Bibliographic record
Abstract
Open and distance education systems are highly diverse, but most adopt a familiar division between the construction and use of a package of relatively free-standing materials, and the support of learners before, during and after study. The use of computer mediated communication has rapidly increased with the take up of the World Wide Web, and distance educators are now adapting this technology for learner support as well as for the delivery of resources. Where learning is supported and led through online interaction, the boundary between taught course resources and learner support is breaking down. However, whatever the intensity of ICT usage, the quality of learner support is vital and impacts very directly on the effectiveness of the course in terms of retaining students and enabling them to achieve their learning outcomes. Evaluation has a vital role to play in ensuring that a quality system is in place and delivered, and in enabling a continuing process of improvement of the system, better to support learners as they study. Practitioner evaluators need to draw upon the expertise of specialist evaluators and the literature of methods and research findings in this area. Effective evaluation is evaluation that is 'fit for purpose ' and proceeds according to best practice in the field. It is not a single thing but a diversity of strategies, drawing in different ways on the key tools of review, planning, data collection, analysis and reporting. The practice of regular evaluation, with evidence that findings are used and reflected upon, is itself one of the indicators of a quality learner support system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.017 | 0.014 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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; both teacher heads agree on what is shown here.
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".