Pre-service and In-services Teachers Perspectives on Academic Success: More than Just A Grade
Bibliographic record
Abstract
Students are constantly bombarded with messages about academic success and the importance of getting good grades. However, definitions of academic success are more complex than a letter grade. Many indicators to define academic success extend primarily from students’ perspectives and ignore how teachers’ definitions of success. This is an oversight as teachers’ perspectives on academic success shape their students’ perspectives on academic success for years to come, and thus represent an important voice to be included in the messaging around academic success. Thus, in this study we were interested in pre-service and in-service teachers’ definitions of academic success, and how they converge or diverge with indicators outlined in current research. We found that teachers have multiple perspectives on academic success, highlighting the complexity of this construct. Moreover, many of their definitions converged with researchers; however, teachers’ definitions were more varied and diverse. Our findings highlight the multidimensional nature of academic success. In closing, we identify various implications for schools and provide suggestions for future research and practice.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".