Curriculum School Students' Attitudes Toward Mathematics
Notice bibliographique
Résumé
Abstract Longitudinal data from four high schools over two school years indicate that students did not want a job using mathematics, even when they viewed mathematics as important. About half were willing to work a long time to understand new ideas or obtain a solution to a problem; slightly more than 50 percent viewed mathematics as mostly memorizing. Teachers must help students develop perseverance and broaden their view of mathematics. Introduction In its 1989 Curriculum and Evaluation Standards for School Mathematics, the National Council of Teachers of Mathematics (NCTM) established two goals related to affective issues: learning to value mathematics and developing confidence in one's own mathematical ability. Other documents from the same era, such as Everybody Counts, also focused on the need to change the public's attitudes and beliefs about mathematics, recognizing that too many people do not believe they can be successful at mathematics (National Research Council, 1989). In the revised Principles and Standards for School Mathematics (2000), NCTM again discussed mathematical disposition, highlighting the importance of students' confidence, interest, perseverance, and curiosity in learning mathematics. The recommendations encourage teachers to replace classrooms emphasizing low-level computation with active classrooms focusing on higher-level thinking. Indeed, how students view mathematics as well as their attitudes toward mathematics can impact their success. Several researchers over the last two decades have found that positive attitudes can increase the tendency of individuals to select mathematics courses and consider careers in mathematics related fields (Haladyna, Shaughnessy, and Shaughnessy, 1983; Maple and Stage, 1991; Trusty, 2002). In analysis of data from the Third International Mathematics and Science Study (TIMSS) for students from Canada, Norway, and the United States, Ercikan, McCreith, and Lapointe (2005) found that the strongest predictor of participation in advanced mathematics courses was students' attitudes toward mathematics. Thus, mathematics educators need to consider these results as they try to encourage more students to consider further study in mathematics related fields. Schoenfeld (1992) compiled a list of beliefs that many students hold, such as there is only one way to solve a mathematical problem, most students can simply memorize mathematics rather than be expected to understand it, and if a problem cannot be solved quickly then it cannot be solved. These views run counter to those that NCTM is trying to encourage. Yet, the beliefs Schoenfeld identified seem to be reinforced in studies conducted more recently. Signer, Beasley, and Bauer (1996) conducted in-depth interviews with 100 high school students about their beliefs of themselves as mathematics learners. They found that low-achieving students often believe their ability level is fixed and is the cause of their failures; hence, they avoid challenges and do not believe they can solve difficult problems. Higgins (1997) studied middle school students' mathematical beliefs; even among students who had completed a yearlong course utilizing problem-solving instruction, many still equated mathematical problem solving with learning problem-solving skills or rules. Likewise, Olson (1998) surveyed high school geometry students and found that one-third did not enjoy mathematics and close to 40 percent found their experiences with word problems to be frustrating. Perhaps this is not unexpected because word problems are not typically solved quickly. More recently, Schommer-Aikens, Duell, and Hutter (2005) studied middle school students' epistemological and mathematical problem-solving beliefs. They found that many students viewed learning as fast and instinctual. The authors pointed out that such beliefs are likely to influence students' problem-solving strategies and amount of time spent on solving problems. …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».