computer SimulationDynamic Decision MakingInteractive Learning EnvironmentsDynamic TaskStructural KnowledgeHeuristics KnowledgeMots clés: simulationdécision dynamiqueoutils de formation interactifsconnaissanceheuristiques
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it