Transforming the online learning space through advanced development retreats
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
Learning technologies have the potential to transform Higher Education, although multifaceted demands on staff time, confidence and training in using new technologies, and a lack of support can make this transformation difficult. The University of Huddersfield recently transitioned to a new virtual learning environment (VLE), which provided the opportunity to change the way staff view and use the new VLE for teaching and learning. As part of this project, three off-site retreats were run to help staff to reflect on and develop their teaching practice to better support student learning in the digital space and develop advanced online resources that support the democratisation of learning, close differential attainment gaps and give every student the best chance of success. Although much is written about different models of practice, there is a lack of theory and conceptualisation around changing practice. Examining the motivations and experiences of staff who participated provides insight into the challenges of implementing change on an institutional level, whilst examining their setup and design highlights ways to support staff during this process. Using participant feedback and experiences to underpin this research, we explore the immediate and ongoing outcomes of these off-site retreats to help transform the University’s approach to technology-enhanced learning.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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