Minimising attrition: strategies for assisting students who are at risk of withdrawal
Bibliographic record
Abstract
This paper explores strategies aimed at minimising attrition by encouraging persistence among online graduate students who are considering withdrawal. It builds upon earlier studies conducted by a team of researchers who teach online graduate students in health care at Athabasca University. First, in 2008–2009, Park, Boman, Care, Edwards, and Perry reviewed assumptions held related to attrition of online learners and defined key terms such as persistence and attrition. Next, Perry, Boman, Care, Edwards, and Park explored factors that influenced online students’ decisions to withdraw. Reported in this paper are strategies related to course design, course delivery, and programme organisation that could reduce attrition rates. An additional section of the paper focuses on strategies to ease the re‐integration of students who have withdrawn and subsequently want to return to their studies. Rovai’s Composite Persistence Model and Harter and Szurminski’s Project Assuring Student Success (PASS) programme are used as a framework for analysis and for generation of recommended strategies.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".