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Enregistrement W2280821800

From Teaching Assistant (TA) Training to Workplace Learning.

2014· article· en· W2280821800 sur OpenAlexvenueaboutno aff
Cynthia Korpan

Notice bibliographique

RevueCollected Essays on Learning and Teaching · 2014
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEvaluation of Teaching Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésApprenticeshipGrading (engineering)PedagogyPsychologyNothingTeaching assistantSupervisorTeaching methodMathematics educationSociologyManagementEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In this paper, I propose a renewed look at how teaching assistants (TAs) are being prepared to fulfill their duties in higher education. I argue that the apprenticeship model of learning that is currently in use be replaced by the more holistic workplace learning approach. Workplace learning theories take into consideration the complexity of the learning situation of the TA. The teaching assistant came into my office to talk about the work that she had been assigned. Quite distraught, she relayed how she knew nothing about the course content and did her best to follow the guidelines provided by the course supervisor. Despite seeking teaching support, students in the TA’s lab soon began to lack confidence in her as their teacher. In turn, her confidence was shattered and her ability to perform as a TA quickly diminished. It is often the case that when I walk across campus, faculty will stop me to tell me his or her story about working with teaching assistants. One day, a professor told me the story about how she had found out, due to poorly graded assignments that the TA, instead of grading the assignments herself, had delegated this to her husband, who was not affiliated with the discipline or university. In my role as the TA Training Program Manager at the University of Victoria (UVic), I hear many stories about TAs. No matter if the story comes from a teaching assistant (TA) or faculty member, both are seeking the same end result – that TAs do their job well. Too often a TA feels he or she is not able to fulfill his or her duties sufficiently because of a lack of skills and required knowledge, which leads to a lack of confidence and employing inappropriate methods of approaching his or her work. My colleagues and I at institutions across North America devise programs and courses that address issues pertaining to TAs’ lack of skills and confidence. I began working in the field of teaching assistant training and graduate student professional development in 2006. At that time, one of the main foci was on programs that prepared graduate students to be faculty members. This focus was influenced by the introduction in the early 2000s of a heavily-funded program in the United States (US), called Preparing Future Faculty (PFF) that favours professional development of future faculty. Despite similar programs already existing, the development of this program helped spawn PFF-type programs at most higher education institutions in North America. UVic, as with many Canadian post-secondary institutions, had such a program put in place in the mid-2000s. The program at UVic changed in 2012 (approved by Senate in January with first intake of students in September, 2012) to become Learning and Teaching in Higher Education (LATHE), a six-unit (two 1.5 credit courses, and one 3.0 credit course) graduate certificate program that is listed on the student’s transcript (not just a notation Collected Essays on Learning and Teaching, 2014, Vol VII, No 2

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)0,013
score de la tête « metaresearch » (Gemma)0,053
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,928
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0130,053
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0070,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,004
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 Opus0,055
Tête enseignante GPT0,379
Écart entre enseignants0,325 · 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.

Devis d'étudeSans objet
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

Citations2
Publié2014
Routes d'admission2
Résumé présentoui

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