The effect of distal learning, outcome, and proximal goals on a moderately complex task
Bibliographic record
Abstract
Abstract The effects of learning versus outcome distal goals in conjunction with proximal goals were investigated in a laboratory setting using a class‐scheduling task. The participants ( n = 96) needed to acquire knowledge in order to perform the task correctly. A ‘do your best’ outcome goal led to higher performance than the assignment of a specific, difficult outcome goal. However, the assignment of a specific, difficult learning goal led to higher performance than urging people to ‘do their best.’ Goal commitment was higher in the learning goal than in the outcome goal condition. The correlation between task‐relevant strategies discovered and performance was positive and significant. The number of task‐relevant strategies implemented by participants assigned a distal learning goal in conjunction with proximal goals was higher than in any other goal condition. Setting a distal outcome or learning goal that included proximal outcome goals, however, did not lead to higher performance than the setting of a distal outcome or learning goal alone. Self‐efficacy correlated significantly with performance, and this effect was mediated through strategy development. Furthermore, the discovery of task‐relevant strategies affected self‐efficacy through an increase in performance. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".