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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".