Bibliographic record
Abstract
Die Tagung Intelligent Tutoring Systems (ITS) hat sich seit ihrer Initiierung durch Claude Frasson in Montreal (1988) fest etabliert. Sie offeriert einen internationalen Kontrapunkt für eine sonst stark US-dominierte \nSzene. Die 6 deutschen Beiträge waren „Group-oriented Modelling Tools with Heterogenous Semantics“ (Pinkwart, Hoppe, Bollen & Eva Fuhltrott), „Adaptive Tutor Processes and Mental Plans“ (Alke Martens & Adelinde Uhrmacher), „Architectural Patterns in Pedagogical Agents“ (Devedzic & Harrer), „Cognitive Modelling Approach to Diagnose Over- \nSimplification in Simulation-Based Training“ (Lüdtke, Möbus, Thole), „Using Computer Algebra Systems as Cognitive Tools“ (Büdenbender, Frischauf, Goguadze, Libbrecht, Erica Melis & Ullrich), „Towards a Specification of Distributed and Intelligent Web Based Learning Environments“ (Möbus, Albers, Hartmann, \nThole & Zurborg). \nDer Tagungsband umfasst 1016 Seiten. Die Papiere waren in die Kategorien „Agents“ (Social; Collaboration; Technologies), Architectures (Rules, Patterns, Hypermedia; Domain Oriented; Knowledge Acquisition, Reuse), Web (Dynamics, Ontologies, Training), Evaluation (Cognition; Technologies; Dynamics; Development, Agents), Authoring (Complex Domains, Instructional Design), Learning (Models; Architectures; Web; Meta-cognition), Simulation, Dialogue \n(Generic; Specific; Social), Narrative, Motivation and Emotions und Poster Papers. Nach Einschätzung der Herausgeber war das Hauptthema der Tagung „AI and Human Learning“ und die „emerging shifts“ Web and Agents, Eval \nuations, Dialogues.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".