Finding Our Way Through a Pandemic: Teaching in Alternate Modes of Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 transform traditional face-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 explored comparative 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 from teacher-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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it