Math Modules Training Improves Math Achievement & Associated Cognitive Processing
Bibliographic record
Abstract
Background: Previous research point to a correlation between mathematical skills and cognitive processes involved in planning and simultaneous processing. Consistent with multicomponent models of mathematical achievement (domain-general and domain-specific skills), PASS theory appears to be very useful as a multifactorial framework that provides specific tests to monitor the development of mathematical competence and to direct intervention procedures and improve mathematical skills. Objective: This study was conducted to assess the impact of the Math Modules Cognitive Training Program on the mathematical competence of typical 2nd-grade students in calculation, problem-solving, and underlying mental functions, compared to a control group. The program was designed to optimize the Planning/FE, Attention, Simultaneous, and Successive cognitive processes through a series of tasks. Participants: The study involved 60 students aged between 6 and 8 years (Mdn = 7 years and 7 months), who were in the second grade of two urban public schools. Method: The program focused on mathematical skill tasks related to fluent calculation and mathematical problem solving that requires PASS cognitive processes for successful completion. The intervention group received the Math Modules program, and the control group followed their usual classroom program. Students were evaluated in calculation, problem-solving, and PASS cognitive processes. Results: Our results showed that the Math Modules Cognitive Training Program focused on calculation and problem solving skills were effective in improving children’s mathematical performance and their PASS cognitive processes, generating gains not achieved by the control group. Conclusions: Our study suggests that fluid calculation and problem-solving math tasks, based on planning and simultaneous processing, could foster curricular math competency.
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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 | 0.001 | 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 | 0.002 |
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".