Factors affecting academic achievement as perceived by secondary school students with Iranian background in Canada
Bibliographic record
Abstract
Despite continuing interest in academic achievement theory and approaches on culturally diverse students, explanations remain inadequate and incomplete. In Canada much of the focus has been on particular groups, particularly those of First Nations ancestry. Much of the research and theory also fails to address successful students and students other than those from a low socioeconomic status. In addition it lacks the perceptions and interpretations of those who may have the most valuable contributions to make---those being the students themselves.;This study combines elements of both micro- and macro-ethnography, as well as other types of qualitative research, in the examination of the perceptions of students with Iranian backgrounds attending a secondary school in one of the wealthiest areas in Canada. The student participants identified five primary factors as most important to their academic successes and difficulties: (1) language, (2) family, (3) peers, (4) school, and (5) racism. Findings are presented primarily in the words of the students. They are analyzed contextually and in conjunction with data gathered from the students' parents and educators at their school. Findings confirm the incompleteness and inadequacies of theories on and/or approaches to the academic achievement of culturally diverse students when applied to Canadian students with Iranian background and suggest directions for further attention. Results may be useful in the fight against stereotypical thinking and racism. They may also aid in a better understanding of the complex connection between ethnicity and school achievement. This understanding is necessary for educators to be able to help guide their culturally diverse students to academic success. Recommendations for doing so are included in the last chapter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".