Redefining Quality Assessment in Canadian K-12 Schools With Empathy, Critical Consciousness and Metacognition
Bibliographic record
Abstract
Over the past decades, educational pedagogies have been shaped by many theorists and frameworks. For the purpose of this paper, the researchers define educational pedagogy through a lens that is influenced by Paulo Freire. For instance, educational pedagogy can be defined as seeking a balance within the tension between freedom and authority, while navigating the challenging structures that shape educational institutions (Freire, 2005, 2020). These structures include the impact of lived experiences and realities, the role of curricula, and the changing interactions within society that influence the pedagogical frameworks in education (Freire, 2020). This paper begins to consider a new framework for assessment and evaluation in Ontario, Canada’s K-12 schools, where educational pedagogy, empathy, metacognition, and critical consciousness may be posited as potential components moving forward. This paper deeply examines these three key elements of effective and authentic assessment: empathy, critical consciousness and metacognition. Each of these terms is defined and situated in a 21C digital learning context, and the authors suggest that new directions in assessment and evaluation must consider these three significant as fundamental to any redesign of assessment practices. Consideration may be given to the perspectives and experiences of educational leaders, educators, students, and their communities, and how these perspectives can shape the values and how we meet the challenges in education. Pedagogy in practice can often reflect the strategies and beliefs that influence educators’ values and choices, and the resulting impact on learning. Part of these pedagogical considerations may include the development of empathy, critical consciousness and metacognition throughout the assessment and evaluation process. Using a Canadian context, the authors argue that post-pandemic learners require a different set of tools to learn, and post-pandemic teachers need to reconfigure the ways in which that learning is measured, assessed and evaluated. , ,,,
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".