The Effect of Motivation and Self-Efficacy on Math Studies in the Israeli Ministry of Education's Program "Give Five"
Bibliographic record
Abstract
Studying mathematics is an essential condition for acquiring an education in most fields such as all exact sciences, financial sphere, programming, etc. It enables students to choose from among a large variety of professions with significantly high chances of academic admission, mainly in fields such as engineering, natural sciences, and technology, as well as in a considerable part of the social sciences Hence, studying mathematics in high school is a critical and key factor for continued studies and for integration in many professions in the Israeli workforce. The current study sought to expand knowledge on the effect of students' psychological feelings, such as motivation and self-efficacy, in light of the "Give Five" reform initiated by the Ministry of Education implemented in 2015. The study examined the effects of the "Give Five" reform on student motivation and self-efficacy, while examining whether these influences were gender-dependent. The study confirmed a positive correlation between the degree of motivation to study mathematics and the level of self-efficacy, and no difference was found between males and females in their level of motivation and self-efficacy. Future recommendations include research into the significance of motivation and self-efficacy as a major determinant of scholastic success.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".