Secondary Students' Attitudes toward Mathematics
Bibliographic record
Abstract
Abstract The purpose of this study was to investigate the attitudes of secondary school students toward mathematics study, to compare the attitudes of students in the USA with eight other countries, and to compare differences in attitudes by gender for students in the USA. The study also analyzed the relationships between these attitudes and other mathematics learning factors and reported their impact on mathematics achievement. Introduction Research centering on students' attitudes toward mathematics study has received increasing attention. Studies have shown that factors such as motivation and attitude have impacted student achievement (Cote & Levine, 2000; Singh, Granville & Dika, 2002). Moreover, instructional strategies may also support student needs in order to increase student achievement. For example, Bottge (2001) found that when math problems were interesting and engaging, students with learning disabilities were able to solve problems that emphasized higher level thinking skills. Tymms (2001) investigated 21,000 students' attitudes toward math and suggested that the most important factors were the teacher and students' academic level; while age, gender, and language were weakly associated with attitudes. Webster and Fisher's (2000) study revealed that rural and urban students' attitudes toward math and career aspirations positively affected their performance. Altermatt and colleagues (2002) found that students' attitude changes could be predicted and influenced by types of classmates. Webb, Lubinski, & Benbow (2002) found educational experiences, abilities, and interests predicted undergraduate degree concentrations in math and science. Koller, Baumert, and Schnabel (2001) studied gender differences in mathematics achievement, which favored males in achievement, interest, and placement in advanced math courses. Few studies systematically analyzed attitudes, various mathematics learning factors, and achievement of secondary school students using an international database. Utilizing trends in International Mathematics and Science Study (TIMSS), provides insight into cross-national similarities and differences, and augments the existing literature. Methods Sample. A total of 9,072 eighth grade students in the USA were compared with students from eight other countries. These countries included Australia (4,032), Canada (8,770), Chile (5,907), England (2,960), Israel (4,195), Japan (4,745), Russia (4,332), and South Africa (8,146). Australia's sample included both eighth and ninth grade students, and England's sample included only ninth grade students. The sampling design from the TIMSS 1999 study ensured that a representative sample of eighth or ninth grade students was drawn. Data Sources. The data were derived from the TIMSS 1999 study that included student achievement in mathematics and information obtained through a student questionnaire. A total of 57 items were selected from the student questionnaire. Of these items, 11 reflected students' attitudes toward mathematics study. Questions were centered on three categories: importance (2 items), interest (3 items), and difficulty (6 items). Students rated their level of agreement with each item on a four-point scale: 1=Strongly Disagree, 2=Disagree, 3=Agree, and 4=Strongly Agree. Of the remaining 46 items, questions were centered on additional categories including family factors (4 items), friends/classmates' attitudes and behaviors (4 items), self-expectations (3 items), self-concept of performance in math (4 items), motivation (4 items), teaching approaches (26 items), and gender (1 item). Data Analysis. Descriptive statistics were employed to analyze the characteristics of eighth grade students, which centered on three categories: importance, interest, and difficulty. Where questionnaire items that were categorized as indicating interest or difficulty were reversed, the items were recoded to reflect the opposite score. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".