Shifting Pedagogies during COVID-19: The Professional Learning of Teacher Educators regarding Digital Technology in a Time of Crisis and Change
Bibliographic record
Abstract
The COVID-19 crisis that began in March 2020 presented teacher education with many challenges, among them the urgent need to support teacher educators with the sudden move to remote teaching. Teacher educators immediately started rethinking their pedagogy for a new digital classroom environment as universities rushed to provide overnight supports to help with this transition. Empirical research on how teacher educators have navigated their professional roles during this period of uncertainty is limited. There is also limited research on the professional learning of teacher educators, particularly in the area of digital technology. The aim of this study was to explore teacher educators’ shifts in views and pedagogy using digital technology as they adapted to teaching in an online environment. Investigating those shifts also required examining teacher educators’ backgrounds and experiences with digital technology and their professional learning during the COVID-19 pandemic. In this qualitative multi-case study, I explored the experiences of six teacher educators within a graduate teacher education program in a large urban university in Canada. Each participant took part in two semi-structured interviews and described teaching and learning artifacts that reflected a change in their pedagogy. Three key findings emerged from this study. First, the COVID-19 pandemic served as a catalyst for change in teacher educators’ enacted pedagogies. Second, teacher educators displayed heightened responsivity in two ways. They placed teacher candidate learning at the center of the online classroom and offered flexible and accessible learning experiences. Also, teacher educators’ changes in pedagogy were frequently shaped by inequities exacerbated by the pandemic. Lastly, study participants discussed different possibilities for teacher educator pedagogies, with an emphasis on flexible approaches to teaching and learning, a changed role in an online setting, and digital technology integration. Implications for teacher educators include the need to engage in ongoing learning, understand the role and uses of digital technology in the teacher education classroom, and recognize their ongoing responsibility to foreground equity and social justice in their teaching. Thus, teacher education programs must include formal and informal structures and resources that support the individualized and diverse professional learning of teacher educators.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".