The Role of Goal Orientation and Self-Efficacy in Learning from Web-Based Worked Examples
Bibliographic record
Abstract
The goal of this study was to understand the roles of goal ori entation and self-efficacy when learning from worked exam ples. A Web-based learning environment, used as a compo nent of a traditional undergraduate chemistry course, served as the context for the study. Goal orientations were derived from Elliot and McGregor’s (2001) achievement goals frame work. Structural equation modeling was applied to measures of individual goal orientation, self-efficacy, use of online worked examples and achievement (N=176). Results indicate that a mastery-approach orientation was the strongest predic tor of achievement, but worked example use and self-effica cy were not related to either of the mastery orientations. For the performance orientations, worked example use was estab lished as an antecedent to self-efficacy and achievement. Results are discussed in terms of goal theory and its applica tion to the design of instructional materials.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".