Developing critical thinking and problem solving skills through skill-enhancing game / Nor Aishah Abdullah
Bibliographic record
Abstract
Reasoning is believed as one of the core characteristics of chess game. Player of the game tend to break down big problems to smaller pieces and then put them back together. Although various schemes and programs have been put into action in the U.S., Canada and some European countries, playing chess is merely regarded as hobby when it should be exploited for increasing child’s ability to think critically. This research is an experimental research in observing students’ development in critical thinking skills through chess playing. Participants were divided into two groups of student age 10 years old. The first group comprised of students with the ability to play the game and the second group consisted of students who have no prior knowledge of the rules or the strategies of such games. Both groups were subjected to pre-test and post-test involving solving problems in Mathematics, Science, and Critical Thinking test. After 10 weeks of chess intervention, data were analyzed using t-test to compare the mean differences between two groups, and Pearson’s test to see correlation between two variables; critical thinking and science, critical thinking and mathematics. Results have shown that chess has the potential as a good tool to develop students’ critical thinking skills, although different set of students demonstrate different result. Larger sample size and longer duration of experiment could demonstrate a better result in participants’ scores and this can take into consideration for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".