E-Tools and Teaching ESL Through Distance Education, paper presentation at the Teaching English as a Second Language (TESL) Canada 2008 Conference, May 29-31, 2008, Moncton, NB
Bibliographic record
Abstract
In the end, only three of us presented at the three-hour long symposium--me, Debra Hoven, and Corinne Bosse. I covered the material that Tunde Tuzes had been going to present as well as my own section. I’ve attached copies of the ppt presentations I used for my parts of the presentation (E-Tools update and Critical Discussion Groups--I have to thank David Brundage for providing me with this last presentation). This particular session was the most highly attended of the symposia offered at the conference, and we received very positive feedback. The audience responded well to the variety of e-tools that we presented and asked a large number of questions about the techniques discussed. I started out be giving some background information about AU, so that we wouldn’t have to deal with those questions as we went through our various presentations. The more technical questions were answered by Debra or Corinne; I fielded the more general questions. It was a very worthwhile activity to showcase the variety of e-tools being used at AU, and, within the TESL community to showcase AU as a leader in Canada both in Distance Education and technological innovation. After the symposium was over, a number of participants approached us individually to tell us how much they had appreciated the session and how much they had learned from it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".