Symposium 1: Networking educational administrators through e-Teaching
Bibliographic record
Abstract
This paper is about e-teaching in the context of a university graduate program and how the program metamorphosed from being entirely campus based to entirely online, and from declining enrolments to increasing enrolments. It is about the faculty and lecturers who conducted a critical analysis of their particular situation, examined their options, researched other program delivery options and finally settled on a solution that has proven successful. Because of this labour-intensive process, they discovered that the networking processes undertaken by them to complete their mandate enabled them to become better networking mentors as e-teachers. This is their story and it sheds a critical light on various delivery models currently deployed by universities worldwide. Most importantly, it demonstrates that a good deal of reflection and agreement on what good teaching is and how it can be achieved has to be conducted prior to delivery model adoption. It also raises the question as to the wisdom of across-the-board institutionally decided program delivery choices. Such choices not only affect e-learners down the road but also directly affect faculty’s motivation and ability, as a community of practice, to carry out their teaching responsibilities efficiently and sustainably. Data was gleaned from an ongoing study, mostly from a series of semi-structured interviews with administrators and faculty, including lecturers, who participated, over the course of a two-year period, in transferring this program from on-campus to online. Six semi-structured interviews, each lasting between 60 and 90 minutes, were conducted. Access to work documents, ongoing email exchanges and personal conversations completed the data set. The interview guide used was developed to highlight respondent experience over the course of the two-year program migration online, focusing on the move from on-campus teaching to e-teaching, course delivery model selection, and an open questions for respondents to add related comments. Complete data analysis and interpretation is proceeding as this proposal is being sent to conference organisers. Results show a high degree of unanimity in the decision to adopt BOLD as program delivery mechanism. Respondents describe how it gave them back what they most liked in their work: direct and continuous, regular contact with their students, free-flowing discussions on concepts, principles and their applications, access to students who would normally not enrol in their courses and a workload that was more manageable than other delivery systems they had known.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".