Secondary Students' Attitudes toward Mathematics
Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».