Exploring Items for Measuring Self-initiated Professional Development Construct in The Context of Continuous Professional Development System Usage
Bibliographic record
Abstract
Self-initiated professional development (SI-PD) refers to the proactive engagement of teachers in activities aimed at enhancing their own intellectual capabilities, accumulating practical knowledge and cultivating positive dispositions. This involves the deliberate design, implementation, and evaluation of the learning process, utilising educational resources as a means of support or facilitation. SI-PD is evaluated to determine its effect on the usage of the Continuous Professional Development (CPD) system by Malaysian teachers. There are twenty-six items on the SI-PD, which were adapted from prior studies that employed self-directed learning. The items in this self-directed learning construct have been modified to reflect the context of the CPD system usage. The questionnaire items were then reviewed and verified by the experts from the aspects of content validity, face validity and criterion validity. A pilot study was conducted on 100 teachers who were randomly selected from the central region of Malaysia, which includes three states: Selangor, the Federal Territory of Kuala Lumpur, and the Federal Territory of Putrajaya. Findings of Exploratory Factor Analysis (EFA) yielded five components (motivation, self-monitoring, self-efficacy, self-management, and self-regulation), and internal reliability was achieved for all five components.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".