The InnovaWood Module Bank: Building an international e-learning platform for shared MSc courses in wood science and technology
Bibliographic record
Abstract
The InnovaWood Module Bank is a shared e-Learning platform for standalone science, technology and education modules in wood science. A group of members of InnovaWood have committed to jointly develop this platform. The institutes benefit in that they can widen the range of courses they offer and use their teaching capacities more efficiently. Students obtain the possibility to take online courses at another university without the need of costly exchange programmes. New e-Learning tools and teaching methods give them more choice and more flexibility to pursue their own individual preferences during their studies. To participate in the Module Bank, organisations must commit to providing at least one module of 3 ECTS at the MSc level. In return they obtain access to the whole series of modules that are offered collectively. The main benefits are that an institution obtains access to high quality lectures of experienced teachers in specific thematic fields and the opportunity to complement their core study programmes with additional online modules. Among others, these contain a module on ‘Wood degradation and wood protection’ by the University of Göttingen, which is relevant for IRG. The Module Bank contributes to new internationalisation experiences and a diversification of teaching contents and formats. Presentation at IRG Annual Meeting 2019 in Quebec, Canada. International Research Group on Wood Protection | irg-wp.com
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".