Collaborative Learning and Research Training: Towards a Doctoral Training Environment
Bibliographic record
Abstract
Doctoral training has not been studied in depth as a learning situation, and no learning environment has been designed to specifically support actors involved in the training of future researchers. The research literature on doctoral education indicates that the knowledge about doctoral training needs to be made explicit and formalized. We claim that several problems brought up in the literature on PhD Training could be reduced or solved by a doctoral training environment designed on the basis of a cognitive analysis. Doctoral training in the sciences consists essentially of research training through immersion in scientific communities and activities. Collaborative learning is built in authentic research situations, where doctoral students discover collaborative research. The model of a ‘Collaboratory' provides the foundations for the practice of collaborative research. Future researchers are expected to be competent in practicing ‘E-science' and knowledgeable about distributed research with remote access to shared instruments. The ability to practice ‘Coexperimentation' is part of the research skills. An authoring environment has been prototyped as well as an instantiation of a PhD program in the field of Cognitive Informatics One Use Case consists of two or three research distributed teams sharing observations and discussions, a research training situation involving immersion and collaborative learning. A series of tests and co-experimentations involving Inquiry Learning Environments as a topic of study in the field of Technology-Enhanced Learning was conducted. An international collaboration happened through Kaleidoscope and the coexperimentations were made possible by an optical network infrastructure providing high quality interactions in terms of sharing and telepresence.
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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 | 0.069 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".