Creating an Open Online Educational Resource to Support Learners as They Navigate Their Studies Alongside Work and/or Family
Bibliographic record
Abstract
As labour markets undergo rapid and profound transformations, lifelong learning is essential to ensure a responsive, competitive, and skilled workforce. Mature learners are a diverse group, but in comparison to their younger student counterparts, are more likely to have employment and/or caring responsibilities. This field note discusses the development and features of a novel online open educational resource, called At a crossroads: Navigating work and/or family alongside study (At a crossroads for brevity). The resource aimed to assist university students to both learn about the support options available to them as well as to consider how they themselves might make decisions if they experienced a conflict between their student/work/family roles. At a crossroads is innovative in terms of how it was developed (i.e., via survey-based research, story completion method, and consultations sessions with tertiary students) and in terms of what it is (i.e., an online interactive resource that incorporates short dramatizations, social polls, and opportunities to reflect). Our experience in developing this resource caused us to consider how making resources designed to be engaging and informative, while encouraging, positive changes, must be part of the solution. This is especially so when there is significant concern around the overall well-being of tertiary students and their course completion rates. While universities have attempted to offer a range of tools to support their students, on-demand online resources such as At a crossroads are easily accessed, free to use, and deliver content in an engaging manner.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".