The Development and Validation Prospective Mathematics Teachers Holistic Assessment Tools
Bibliographic record
Abstract
This study aims to explain the stages of developing a Holistic assessment instrument for the competence of prospective mathematics teachers based on constructs from several literature reviews to measure the competence/ability of prospective mathematics teachers.This development goes through 8 steps of developing non-test instruments.Validity and reliability of instrument traced from 101 students.The initial instrument design carries out initially, then validated with the Aiken formula by 14 experts.The series of initial stages obtained 30 instruments ready to be tested from the original 40 items.The second stage is to perform a confirmatory factor analysis (EFA) followed by testing the construct and convergent validity and looking for the reliability coefficient with confirmatory factor analysis (CFA).The results of the EFA produced 28 items become into four factors, namely pedagogic content knowledge, mathematical content knowledge, positive behavior and respect, teacher enthusiasm (passion).The results of the CFA indicate that the constructs built have construct validity in the good category, convergent validity fulfilled because all AVE values are more than the minimum limit (0.5).Internal reliability (Cronbach's Alpha) = 0.96, Composite Reliability (CR) is in the range 0.88-0.92,and Average Variance Extracted (AVE) is in the range 0.55-0.58.The results of the CFA produced 27 items.Based on the measurement, it can say that the instrument of Holistic Assessment of Prospective Mathematics Teachers is suitable for use at the research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".