Self-Directed Online Training for LSP Teachers: Challenges and Solutions in the LSP-Teoc.Pro Project
Bibliographic record
Abstract
The use of new technologies is one the main characteristics of 21st century educational systems. In this regard, the implementation of online courses became even more significant during the COVID-19 pandemic, as online tools can enhance an interactive experience even in circumstances where attending traditional classes is impossible or inconvenient. This paper describes the project LSP-TEOC.Pro, which is based on the development of a self‐directed online course for the training of LSP teachers in tertiary education. The course aims to fill an important educational gap, in that LSP teachers rarely receive specific training and this issue seems to emerge at a global level. The course is freely available worldwide and the discussion presented in this paper focuses on the main advantages, limitations, and potential developments of this type of course. In particular, it is shown that computer self-efficacy and motivation are key constructs in online self-directed learning, and simple strategies can be implemented to favor their enhancement.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".