School Engagement Trajectories and Their Differential Predictive Relations to Dropout
Bibliographic record
Abstract
Although most theories draw upon the construct of school engagement in their conceptualization of the dropout process, research addressing its hypothesized prospective relation with dropout remains scarce and does not account for the academic and social heterogeneity of students who leave school prematurely. This study explores the reality of different life‐course pathways of school engagement and their predictive relations to dropout. Using an accelerated longitudinal design, we used growth mixture modeling to generate seven distinct trajectories of school engagement with 12‐ to 16‐year‐old students (N = 13,300). A vast majority of students were classified into three stable trajectories, distinguishing themselves at moderate to very high levels of school engagement. We refer to these as developmentally normative pathways in light of their frequent occurrence and stability. Although regrouping only one‐tenth of participants, four other nonnormative (or unexpected pathways) accounted for the vast majority of dropouts. Dropout risk was closely linked with unstable pathways of school engagement. We conclude by debating the delicate investment balance between universal strategies and more selective and differentiated strategies to prevent dropout. We also discuss the need to better understand why, within normative trajectories, some students with high levels of school engagement drop out of school .
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".