Teaching with Compassion: Autoethnographies from the front lines of e-learning
Bibliographic record
Abstract
In 2020 the landscape of teaching in higher education was forced to change given a global pandemic. As a result there were/are inevitable shifts in how course instructors develop and deliver their courses, as well as how they connect with students. Remote, or distance, learning is not a new phenomenon, and e-learning has been delivered across different institutions of higher education for approximately twenty years. However, scholarship in distance learning is dated and the empirical literature in digital pedagogy has gaps when it comes to best practices for teaching and learning in an online format. This paper highlights the importance of teaching with compassion as it fosters better relationships between instructors and students and helps to build community in learning environments. Relationships facilitate learning, and this is especially important in strained times - such as a higher concentration of online teaching and learning due to a pandemic. The notion of compassionate teaching is described. Using self ethnography and drawing on examples from their own course experiences, the authors present what has worked well in delivering small and large courses online. In particular, the first half of the paper focuses on backward course design, multimedia, accessibility, and forward/backward extensions. The latter half of the paper describes strategies embedded in a course and their positive effects for instructors and learners.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".