BENEFITS of CHESS for ACADEMIC PERFOMANCE and CREATIVE THINKING
Bibliographic record
Abstract
www.vivacityinc.com/chess Chess is widely believed to increase “mental muscle”. The academic benefits of the game appear to be extensive. There are a number of studies, which support the contention that exposure to chess enhances memory, boosts spatial and numerical skills, increases problem-solving capabilities, and strengthens logical thinking. [2] Many schools all over the world encourage chess play to enhance academic performance. [10] Studying chess systematically has also shown to raise students ’ IQ and exam scores (Dullea 1982; Palm 1990; Ferguson 2000), as well as strengthen mathematical, language, and reading skills (Margulies 1991; Liptrap 1998; Ferguson 2000). Chess is a fun way to teach children how to think and solve an ever-changing and diverse array of difficult problems. [3] More and more schools around the world are recognizing the value of chess, with instruction now becoming part of standard curriculums. [3] Chess around the globe A 1973 –74 study in Zaire by Dr. Albert Frank found that good teenage chess players had strong spatial, numerical, administrative directional, and paperwork abilities. [4] Dr. Robert Ferguson notes that “this findings tends to show that ability in chess is not due to the presence in an individual of only one or two abilities but that a large number of aptitudes all work together in chess. ” Dr. Frank’s study found that learning chess strengthened both numerical and verbal aptitudes. This occurred for the majority of students (not just the strong players) who took a chess course for two hours each week for one school year. Other studies have added that playing chess can strengthen a child’s memory (Artise). [3] A 1990 – 92 study in New Brunswick, Canada, further shows the value of chess for developing problem solving skills among young children (Gaudreau 1992). Using chess in grades 2 to 7 as part of the mathematics curriculum demonstrated that the average problem solving score of pupils in the province increased from 62 % to 81%. [9]
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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 | 0.000 | 0.000 |
| 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 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".