Academic Literacies as Cornerstones in Course Design: A Partnership to Develop Programming for Faculty and Teaching Assistants
Bibliographic record
Abstract
We discuss an educational development approach to embedding academic literacies instruction within disciplinary curricula. This developmental, embedded approach contrasts with the generic, extra-curricular, study-skills approach adopted in many universities. Learning Commons partners at York University, including librarians, writing instructors, and learning skills counsellors, collaborated with educational developers in the York Teaching Commons to design a program for course instructors and teaching assistants (TAs) who seek to improve their students’ academic literacies. This program includes interactive workshops focusing on strategies to facilitate the redesign of courses and assignments so as to give explicit attention to process-related practices and abilities involved in library research and writing. The academic theory underpinning this program is outlined along with its key content elements. We also describe how the program draws on SPARK (Student Papers and Academic Research Kit), an online resource, created by the York Learning Commons under a Creative Commons license with the goal of helping students succeed with written academic assignments. Feedback from instructors and TAs support that the program has played an important role in helping them to question their assumptions and redesign their teaching practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".