The multiplicative function of expectancy and value in predicting engineering students' choice, persistence, and performance
Bibliographic record
Abstract
Abstract Background Students are more likely to persist when they both perceive themselves as capable of success (expectancy) and perceive tasks to be interesting, important, and useful (values) or less costly in terms of effort, lost opportunities, and psychological stress (perceived costs). Prior research has not examined whether these motivational beliefs synergistically predict engineering‐related outcomes; studying such synergy is critical for understanding how multiple forms of motivation combine to support engineering persistence. Purpose/Hypothesis We tested how engineering academic self‐efficacy (expectancy), values/costs, and their interaction predicted engineering‐related outcomes. We hypothesized that there would be significant interactions between self‐efficacy and values/costs in predicting engineering persistence and academic success. Design/Method Structural equation modeling was used to investigate latent interactions between self‐efficacy and values/costs (interest, attainment, and utility values; opportunity, effort, and psychological costs) in predicting career intentions, aspirations for engineering graduate school, and engineering retention, and grades in foundational courses for engineering among first‐year engineering undergraduates ( n = 2420). Results Significant interactions between self‐efficacy and values (interest and utility only) were identified, but not for self‐efficacy and attainment value or costs. Feeling both competent in engineering and highly valuing engineering were simultaneously related to higher engineering persistence, as compared to either feeling competent or valuing engineering alone. Conclusions The findings contribute to expectancy–value theory by providing a more precise understanding of the role of each type of value and cost in predicting distal outcomes, and practicing by highlighting the importance of supporting both expectancy and values when intervening to support engineering persistence.
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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 | 0.014 |
| 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) | 0.004 | 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".