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Record W2280821800

From Teaching Assistant (TA) Training to Workplace Learning.

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

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsApprenticeshipGrading (engineering)PedagogyPsychologyNothingTeaching assistantSupervisorTeaching methodMathematics educationSociologyManagementEngineering
DOInot available

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0090.006
Open science0.0010.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.379
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2014
Admission routes2
Has abstractyes

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