Bibliographic record
Abstract
As former practitioners and advocates for classroom instruction seek to compare the relative advantages and disadvantages of face-to-face and online teaching by reporting on their primarily one-off experiences with developing and delivering online courses within a more traditional university culture, forays by more traditional universities into online education have begun to dominate the distance education and online literature.No less challenging or instructive, however, is the fundamental transformation that seasoned practitioners and administrators of distance education find themselves facing as they endeavor to systematically enhance old models of distance education by taking advantage of the e-learning environment.Some would argue, as in fact I frequently do, that this challenge is of a similar magnitude to the one faced by new entrants into the non-classroom learning environment, for classroom-teaching converts to online learning are often much more in control of their teaching and learning environment than are their counterparts in single or dual mode distance teaching systems.In the first instance, the institution has traditionally invested primarily in classroom teachers who are relatively free to determine how to deliver their courses (whether in a face-to-face or distributed setting) at any given time.In contrast, while teachers in an organization where distance delivery is considered as a mainstream activity find themselves supported by institutional infrastructures and learning/teaching support functions, they are also constrained by these very same features which, in the past, complemented the individual academic's expertise and served to create a comprehensive high quality learning environment for distance learners.Endnotes 1. Developed by IRRODL Editor, Dr.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.265 | 0.155 |
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".