Notice bibliographique
Résumé
Universities are using new technologies for both practical aims (to reduce costs or to cater for greater student numbers without increasing teacher numbers) and/or pedagogical aims (to improve students‘ learning). Frequently new technologies are introduced before the impact of these technologies on learning is fully assessed. \n \nThis thesis focuses on the introduction of virtual microscopes into histology and pathology teaching in the Health Sciences at Murdoch University. \n \nAn exploratory study was conducted in 2006 in which 47 students were randomly allocated to one of two groups. In their laboratory work one group used optical microscopes (Optical Group) and the other group used virtual microscopes (Virtual Group) during one semester. At the beginning and the end of the semester, an ASSIST survey (Tait, Entwistle, & McCune, 1998) was undertaken to determine any changes in the students‘ learning approach. As part of the assessments in their course, students completed an Attitude Survey about their attitudes to microscopes. Students were also required to complete a log book detailing their time spent studying. The results were analysed using appropriate statistical tests, frequencies, Chi-square, correlation, ANOVA, Two-way ANOVA, and the General Linear Model. The exploratory study tested the research design and the methods for analysing the data for the main study. Some modifications were made to the Attitude Survey prior to the commencement of the main study. \n \nIn 2007, the main study was undertaken with 293 students. In addition to the ASSIST survey, the Attitude Survey, and the log books, the students were asked to participate in focus groups and interviews to build a richer picture of microscopes and learning. \n \nThe results indicated changing trends in the students‘ learning approaches. The Optical Group moved from surface to strategic; the Virtual Group from deep and strategic tosurface learning during the teaching period. However, there were no statistically significant differences between the groups. The use of virtual microscopes in histology and pathology laboratories therefore does not encourage deep learning any more than the use of optical microscopes. The virtual microscopes do, however, enable students to study at times and locations that are convenient to them and they are easier to use than the optical microscopes. \n \nThe students‘ responses to the items in the Attitude Survey were content analysed and 15 themes emerged from the data. These themes indicated that there are critical issues, such as authenticity and group work, which need to be addressed when introducing virtual microscopes into the classroom. \n \nIn identifying critical issues and ensuring there were no detrimental effects in using virtual microscopes, recommendations were developed for histology and pathology educators to assist the implementation of virtual microscopes into a university curriculum. This was done with a view to enhancing pedagogical practice and included the development of microscope skills, authenticity, linking theory with practice and group work.
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 | 0,004 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 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 ».