Educational triage in open distance learning: Walking a moral tightrope
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>Higher education, and more specifically, distance education, is in the midst of a rapidly changing environment. Higher education institutions increasingly rely on the harvesting and analyses of student data to inform key strategic decisions across a wide range of issues, including marketing, enrolment, curriculum development, the appointment of staff, and student assessment. In the light of persistent concerns regarding student success and retention in distance education contexts, the harvesting and analysis of student data in particular in the emerging field of learning analytics holds much promise. As such the notion of educational triage needs to be interrogated. Educational triage is defined as balancing between the futility or impact of the intervention juxtaposed with the number of students requiring care, the scope of care required, and the resources available for care/interventions.</p><p>The central question posed by this article is “how do we make moral decisions when resources are (increasingly) limited?” An attempt is made to address this by discussing the use of data to support decisions regarding student support and examining the concept of educational triage. Despite the increase in examples of institutions implementing a triage based approach to student support, there is a serious lack of supporting conceptual and theoretical development, and, more importantly, to consideration of the moral cost of triage in educational settings.</p><p>This article provides a conceptual framework to realise the potential of educational triage to responsibly and ethically respond to legitimate concerns about the “revolving door” in distance and online learning and the sustainability of higher education, without compromising ‘openness.’ The conceptual framework does not attempt to provide a detailed map, but rather a compass consisting of principles to consider in using learning analytics to classify students according to their perceived risk of failing and the potential of additional support to alleviate this risk.</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 | 0.010 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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