Making distance visible: Assembling nearness in an online distance learning programme
Bibliographic record
Abstract
Online distance learners are in a particularly complex relationship with the educational institutions they belong to (Bayne, Gallagher, & Lamb, 2012). For part-time distance students, arrivals and departures can be multiple and invisible as students take courses, take breaks, move into independent study phases of a programme, find work or family commitments overtaking their study time, experience personal upheaval or loss, and find alignments between their professional and academic work. These comings and goings indicate a fluid and temporary assemblage of engagement, not a permanent or stable state of either “presence” or “distance”. This paper draws from interview data from the “New Geographies of Learning” project, a research project exploring the notions of space and institution for the MSc in Digital Education at the University of Edinburgh, and from literature on distance learning and online community. The concept of nearness emerged from the data analyzing the comings and goings of students on a fully online programme. It proposes that “nearness” to a distance programme is a temporary assemblage of people, circumstances, and technologies. This state is difficult to establish and impossible to sustain in an uninterrupted way over the long period of time that many are engaged in part-time study. Interruptions and subsequent returns should therefore be seen as normal in the practice of studying as an online distance learner, and teachers and institutions should work to help students develop resilience in negotiating various states of nearness. Four strategies for increasing this resilience are proposed: recognising nearness as effortful; identifying affinities; valuing perspective shifts; and designing openings.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".