An Examination of Personal Financial Literacy Teaching and Learning in Ontario High Schools
Bibliographic record
Abstract
The purpose of the study was to examine the experiences of current high school students, teachers, and high school graduates in Ontario regarding their experiences with personal financial curriculum and teaching at the secondary level, and to identify the ways in which this important educational experience may have helped prepare students to become financially literate. I sought to examine how helpful and productive financial literacy teaching and curriculum were at preparing students to confidently address personal financial issues they would inevitably face. I considered this overarching issue using a transdisciplinary lens from the perspective of the three stakeholder groups. Using a case study research design, the first of its kind in Ontario or Canada on this topic, I utilized interviews, artifacts, and surveys to uncover student and teacher experiential data across three high schools and two institutions of higher learning in southeastern Ontario. For the interviews and artifacts, there were 12 students and 12 teachers from three high schools. For the high school graduate surveys, there were 344 participants, drawn from one college and one university. Inductive thematic analysis was used for the interviews and artifacts; descriptive and inferential statistical analyses were used for the surveys, with some significance testing. The major findings were that current students and high school graduates perceived curriculum and teaching experiences as seriously lacking in effectively preparing them to be financially literate, and that a fundamental reorientation around transdisciplinary, student-led learning was key to transforming such learning into a more meaningful and valuable educational experience. The central finding from the teacher perspective was that such instruction should be mandated, properly supported and largely student-led; reaffirming the view that greater student learning, meaning and value were achievable with suitable enactment. Accordingly, the three stakeholder groups shared the view that curriculum and teaching in the area of personal financial literacy was largely absent and/or ineffective at meeting student interests and needs. With mandatory, properly supported, student-oriented (transdisciplinary) instruction, the potential exists for more effective and valuable learning, resulting in better equipped high school students that were properly prepared to successfully navigate financial issues and the path ahead.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".