MétaCan
Menu
Retour à la cohorte
Enregistrement W2610491818 · doi:10.18260/1-2--5129

Teaching Opportunities For Graduate Assistants(Toga)

2020· article· en· W2610491818 sur OpenAlexaffabout
Elaine Crocker, R. Venkatesan, Steven Shorlin, N. Dawood

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueExperimental Learning in Engineering
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésGraduate studentsTeaching and learning centerTeaching assistantGraduate educationMedical educationProfessional developmentFaculty developmentTeaching methodPsychologyMathematics educationMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Teaching Opportunities for Graduate Assistants (TOGA) Abstract This paper describes the evolution and components of a program designed to enhance the teaching opportunities and expertise of graduate teaching assistants in the Faculty of Engineering and Applied Science at Memorial University of Newfoundland. The primary focus of the program is to provide professional development to graduate teaching assistants related to teaching and learning. It is a collaborative initiative involving the Faculty of Engineering and Applied Science, the School of Graduate Studies and the Instructional Development Office in the Division of Distance Education and Learning Technologies. Introduction The Teaching Opportunities for Graduate Assistants (TOGA) program operating within the Faculty of Engineering and Applied Science at Memorial University is underpinned by three main guiding principles: to enhance the development of graduate teaching assistants with respect to teaching and learning; to more positively impact the learning of undergraduate students; and to support the ongoing mentoring of graduate students by individual faculty. Currently, graduate programs in many North American universities include opportunities for graduate students to experience teaching-related activities and professional development opportunities related to teaching and learning. Many examples of this resulted from the Preparing Future Faculty initiative in the United States1. Canadian universities such as The University of Waterloo, The University of Victoria, and the University of Western Ontario also offer such opportunities2.Wulff and Austin (2004) argue that graduate teaching assistants should be given a variety of teaching assignments as part of a systematic process3. This is possible within the TOGA model. Evolution of Model The original model for TOGA that was piloted throughout our university from 2005 - 2007 encompassed three main categories of teaching assignments for graduate student teaching assistants (TAs). Teaching assistants at level 1 were considered to be beginning or novice TAs who would not provide much direct instruction to undergraduate students. At level 2, a graduate teaching assistant would be more involved in providing instructional support to undergraduate students and employed in such roles as tutoring, providing assistance in labs, or facilitating small group discussions. At level 3, graduate teaching assistants would be assigned such roles as being a course teaching assistant (teaching to a maximum of three hours), a professional development facilitator for TOGA 2 teaching assistants, or a course curriculum assistant. A systematic program of professional development was organized and provided for the graduate teaching assistants at the TOGA 2 and TOGA 3 levels. Completion of a professional development program designated at each level is required in order for the graduate student to be eligible for a TOGA 2 or TOGA 3 appointment. When a graduate student is assigned to the TOGA 2 or TOGA 3 level, he/she receives a stipend of $250 or $500, respectively, from the School of Graduate Studies in addition to the normal compensation for the TA task4.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,813
Score d'incertitude au seuil0,530

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,139
Tête enseignante GPT0,270
Écart entre enseignants0,131 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission2
Résumé présentoui

Explorer davantage

Même sujetExperimental Learning in EngineeringTravaux en français237 207