Finding Our Way Through a Pandemic: Teaching in Alternate Modes of Delivery
Bibliographic record
Abstract
The COVID-19 pandemic caused a dramatic pivot to online learning and has forced teachers to critically re-evaluate teaching strategies. Thus, the questions, framing this self-study were: 1) How will I be able to do the learning activities I normally do in the classroom online including individual work, group activities, debates, and whole class discussions? and 2) How will I be able to pivot my signature lessons to the alternate delivery model? This self-study of teaching and teacher education practices (S-STTEP) builds on previous research to transformtraditionalface-to-face lessons into effective online lessons using alternate modes of delivery. In this paper, Ted shares some of his signature lessons including ice-breakers, critical response questions, discussions, group activities, and jigsaws, utilizing Moodle, Big Blue Button, Padlet, Google Docs, and other online tools. With Georgann’s help as a critical friend, Ted critically analyzed his teaching of Master of Education graduate students through S-STTEP. In addition, he exploredcomparative ethnographic narrative(CEN) as another way of knowing within the S-STTEP space. Data included detailed weekly reflections. In addition, students provided written feedback at the end of each class, and at the end of term through a survey and course evaluation. Ted shared weekly electronic journal reflections and student feedback with Georgann, via email and teleconferences. Then, together Ted and Georgann made meaning from these field texts. The research text evolved fromteacher-to-teacher conversations. Promising pedagogies for synchronous and face to face learning were identified with several signature lessons the focus. Georgann, as Ted’s critical friend helped confirm and verify the most significant results amongst the many interesting reflections made.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".