K-12 School Leaders’ Application of Professional Learning Communities (PLCs) During a Pandemic
Bibliographic record
Abstract
Using reflective practice inquiry (Schön, 1983), this article highlights the role of K-12 school leadership approaches in facilitating Professional Learning Communities (PLCs) (Dufour & Dufour, 2012; Dufour & Eaker, 1998; Dufour et al., 2008) during the COVID-19 pandemic. In a constantly changing and uncertain world, school leadership is acknowledged as being more complex and multi-faceted while also becoming more intensified, demanding, and diverse than ever before (Canadian Association of Principals (CAP), 2014; Pollock & Schleicher, 2015; Wang & Hauseman, 2015;). Leadership continues to evolve and become more multi-layered during a pandemic, requiring both face-to-face and remote learning options. Therefore, a leader’s responsive approach may differ based on the situational context. Educational research in instructional leadership (Hallinger, 2003, 2005; Robinson, 2011), shared leadership (Dewitt, 2017; Leithwood, 2012), and adaptive leadership (Bagwell, 2020; Dunn, 2020; Heifetz et al., 2009) have shown these to be effective leadership approaches. A PLC is an organizational path for leadership to facilitate the building of relational trust, and especially during complex, uncertain times, such as during a pandemic. To be an effective leader, trust becomes an essential factor within schools (Bryk & Schneider, 2002; Fink, 2015). Strengthening relational trust between teachers and the principal fosters conditions for members of a school community working together as well as social and academic progress for student learning (Bryk & Schneider, 2002; Tschannen-Moran & Hoy, 1998). The leadership implications for K-12 principals require adaptability and resilience to the ever-changing context while always maintaining ethical and moral standards. This article highlights the critical role in developing PLC collaborative opportunities to establish teacher connections based on relational trust to support student learning.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".