A AVALIAÇÃO DE CURSOS A DISTÂNCIA APLICANDO OS PRESSUPOSTOS DA PESQUISA AVALIATIVA FORMATIVA
Bibliographic record
Abstract
One of the criteria for the credibility and trustworthiness of scientific research is the methodological procedure adopted and it falls to the researcher to operationalize the theoretical assumptions in order to establish how to collect and analyze research data. In this article there is a description of a method for applying Formative Evaluative Research, a type of research for evaluating processes, be they methods of service, teaching or even proposals for training, as is the case here. The method created is part of a doctorate study, validated through evaluation in a bi-modal course (a course where activities are carried out both locally and at a distance) in teacher training for the integration of technological resources in teaching practice at a Canadian university. Among the results we see that, in addition to the central matter of research, it is essential to prepare guiding questions to be answered at every stage of an evaluative process as they make the researcher concentrate on the central focus of his research. In courses where students have both local and distance activities, the sources of data are rich and the variables that arise during research are manifold, leading the researcher to disperse the focus of his evaluation. A limit to be considered is that the method should be applied during the training process so that the researcher can more easily access the subjects involved in the process. In this way it will always be possible, when deemed necessary, to complement the information that has been collected.
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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 | 0.160 | 0.346 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".