Development of ethical codes for instructors engaged in distance education: a Delphi study
Bibliographic record
Abstract
Ethical codes are written documents that delineate a set of rules and principles that guide the duties and responsibilities of professionals within a professional organization from an ethical perspective. The research aims to develop an ethical code framework for instructors engaged in distance education. The Delphi, a qualitative research method, was employed to achieve this aim. The study included 22 distance education experts, selected via purposive sampling. The Delphi technique, conducted in three rounds, commenced with an evaluation of the ethical dimensions of distance education. In the study’s second phase, we invited participants to suggest additional ethical codes for the agreed-upon dimensions. We collected the suggestions and agreed on specific ethical code items in the third round. The researchers thus established the final form of the ethical code list. This process resulted in developing an ethical code framework for instructors engaged in distance education. The framework comprises sixdimensions: Instructional Design (ID), Social Interaction (SI), Content Provision (CP), Technology Usage (TU), Management (M), and Assessment and Evaluation (AE). It includes 62 ethical code items. The study demonstrates that the ethical code dimensions align with the roles of online instructors, as described in the relevant literature. This alignment substantiates the ethical code framework's validity and reliability.
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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 | 0.158 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".