Equity, pedagogy and inclusion. Harnessing digital technologies to support higher education access and success
Bibliographic record
Abstract
Australia is striving to reach a 20 per cent across-the-board higher education target participation rate for students from low socio-economic backgrounds by 2020. This paper focuses on enabling higher education access for students who might otherwise be excluded by complex socio-economic circumstances. Digital learning and communication tools provide a vital pathway to higher education and a means of re-engineering pedagogies to better meet students’ learning needs, especially when students cannot access regular on campus face-to-face teaching. On-line learning enables both access to higher education and effective ways of engaging students with learning, especially those who are isolated by location or by circumstances associated with work and family commitments. This paper focuses on broad-brush factors students say supported their study success while undertaking an externally delivered, on-line teaching degree. It reports on students’ decision to study and their subsequent on-line study experiences, progression and outcomes. Analyses of students’ perceptions indicated the value of on-line pedagogy supported by ‘face-to-face’ interaction (albeit at a distance) with academics. While students wanted to study externally and on-line and in their ‘own time’ and at their own ‘pace’, all valued a personal, on-going relationship with their lecturer, teacher or other university-based mentor. Overwhelmingly, students , found the on-line learning relatively straightforward to navigate, practical and rewarding, but all wanted conversations with a “real person” although this could be on the phone, in a video conference/Skype situation or as part of a remote site ‘tutorial’ or consultation.
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.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.026 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".