Predicting Academic Performance with an Assessment of Students’ Knowledge of the Benefits of High-Level and Low-Level Construal
Bibliographic record
Abstract
Metamotivation research suggests that people understand the benefits of engaging in high-level versus low-level construal (i.e., orienting toward the abstract, essential versus concrete, idiosyncratic features of events) in goal-directed behavior. The current research examines the psychometric properties of one assessment of this knowledge and tests whether it predicts consequential outcomes (academic performance). Exploratory and confirmatory factor analyses revealed a two-factor structure, whereby knowledge of the benefits of high-level construal (i.e., high-level knowledge) and low-level construal (i.e., low-level knowledge) were distinct constructs. Participants on average evidenced beliefs about the normative benefits of high-level and low-level knowledge that accord with published research. Critically, individual differences in high-level and low-level knowledge independently predicted grades, controlling for traditional correlates of grades. These findings suggest metamotivational knowledge may be a key antecedent to goal success and lead to novel diagnostic assessments and interventions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".