The correlation between participating extramural programming courses and children's intelligence development
Bibliographic record
Abstract
Recently, artificial intelligence has been developed at an surprising speed and used in different aspects in our society. In fact, robot programming courses have become a hot topic being discussed in the education sector. Since 2014, computer education reform in primary and secondary schools worldwide has advanced programming courses to the first grade of primary school or even pre-school stage[1]. Therefore, an increasing number of parents enroll their children to robot programming training courses. The father of children's programming education Mitchel Resnick [2] believe programming is a kind of education that allows children to creatively address practical problems by thinking in a way that how programs run and this goal has become the recruitment and promotion slogans used by various programming training institution. However, whether extramural programming courses can actually achieve the goal claimed or they are just a so-called 'stupid tax' paid by parents remains to be a widely discussed and controversial topic. Based on Wechsler Intelligence scale of children fourth edition measurement scale [3], our research conducts an intelligence test for 50 children, with an average age of 10 and who are attending programming courses, to find out whether participating extramural programming courses can indeed enhance children's problem-solving skills and calculation capacity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".