Collegial evaluation of online English for Specific Purposes (ESP) courses
Bibliographic record
Abstract
This paper reports on a joint pilot study project between two universities in Ecuador and Sweden to develop a practical working framework for the evaluation of each respective institution’s online/blended courses in English for Specific Purposes (ESP). The basis for the evaluation is the Conversational Framework (Laurilland, 2012), later developed in the form of a MOOC course in online and blended learning. This course is offered by the Future Learn social learning platform, which offers a large variety of online courses from a consortium of universities worldwide. The teaching and learning of ESP is characterised as the development of learner concepts and practice through interaction between the instructor and learner peers through collaboration and interaction. The learning process in ESP is envisaged using the Conversational Framework in terms of six basic learning types: acquisition, collaboration, discussion, inquiry/investigation, practice, and production. Our work reports on the process of assessing each other’s online courses in terms of the extent to which opportunities are provided for students to engage in these learning types. Results from this pilot study suggest that the Conversational Framework can provide a simple, robust, and transparent basis for the initial evaluation of online courses.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".