Bibliographic record
Abstract
St@tNet, the French interactive internet course on introductory statistics (http://www.agro- montpellier.fr/cnam-lr/statnet/) is a complete, interactive, web-based, course on introductory statistics with six modules: data description, probability, random variables, sampling and estimation, tests, and elements of linear regression. St@tNet has been realised by a consortium of several French-speaking universities, with the support of the French Ministry of Education and of the Agence Universitaire de la Francophonie[SEPARATOR] its content is freely accessible. The course corresponds to a classical credit of 50 hours of teaching. St@tNet is composed of three main parts: · A course where each chapter is itself divided into four parts a presentation, a development, a summary, interactive exercises, a glossary, ... · Downloadable files : course notes, exercises, datasets · A set of tools including links, statistical tables, a glossary. Each chapter is accompanied by a short video introduction (about 2 minutes) presented by the teacher who was in charge of this part. At the CNAM, the course is offered since 1999 as an on-line distance teaching for continuous education. Registered students receive a CD (to avoid permanent Internet connection) with the full contents and an Internet password to have access to specific services : tutorship with a discussion forum in a virtual class-room, e-mail with a teacher for 25 students, access to previous exams subjects, etc. St@tNet is an independent platform and may be integrated in any learning system. Any French speaking university could thus include St@tNet as a distance teaching offer. In this communication, we will present the results of our experience in using St@tNet for distance teaching and self-learning. They have induced deep modifications in the production of new material for the course. Thanks to a joint cooperation with the first author, we are enlarging the scope of St@tNet with new chapters devoted to statistical modelling, including a complete course on multivariate linear regression theory, and also some chapters related to generalized modelling. Since these new chapters are intended for more mature students, already familiar with elementary probability and statistics, the need for a somewhat entertaining and amusing HTML interface is vanishing, and the new course documents will be produced in the Latex-PDF environment that provides all the necessary internal and hyperlink possibilities. And the pedagogy is even more active, and very much project oriented. Animations, (some will be shown in the presentation) now produced with Flash-MX, will enhance much of the teaching material.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.064 |
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".