Attracting, engaging and retaining: new conversations about learning: Australasian student engagement report
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».