Students’ Achievement in Math and Science: How Grit and Attitudes Influence?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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 it