The Effectiveness of a Learning Community in Bringing About Changes to Instructional Practices in the Area of Assessment for Learning
Bibliographic record
Abstract
Professional learning communities and assessment for learning have been proven to have significant impact on student learning. However, what still remains unclear is why these practices are not resulting in improved student learning the way research has indicated. The intent of this descriptive case study was to determine what schools and school divisions were doing to aide teachers in embracing these two practices. This study examined the following question, In what ways does a professional learning community support and enable teachers to implement assessment for learning within their daily practice?, within the context of the two sites that were being studied. Four educators from two different schools in the provinces of Alberta and Saskatchewan took part in the research. From this study seven findings were revealed. The findings include: • time was not provided to collaboratively discuss student learning; • discussions about students focused on student behavior as opposed to student learning and teacher actions; • support and follow through was minimal at best; • participants could not identify the essential work of a PLC or key elements of assessment for learning. The research utilized triangulation of data through participant observations, in-depth interviews and physical artefacts. The following conclusions were attained using a Findings, Interpretations and Conclusions Matrix: • teachers were not able to change their philosophical beliefs because the PLC framework was not present; • collaborative time must be protected like instructional time; • teachers must be held accountable for their discussions and actions around student learning; • participants were not utilizing observations, conversations and products to gain a more accurate account of student learning; and • leadership is critical if the PLC framework and assessment for learning is to have any impact on teacher practices.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".