PSCP Cognitive Engagement Scale: A Scale Development Study
Bibliographic record
Abstract
This study aimed to develop and validate an instrument to measure students' cognitive engagement in teaching-learning environments. An exploratory correlational method was employed to develop the scale. 446 university pre‐degree students learning English as a foreign language participated in the study. A pilot study was conducted with 117 students to explore the item and factor structure of the scale, resulting in the removal of eight items from the scale. A subsequent study with 329 students was conducted to confirm the scale's item and factor structure. Results showed that the scale demonstrated content validity, with a content validity index of .94. The scale consisted of nine items and two factors, identified as cognitive attention and cognitive effort. Convergent validity was established, as evidenced by composite reliability values of .83 and .84 for each factor, with average variance extracted of .55 and .51, respectively. Corrected item-total correlation values ranged from .54 to .71, and inter-item correlation exceeded .30. Reliability analysis revealed high internal consistency, with each factor demonstrating reliability of .83 and .85, resulting in an overall scale reliability of .89. In conclusion, the findings indicate that the developed PSCP Cognitive Engagement Scale is a valid and reliable scale for measuring cognitive engagement in learning environments.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".