Promoting Mathematical Competence and Confidence in Latina Preservice Teachers: Examining Home and School Experiences.
Bibliographic record
Abstract
McCoy, 1997, p. 71), performance gaps in mathematics are found between students of color and their White peers. For instance, in California, although 58% of seventh-grade students’ performance levels were proficient or advanced, 32% of Latinos performed at proficient or advanced levels, and just one quarter (26%) of Black seventh graders achieved proficiency (CDE, 2009). National data indicate similar disparities. Although the 2008 NAEP data show a slight narrowing of the achievement gap since 1973 for nineyear-olds, there is still a 16-point gap in achievement between Hispanic students and White students and a 26-point gap between Black students and White students (U.S. Department of Education, 2008b). By the time students hit 17 years of age, the Hispanic/White gap increases to 21 points, and the Black/White gap to 27 points. Given the fact that aptitude across races and ethnicity is equal, how do we account for such differences? Many factors appear to contribute to the differences in academic performance associated with race and ethnicity. Some researchers focus on students, what they bring to school, and how that influences achievement. For example, some researchers (Cocking & Chipman, 1988; Cornelius-White, Garza, & Hoey, 2004) emphasize that factors affecting Latino achievement include motivation, entry level knowledge, family support, opportunities to learn, and attitude. Self-efficacy, or the belief that one can work toward a set of goals to achieve success, is also related to academic achievement. Despite the fact that Hispanics come to school with aptitude equal to that of their peers, Hispanics tend to have lower self-efficacy in mathematics (Stevens, Olivarez, & Hamman, 2006). And self-efficacy tends to function differently for different This article presents the activities and results of an American Association of University Women (AAUW) Campus Action Project designed to promote Latinas’ mathematical confidence and competence in teaching elementary mathematics. The project explored how school and family experiences contribute to Latinas’ sense of self efficacy as teachers of mathematics in elementary schools. Through analysis of their personal histories as they intersect with mathematics, and the patterns found among those histories, Latinas pursuing careers in education explored issues of confidence as they relate to both teaching and learning mathematics. The goal of the project was to ensure confidence and self efficacy among Latinas in order to enable them to encourage powerful mathematics learning and confidence in their own students. Participants’ experiences suggest improvements and inclusion for K-12 and teacher education as well as ways to promote and connect learning to at-home experiences.
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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 | 0.001 |
| 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 | 0.001 |
| 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".