Exploring new learning paradigms in ODL: A reflection on the paper of Barber, Donnelly and Rizvi (2013): “An avalanche is coming: Higher education and the revolution ahead”.
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
<p class="Paragraph1"><span lang="EN-US">The paper of Barber, Donnelly &amp; Rizvi (2013): “An avalanche is coming: Higher education and the revolution ahead” addresses some significant issues in higher education and poses some challenging questions to ODL (Open and Distance Learning) administrators, policy makers and of course to ODL faculty in general. Barber et al.’s paper does not specifically address the area of teaching and learning theories, strategies and methodologies per se. In this paper I would therefore like to reflect on the impact that the contemporary changes and challenges that Barber et al. describes, have on teaching and learning approaches and paradigms. In doing so I draw on earlier work about future learning paradigms and navigationism (Brown, 2006). We need a fresh approach and new skills to survive the revolution ahead. We need to rethink our teaching and learning strategies to be able to provide meaningful learning opportunities in the future that lies ahead.</span></p>
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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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