Comparison of Conventional to the <i>Vedic</i> Mathematics System: Through Statistical Analysis of Pre and Post Test Result
Bibliographic record
Abstract
Any business, like mining, where observation and procedure call for a lot of fast calculations, must prioritize speed and accuracy. India has a wealth of minerals, and government agencies such as NMDC actively participate in the process of extracting minerals from ore. However, our methods must be tweaked. Quick calculations are necessary for field work. Compared to the standard approach, the vedic mathematics system is incredibly rapid and easy to use. The goal of the work was to compare the vedic mathematics system to the conventional system, through the performance of group of students in terms of accuracy and speed in a pre-test and post-test on basic mathematics multiplication. Mathematics test was conducted before and after workshop on vedic mathematics, for 25 students from the Department of Mathematics of first year from Satya Sai Women College Bhopal. The outcomes demonstrated that the students result in the test after workshop was remarkably better than in the test before workshop in terms of speed and accuracy in spite of the fact that the vedic methods were newly introduced to them and conventional methods were known to them from long time. Statistical analysis was done which shows that vedic math’s increases speed and accuracy by significant difference and students were happy to use vedic math’s methods.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".