Creating a Competency Continuum: Assessing mathematical literacy in secondary education
Bibliographic record
Abstract
Following educational trends in jurisdictions around the world, in 2015 the Ministry of Education (MOE) in British Columbia (BC), Canada introduced a redesigned K-12 curriculum which moved away from content-focussed educational objectives towards competency-driven learning outcomes. One of the significant challenges of this curriculum change is the absence of a framework that supports educators in effectively assessing the type of learner competency development used by the MOE as part of their graduation framework at the secondary level. In this project, we responded to this challenge by developing and testing a novel assessment instrument for use in a competency-based learning environment in BC. This instrument incorporates fuzzy logic principles to assess learning artifacts in the context of mathematical literacy as defined by the Organization for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA). In partnership with the Pacific School of Innovation and Inquiry (PSII), an independent, inquiry-based secondary school located in Victoria, BC, we introduced this assessment instrument, and then, over the course of two months in 2018, collected data concerning its use in assessment of mathematical literacy in their highly personalized and interdisciplinary learning environment. We present the initial findings from the study, and iterations on the assessment tool which further address challenges of implementing competency-based assessment (CBA) in BC K-12 classrooms and beyond.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".