Development of Ability in Construction of Mathematical Skill and Process Instruments for Students in the Faculty of Education
Bibliographic record
Abstract
The measurement instruments are part of assessment the teacher uses in assessing the learner’s learning process and outcomes. So, it is very necessary to prepare teacher students for their future as teachers. The objectives of this research were 1) to develop guidelines on construction of mathematical skill and process instruments, 2) to develop ability of construction of mathematical skill and process instruments of teacher students to meet the 70 percent criterion. The target group of the focus group discussion consisted of 4 experts in mathematics study. The experimental group consisted of 48 4th year students in the Faculty of Education, Mahasarakham University, obtained through cluster random sampling. The instruments used in the research were focus group issues, guidelines on construction of instruments, and an assessment form for ability of construction of mathematical skill and process instruments. The analysis of data employed content analysis and one sample t-test analysis. The research results were as follows: 1) The guidelines on construction of mathematical skill and process instruments emphasized enabling students to design instruments by themselves, by answering the following questions: “What is measured?” (What), “Why is it measured?” (Why), “When is it measured?” (When), “Who measures it?” (Who), and “How is it measured?” (How). The instruments constructed were various. 2) The ability of construction of mathematical skill and process instruments of the teacher students met the 70 percent criterion, with statistical significance at the .05 level (t=7.06, df=47). The research results, apart from being able to be used with teacher students, can also be used with in-service teachers.
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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 | 0.022 | 0.070 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".