Influence of Demographic Factors on Students’ Beliefs in Learning Mathematics
Bibliographic record
Abstract
Learning mathematics has been recognized by many as important. It does not only develop students’ ability to think in quantitative terms but can also enhance skills such as analytical and problem solving skills. However, to enable us to tell our students how important mathematics is we have to understand students’ beliefs in learning mathematics so as to find ways to improve students’ performance in mathematics. The aim of this study is to examine the relationship between business students’ beliefs in learning mathematics and demographic factors. Data were collected from three hundred and seventy six students in three higher learning institutions enrolled in business mathematics class. Descriptive statistics will be used to describe the sample and Pearson chi-square test will be used to test students’ beliefs and the relationship between students’ beliefs and demographic factors (gender, institutions, previous mathematics grade, secondary education and major). Our results suggest that students’ beliefs are positive and significant in learning mathematics. Using Pearson chi-square test, generally the results suggest that there are significant differences in means between students’ beliefs based on institutions and mathematics grade. In addition, we find that overall there are no significant differences in means between beliefs based on gender, secondary education and major.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 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".