Effective Teaching & Teaching Evaluation Practices: Canadian University Geography Departments
Notice bibliographique
Résumé
In this thesis teaching evaluation practices in Canadian university geography departments are examined. The objective of this research is to identify good practices for teaching evaluation that can be applied within geography departments at Canadian universities and may be applicable to other departments and within other countries. In order to meet this goal a number of research questions were identified. These include: What is effective teaching in higher education?; What is effective teaching within the discipline of geography in higher education?; How and why is teaching evaluated in higher education?; What is the breadth of teaching evaluation practices currently used in geography departments within Canada?; How are the results of teaching evaluations used to enhance teaching quality within the discipline of geography in higher education?; How are the results of teaching evaluations used to reward teaching excellence within the discipline of geography in higher education?; and, What are ‘good’ teaching evaluation practices within the discipline of geography in higher education? \nA thorough review of the literature resulted in the development of a conceptual framework for effective teaching and for effective teaching evaluation. Both of these frameworks were tested using empirical data. The empirical data were collected from a national-level survey of geography departments across Canada (n=10), oral interviews with chairs of Canadian geography departments (n=23) and oral interviews with individuals suggested by chairs of Canadian geography departments (n-11).\nThe research provided validation of the conceptual framework of good teaching. This framework identified eight parameters of good teaching: discipline knowledge, course organization, delivery of instruction, student/instructor interaction, assessment tasks, administration, professional development and skill development. The results from the research suggested that the original conceptual framework of effective teaching evaluation was too simplistic. It also demonstrated that effective teaching evaluation occurs within the demographics and culture of place which results in the creation of an environment of evaluation. In this environment of evaluation seven parameters of an effective teaching evaluation system are identified including: defining good teaching, operationalizing good teaching, defining purpose of teaching evaluation, using a multiplicity of tools, employing an iterative teaching evaluation cycle, including a system of awards and including a mechanism for teaching enhancement.\nIn the future these conceptual frameworks can be tested in a variety of other disciplines and within different countries. The primary stakeholder in this research was chairs of geography departments. There are a number of other stakeholders described in this research and the frameworks could be tested from their perspectives.
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 | 0,050 | 0,104 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,009 |
| Études des sciences et des technologies | 0,015 | 0,007 |
| Communication savante | 0,014 | 0,003 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».