Trajectory of Learning Experience from the Performance of Canada's Youth in Mathematics
Bibliographic record
Abstract
This study empirically evaluated the learning experience of Canadian youth in mathematics, based on existing theoretical tenets, to explore a conceptual model that depicts two major orientations: one is the internal aspects, which focus on how the students feel; and another is external aspects, which focus on how the students behave. An imperative question underpinning this research involves examining different untested trajectories of learning experience in Mathematics that could elucidate and predict individuals’ mastery-approach goals. The data from the Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) 2012 surveys comprises participants who are 15-year-old Canadian students from different provinces, investigating how the factors of learning approaches, learning environments, and participants’ familiarity affect their development of skills and learning attitudes, both at home and at school in mathematics. Path analytical procedures are applied to test the hypothesized relationships, and certain notable findings would impact mediating mechanisms while remarkable trajectories would inform complete pedagogical practices. Particularly, this research is significant for its un-exploratory nature. The Path Analysis offers visions of the relationships between the internal and external aspects, and the relationships between the elements in those same internal and external aspects. The contribution of this study is that the results/findings could support policy targets by establishing measurable goals for consideration and implementation, assist in the building of trajectories for reform, as well as notify all stakeholders such as educators, researchers, parents, governments, and policy makers about the importance of understanding individuals’ academic and learning patterns, as the researcher contends.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".