Transforming the online learning space through advanced development retreats
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.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".