Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".