Targeted and Tailored: The Importance of a Personalized Approach to Open Distance Learning Support
Bibliographic record
Abstract
The article explored the need to provide ODL students with support that is more personalised in nature; support that speaks to the presence of a “human touch,” and specifically refers to an individual whose primary focus is on relationship building and fostering a sense of community and care amongst its online students. Although literature reveals the importance of online student support, including affective, cognitive, and systemic support, not enough is known about the value of having a role whose sole focus is personalised affective support in an online learning environment. Within a qualitative approach, the study used 12 semi-structured interviews and five focus groups with 34 participants to explore the value ODL students place on non-academic support. The findings revealed that while participants were familiar with who their Programme Success Tutor (PST) was, for various reasons, there was no shared or common understanding of the role as being intentionally affective in nature. Based on the findings, the study suggests that a PST or a similar role at an ODL institution should be closely aligned with the needs and expectations of the students for whom this role is envisaged. We further recommend using Karp’s four non-academic support mechanisms as a framework when establishing or revising such a support role.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".