Instructor's Privacy in Distance (Online) Teaching: Where Do You Draw the Line?
Bibliographic record
Abstract
The exponential growth of distance learning provision in the past forty years poses pertinent and critical ethical issues. Students participating in distance education via an online course are required to recognize and resolve various ethical issues, some of which focus on the instructor's actions. The university, too, as it supports students and instructors, is ethically involved in the process. As the number of online classes continues to grow, an increasing number of articles are being written about student and program integrity but there is a notable absence of articles or research focusing on the emerging issue of institutional integrity in relation to instructors. The ideology of New DEEL’s (Democratic Ethical Educational Leadership) speaks to the ethical basis of online teaching and this paper delineates an authentic ethical dilemma for which a universalized and generalized ethical model is proposed to be usefully applied to all issues involving privacy of participants. All names are fictitious. Distance education is a discipline that subsumes the knowledge and practice of pedagogy, of psychology and sociology, of economics and business, of production and technology. (Anderson & Elloumi, 2004, Athabasca University, Canada’s Open University) There can be no doubt that much good practice has evolved in the field already, largely organically, and there are as yet few comprehensive guides available for any university or college in the United Kingdom or elsewhere venturing into the open and distance learning market. This is in stark contrast to many other professions—the practices of law and medicine, for instance, both of which are governed by explicitly formulated ethical principles (Gourley, 2007 Vice-Chancellor of the Open University, UK).
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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 | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.019 | 0.049 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".