Teachers’ Perceptions of Students’ Postsecondary Education Aspiration Capabilities
Bibliographic record
Abstract
This study examines how teachers perceive students’ postsecondary education (PSE) aspirations and whether teachers’ perceptions differ depending on the income levels and PSE application rates of students in secondary schools. The study contributes to research about students’ PSE aspirations by analyzing the kinds of perceptions teachers hold of students’ PSE aspirations and why they might support some aspirations over others. The premise was that teachers’ perceptions could be a limiting factor on students’ aspirational capabilities if teachers perceived students as not having the aptitude to be successful, and that this might further be correlated with students’ family incomes. The research used a mixed methods convergent research design: Quantitative data consisted of teachers’ responses (n =113) to a survey about their perceptions of students’ PSE aspirations, and qualitative data were generated from semi-structured interviews (n =12) with teachers. Participants were drawn from three schools of varying income and PSE participation levels from within the same public-school board in southern Ontario. The findings were interpreted using the Sen-Bourdieu Analytical Framework as proposed by Hart (2019), combining both Sen’s capability approach and Bourdieu’s theory of social reproduction. The quantitative data revealed that teachers’ perceptions of students’ aspirations and how teachers prepared students for PSE did not significantly differ across schools. The qualitative data, however, showed that teachers evaluated students’ capacity to aspire on the basis of students’ capability sets, as perceived by teachers, and that teachers’ pedagogy and PSE praxis differed depending on the income level of the school in which they taught. Further analysis revealed that teachers were frustrated with policies that limited their ability to effectively support and encourage students’ PSE aspirations. A conclusion drawn from this study was that, to increase students’ capacity to aspire, teachers must adjust their pedagogy and praxis to address the distinct needs of students with different levels of access to forms of capital and different family habitus; however, secondary and PSE leaders must also collaborate with teachers when developing policies and protocols that enhance students’ aspirational capabilities and functionings so all students, regardless of income level, have equitable opportunities to aspire to a PSE.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".