Construct Validity of the Two-Factor Revised Learning Process Questionnaire in a Singapore High School
Bibliographic record
Abstract
The Revised Two-factor Learning Process Questionnaire (R-LPQ-2F) is an instrument for assessing students’ learning approaches at the high school level. The instrument has significant potential for use in Singapore schools, but as yet, has not been validated in this context. This study evaluated the validity attributes of the R-LPQ-2F in a sample of Singapore senior high school students. The sample comprised 455 Year 11 students (266 male, 189 female) from Singapore. The internal structure of the R-LPQ-2F was evaluated by replicating the confirmatory factor analyses published in previous validations of the instrument, and assessing its internal consistencies and inter-scale correlations. Relationships between the R-LPQ-2F subscales and external variables were also evaluated. Results indicated that for the Deep Approach scale, a one-factor model fit the data well. For the Surface Approach scale, a four-factor model (Fear of Failure; Aim for Qualification; Minimizing Scope of Study; and Memorization) was found to fit the data best. Correlations between scores on the R-LPQ-2F subscales, on the Motivated Strategies for Learning Questionnaire, and a physics achievement test demonstrated expected patterns of correlation. Overall, results obtained in this study supported the construct validity of the R-LPQ-2F for use with Singapore high school students.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".