Use of Digital Learning Platform in Diagnosing Seventh Grade Students’ Mathematical Ability Levels
Bibliographic record
Abstract
This paper aims to describe the design and inspection of the quality of a digital learning platform to diagnose mathematical ability levels of seventh-grade students with regard to the topics of Measurement and Geometry. A total of 517 seventh-grade students from 23 schools in four regions, namely north, northeast, central, and south of Thailand were randomly chosen took part as test-takers. The researchers employed a design-based research approach incorporating three stages starting from designing a digital learning platform as an assessment model to diagnose students’ mathematical ability levels, up to employing a Multidimensional Random Coefficient Multinomial Logit Model to inspect the quality of the seventh-grade students’ mathematical abilities assessment model. The research tool consisted of three subjective questions and 15 multiple-choice questions which were used to develop two five-level construct maps ranging from unresponsive to tactical intelligent for the mathematical procedure dimension, and prolonged intellectual structure for the conceptual structural dimension. The findings verified that there were three forms of evidence to support the quality of the mathematical abilities assessment model. The results of the intersection of a mathematical procedure and conceptual structural dimensions’ transition point from levels 1 to 5 as ranging from the lowest to the highest levels at -1.41, -0.69, 0.49, 1.34 and -0.98, 0.14, 0.44, 1.70 respectively. Finally, the empirical findings indicated that the digital learning platform was at a highly appropriate level in terms of its usefulness, suitability, and accuracy except for in terms of feasibility which was at a moderately appropriate level. In conclusion, the digital learning platform can be used to provide substantial information when it comes to diagnosing seventh-grade students’ mathematical ability levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".