Teaching and Learning Ecological Modeling over the Web: a Collaborative Approach
Bibliographic record
Abstract
A framework for web-based collaborative teaching has been created.This framework is implemented as an ecological modeling course (http://iee.umces.edu/AV/Simmod.html),but should be flexible enough to apply to other disciplines.I have developed a series of tools to facilitate interactive communication between students and instructors, and among students taking the course.The course content consists of reading materials that describe the theory of systems analysis and modeling, guidelines on how models can be built, and numerous examples and illustrations.The interactive part includes exercises that can be discussed with and evaluated by the instructor, and provides a means to mimic class discussions.To what extent this approach can replace conventional in-class tutoring has yet to be tested, but the preliminary applications show great promise.I offer this course format as a framework and a prototype for collaborative "open-source" approaches to education, in which the web provides the means to communicate knowledge and skills asynchronously between geographically dispersed educators and students.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".