The piloting of an Academic Literary Education Course (ALEC) to improve academic literacy of first semester undergraduate students in a Western Australian university
Bibliographic record
Abstract
Widening participation has enabled access to higher education for an increasing number of students via a range of non-traditional pathways. Consequently, whilst having a large repertoire of skills and experience, these students may not have developed sufficient competence in academic literacy. The School of Nursing and Midwifery has a large proportion of such students who have difficulty making the academic transition to university study. It is believed that a ‘built in’ approach to supporting these students to develop academic literacy will result in an improvement in their abilities to meet the requirements of the University and to better prepare them to graduate. This paper discusses the piloting of an Academic Literary Education Course (ALEC) for undergraduate students enrolled in the first semester unit of a three year health degree, within the disciplines of nursing and paramedical science. The students completed tests on academic literacy before and after completion of the ALEC. Particular areas of difficulty for students were found to be in referencing and unpacking the assessment question. When mean scores were compared between pre and post-tests in the same group, it was found there was a significant difference between scores at t value = -7,721, degrees of freedom = 181 with a p value of 0.000 or p value < .001. Recommendations include incorporating the ALEC approach in further stages of the undergraduate degree program to support developing levels of academic literacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".