Experiencing the shift: How postsecondary contract and continuing faculty moved to online course delivery
Bibliographic record
Abstract
The shift to online learning that occurred in March of 2020, created an unprecedented period of intense work for faculty and sessional instructors at the post-secondary level. This shift necessitated courses be adapted under short timelines, new technology be integrated into course design and teaching strategies and assessment methods be adapted for an online environment (Van Nuland et al., 2020). This study examines how sessional instructors, referred to in this chapter as contract faculty, and continuing full-time faculty members delivering the same online courses experienced this shift. While the demands of a continuing faculty position call for balancing of teaching, research and service responsibilities, contract instructors have their own unique stressors (Karram Stephenson et al., 2020). Contract faculty lack job security, are paid by the course and often receive their teaching assignments with short notice. By examining their perspectives on delivering the same courses online, we learn that the shift to online teaching resulted in additional work in order to adapt courses to the online environment, with faculty describing the challenges of balancing the additional work with other responsibilities of their position. Concerns of participants focused on a perceived inability to develop relationships with students in an online environment.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| 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".