Online learning and teaching from kindergarten to graduate school
Bibliographic record
Abstract
This volume is the product of the collaboration of invited participants and editors at the Eleventh Working Conference of the Canadian Association for Teacher Education that was held online with the University of Calgary from October 14–16, 2021. The impetus for the conference theme, online learning and teaching from kindergarten to graduate school, emerged alongside the worldwide pivot to online education in response to the global pandemic. This volume examines a variety of ways in which Canadian researchers in teacher education are analyzing, designing, and evaluating diverse online learning pedagogies, learner experiences and outcomes in K-12 and post-secondary education contexts. Chapters are organized in four sections: 1) Online Learning & Teaching in K-12, 2) Relationships & Relationality in Online Learning & Teaching, 3) Online Learning & Teaching in Higher Education, and 4) Conceptualizing Learner Centered Models in Higher Education. Knowledge building and collaboration through the working conference and the chapters in this publication aim to enhance and extend understanding, communication, and critical analysis among Canadian and global teacher educators; this publication also seeks to contribute to research and practice in response to the imperative that “teacher education programs must prepare teachers for the schools of the future – teachers who are experts in disciplinary content, knowledgeable about the latest research on how people learn, and able to respond creatively to support each student’s optimal learning” (Sawyer, 2022, p. 671) in diverse modalities and contexts for learning including online, blended, hybrid, and in person engagements.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".