Students’ Achievement in Math and Science: How Grit and Attitudes Influence?
Bibliographic record
Abstract
Many recent studies in the field of mathematics and science education have been studying the effect of non-cognitive factors in students’ achievement such as emotions, attitudes, values, beliefs, motivation, anxiety and grit. For example, attitude has been an important area in science education, and there have been many attempts to measure students attitudes to understand why they prefer a specific science subject (Reid; 2006). Zimmerman and Brogan (2015) stated that ‘grit predicts successful performance in a variety of contexts and found to be positively correlated with undergraduate grade average.’ Unfortunately, there are very few attempts if any have been studying the effect of grit on students’ academic achievement in Bahrain. Bahrain is an important economic sector in the Arabic Gulf region; it has very ambitious and competitive developing economical and educational vision. This study aims to find relationships between students’ level of grit and attitudes toward mathematics and science and the academic achievements in Bahrain secondary schools. ‘Grit questionnaire’ was adapted from Duckworth et al. (2007), and was administered to a total of 646 secondary school students. ‘Attitudes toward mathematics’ questionnaire was adapted from TIMSS (2011), and administered to a total of 349 secondary school students. ‘Attitudes toward science’ questionnaire was adapted from TIMSS (2011), and administered to a total of 297 secondary school students. The results showed that grit is positively and significantly correlated to academic achievement in math only, while attitudes towards math and science was positively and significantly correlated to academic achievement in both subjects.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".