Rapid Instructional Design: Increasing Educator Capacity for Developing Elearning Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dr Iain Doherty and I spoke for approximately 20 minutes on rapid instructional design as a process for allowing educators to quickly and easily author elearning episodes to enhance their teaching. We made particular mention of the need for quality control and evaluation of the learning designs in the rapid instructional design process. We took questions for five minutes. We were asked about how we would evaluate the impact of the learning designs and discussion lead to the conclusion that there is a need to show benefit at the level of student learning. We were also asked about how we would ensure the quality of the designs. We suggested that we would work with the educators to help them with their designs. This led to further discussion about whether quality control would necessarily slow down the rapid instructional design process. Finally, one attendee let us know that she was about to start a PhD looking at Faculty development. We met with the attendee after our session and agreed to provide previous research along with our session paper.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it