Constructing Progress Maps of Digital Technology for Diagnosing Mathematical Proficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to construct and validate progress maps of digital technology for diagnosing the multidimensional mathematical proficiency (MP) in Number and Algebra for Grade 7 students utilizing the Construct Modeling Approach. Researchers employed four building blocks as follows. Firstly, researchers developed the progress maps as an assessment framework of multidimensional MP. This is followed by creating the test for diagnosing MP. Next, researchers assigned scoring criteria and created the transition points of students’ MP levels. Finally, researchers validated the quality of the progress maps through empirical evidence. A total sample 1,500 Grade 7 students was used to support the validity and reliability evidence of the progress maps through the Wright Map using Multidimensional Random Coefficients Multinomial Logit Model. Results revealed that there were two dimensions of progress maps, namely mathematical procedures (MAP) and structure of learning outcome (SLO), and the researchers investigated three strands of validity evidence, namely test content, response processes, and internal structure. The reliability values in the MAP and SLO were 0.84 and 0.80 respectively. Finally, the Grade 7 students were mainly found to be at level-2 in the MAP dimension (44.95%) and the SLO dimension (61.57%). The experts’ evaluation results showed that the digital technology that was developed at the “most appropriate” quality levels in terms of usefulness, suitability, and accuracy, and at the “very appropriate” for the feasibility aspect, and hence is successfully contributing to the clarification of learning goals, to support for student-centered instruction, and that it is helpful in improving in teacher professional development.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it