Principals and the professional learning community: learning to mobilize knowledge
Bibliographic record
Abstract
Purpose – The purpose of this paper is to use the Objective Knowledge Growth Framework (OKGF) in the development and maintenance of the Canadian Principal Learning Network (CPLN) to advance principals’ knowledge and skills in the area of decision making. First, the paper presents the inception of the CPLN, to assist principals in making decisions and resolving common problems. Second, the evolution of the CPLN web site is presented and recount the challenges faced and collaborative solved by principals. Finally, the paper describes the OKGF based on the critical rationalism of Karl Popper and how principals, engaging and interacting in an online learning community (CPLN) informed their decision-making process. Design/methodology/approach – The paper mindfully assembled an international team of researchers with administration experience, curriculum knowledge and pedagogy, and whose research interests lay in educational leadership, education administration, change theory, educational policy and professional learning. Also in addition, principals who were current graduate students, and new researchers also joined the research team. Findings – The CPLN web site using OKGF, is a step forward in providing principals with a structure and a venue to be reflective and collaborative. However, getting them to interact with each other in a collaborative, reflective online learning community was not an easy feat at the beginning (Lieberman and Miller, 2008; Louis and Kruse, 1995; Schmoker, 2006; Wagner and Kegan, 2006; Bryket al., 2010; McLaughlin and Talbert, 2002; Stoll and Louis, 2007). The study shows principals find the need for a place to reflect, discuss, experiment, practice and learn and, for this group of principals, that place is the CPLN. Originality/value – This study provides a model for principals’ learning in an online learning community using the OKGF. As well, it shows that powerful leadership does not just take place during preparation programmes, but that principals need to continue to learn as they lead in their respective schools (Mitgang and Maeroff, 2008). Sharing of the challenges faced and the learning that occurred principals are capable of addressing not only the challenges posed in their schools but also, as numerous researchers note (Fryet al., 2006; Levine, 2005; Mitgang and Maeroff, 2008), of surviving the job themselves.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".