Confirmation and Structured Inquiry Teaching: Does It Improve Students’ Achievement Motivations in School Science?
Bibliographic record
Abstract
Abstract Guided and open inquiry stands as a valuable instructional strategy for science education. Yet, confirmation and structured inquiry, which provides higher levels of teacher guidance, is more often enacted. These approaches, though more workable, remain unexplored in their effectiveness in improving achievement motivations. This study draws on expectancy-value theory to explore the effect of short-term confirmation and structured inquiry on students’ expectancies of success and intrinsic values in school science when compared to traditional lecture-based strategies. One hundred and nineteen Spanish sixth graders were assigned to three pedagogical conditions using classroom clusters: lecture (control group), confirmation inquiry, and structured inquiry. The intervention consisted of two units of three hours each. Findings revealed no statistically significant differences between pedagogical conditions. Overall, this study failed to find evidence of a difference in students’ expectancies of success and intrinsic value of school science when lecture, confirmation, or structured inquiry teaching strategies were used.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".