Testing Competing Structural Models of Approaches to Learning in a Sample of Undergraduate Students: A Confirmatory Factor Analysis
Bibliographic record
Abstract
Current research into studying orientations indicate that students are directed by motives and strategies that reflect both surface (reproduction of contextual material) and deep approaches (comprehensive understanding) to learning. Both are influenced by a latent factor emphasizing a students desire to achieve academically. In this study, the application of Biggs' Study Process Questionnaire (SPQ) is used to test competing models of students' approaches to learning in a sample of 504 undergraduate university students at a major Canadian university, 36o junior – 1st year and 2nd,(year (71.4%) and 144 senior – 3rd year and 4th year (28.6%). In addition to an internal consistency and test-retest reliability analysis of the SPQ, a confirmatory factor analysis was utilized to evaluate the goodness-of-fit of three competing models and an alternative structural model of the motives and strategies of students' approaches to learning. The results provided support for a three-factor model of approaches to learning (Comparative Fit Index = .974). The achieving motive and strategy subscales were significant indicators of both surface and deep factors. The internal consistency alpha coefficient was .82, and 3-month test-retest reliabilities for the subscales ranged from .54 to .73. The results support a three-factor model of approaches to learning where surface and deep approaches are accompanied by a third latent factor reflecting student's approach to academic achievement. An understanding of students' approaches to learning enhances the process of learning and teaching by providing practitioners with an insight into the intrinsic and extrinsic motives for learning at school.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".