Impact assessment of academic support provided by tertiary learning advisors
Bibliographic record
Abstract
In New Zealand higher education (HE), there is a lack of consistent ways of collecting evidence of the impact made by academic literacy support from Tertiary Learning Advisors (TLAs) on students’ academic performance, retention, and success. TLAs in New Zealand and Australia are primarily involved in providing learning support to students in post-secondary education to encourage development of their academic literacy and essential study skills. They are professional educators who advise students on issues related to academic writing and other academic skills, such as time management or exam preparation, to facilitate achievement of students’ goals of tertiary study (Griffith University, 2021). While it may be recognised that provision of learning support is desirable for a meaningful and successful HE experience for many students, hard evidence that learning support makes a difference to student retention and academic performance is difficult to find (Acheson, 2006, as cited in Breen and Prothero, 2015). This presentation sought to share an attempt to address this issue by investigating the impact of embedded academic literacy support provided by TLAs to three cohorts of students enrolled in undergraduate social work and early childhood education programs at my ITP (Institute of Technology and Polytechnic) in Auckland, New Zealand. Existing research in Australia, Canada, and the United Kingdom suggests that support that embeds academic literacy development in disciplines, rather than academic support that is generic and/or provided through foundation courses, represents a best practice model (Glew et al., 2019).
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".