Attracting, engaging and retaining: new conversations about learning: Australasian student engagement report
Bibliographic record
Abstract
The primary purpose of the Australasian Survey of Student Engagement (AUSSE) is to develop evidence-based conversations that enhance students' engagement with university education. AUSSE is developed and managed by the Australian Council for Educational Research (ACER). Student engagement is defined as students' involvement in activities and conditions that are linked with high-quality learning. Twenty-five higher education institutions, more than half of the universities in Australia and New Zealand, participated in the 2007 AUSSE. The institutions cover the range of each country's higher education providers. This report provides general, cross-institutional and cross-national results of the AUSSE from the 2007 data collection. The survey provided the following new insights into how students interact with university: international students spend more time on campus then domestic students; three-quarters of campus-based students do at least a quarter of their study online, and students who do some or all of their study online spend less time on campus; students working for pay on campus report higher levels of engagement than others; two-thirds of full-time students are in paid work off campus; greater participation in off-campus paid work is associated with greater engagement in work-integrated forms of learning, and feeling less supported by the institution, but roughly the same level of engagement in other activities, except for those working more than 30 hours; students' interactions with staff and participation in 'enriching' educational activities are particularly low; students become more engaged in university study between first and third years, although third-year students see themselves as being less supported by staff; participation in key developmental activities such as internships, foreign language study, community service or a study group is low but increases across the years; and students are more satisfied, perform better academically and are less likely to drop out when high standards are set and they are provided with integrated support to help them succeed. Universities use student engagement information in a range of ways to enhance their educational programs and student services, such as: holding focus groups with learners to explore key findings in more detail, distributing reports around the university, and putting results on the web; discussing findings with key staff such as Department Heads, Faculty Deans, and Student Services; considering the spaces in which students learn; rationalising the information they collect from students; developing new ways of thinking about students' involvement with university; forming plans to manage students' engagement in education over time; and looking at what universities in the USA and Canada do to help students.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".