Understanding the Nature and Correlates of Students’ Heterogeneous Perceptions of Opportunities for Deeper Learning
Bibliographic record
Abstract
Opportunities in school provide important building blocks for students to develop three essential competencies for academic success: confidence, value for academic tasks, and self-regulatory behaviors. However, students’ varying perceptions of such opportunities have received little attention in extant research. Using data from the Study of Deeper Learning, this study focused on students’ perceptions of opportunities that align with recommendations from robust psychological theory and research: real-world connections, personalized supports, and academic press. After examining the factor structure of student perceptions, latent profile analyses revealed five groups of students differentiated by distinct patterns (profiles) of perceptions. Gender predicted profile membership and profiles showed significant differences on both distal and proximal success indicators. Results underscore the importance of perceived deeper learning opportunities for students’ thriving, suggest directions for developing school supports, and contribute much-needed insights about the structure, common patterns, and correlates of heterogeneous perceptions of opportunities for deeper learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".