A Research on the Influence Paths of Mathematics Achievements of Highly Effective Mathematics Learners in High School

Studies of Psychology and Behavior ›› 2016, Vol. 14 ›› Issue (3): 352-359.

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A Research on the Influence Paths of Mathematics Achievements of Highly Effective Mathematics Learners in High School

Kang Yueyuan¹, Zhang Nan¹, Wang Guangming¹, She Wenjuan², Liu Yanyun³

1 School of Teacher Education, Tianjin Normal University, Tianjin 300387;
2 School of Tianjin Binhai New Area Affiliated to Tianjin Normal University, Tianjin 300452;
3 Malvern College Qingdao, Qingdao 266106

Received:2015-09-10 Online:2016-06-06 Published:2016-05-20

高效率数学学习高中生数学成绩的影响路径

康玥媛¹,张楠¹,王光明¹,佘文娟²,刘艳云³

1 天津师范大学教师教育学院，天津 300387;
2 天津师范大学滨海新区附属学校，天津 300452;
3 青岛墨尔文中学，青岛 266106

通讯作者: 张楠,E-mail:xiaoyu6109@126.com
基金资助:
教育部人文社科一般项目（13YJA190012）

Abstract

Abstract: To improve students′ mathematics learning efficiency has become one of the necessary methods to reduce overweight academic burden. This article aims to explore the effect of different factors on the performance of highly effective mathematics learners in high school, and the path of these factors. 102 students in high school were selected by focal sampling. Data were collected through the method of questionnaire survey. This study used five questionaires. Four of them is the self-made questionnaire. Findings suggested that: 1)The effects of mathematics metacognition, mathematics non-intelligence factors, intelligence factors, mathematics learning strategies and mathematics literacy decreased. 2)These five factors influenced the mathematics achievements mainly through two paths. One path indudes intelligence factors and mathematics literacy, another path contains mathematics metacognition, mathematics non-intelligence factors and mathematics learning strategies.

Key words: highly effective mathematics learning, mathematics metacognition, mathematics non-intelligence factors, mathematics learning strategies, mathematics literacy

摘要： 为探究高效率数学学习高中生的数学学习成绩的影响因素的不同作用效果，以及这些影响因素间存在的作用路径，通过目标抽样，选取102名高中生为被试开展调查研究。通过数据分析，并依托AMOS软件的模型界定搜寻功能进行路径分析，获得研究结论：（1）数学元认知、数学非智力因素、智力因素、数学学习策略和数学素养这5个变量对高效率数学学习高中生的数学学习成绩的作用效果值依次递减。（2）5个变量通过两条主要路径影响高效率数学学习高中生的数学学习成绩，一是智力因素与数学素养构成的影响路径，二是数学元认知、数学非智力因素以及数学学习策略构成的影响路径。

关键词: 高效率数学学习, 数学元认知, 数学非智力因素, 数学学习策略, 数学素养

CLC Number:

G442

Kang Yueyuan, Zhang Nan, Wang Guangming, She Wenjuan, Liu Yanyun. A Research on the Influence Paths of Mathematics Achievements of Highly Effective Mathematics Learners in High School[J]. Studies of Psychology and Behavior, 2016, 14(3): 352-359.

康玥媛,张楠,王光明,佘文娟,刘艳云. 高效率数学学习高中生数学成绩的影响路径[J]. 心理与行为研究, 2016, 14(3): 352-359.

References

[1] 陈英和, 韩瑽瑽.(2012). 儿童青少年元认知的发展特点及作用的心理机制. 心理科学, 35, 537-543.
[2] 康玥媛.(2009). 高中数学高才生数学学习的个案研究. 硕士学位论文. 天津师范大学.
[3] 林崇德.(1997). 论学科能力的建构. 北京师范大学学报(社会科学版), 1, 5-12.
[4] 李洪玉, 何一粟.(2004). 学习能力发展心理学. 合肥: 安徽教育出版社.
[5] 李洪玉, 阴国恩.(1997). 中小学生学业成就与非智力因素的相关研究. 心理科学, 20(5), 423-427.
[6] 李娜.(2010). 高中数学学困生学习效率低的归因研究. 硕士学位论文. 天津师范大学.
[7] 李士锜.(1996). 熟能生巧吗. 数学教育学报, 5(3), 46-50.
[8] 沈德立, 白学军.(2006). 高效率学习的心理机制研究. 心理科学, 29(1), 2-6.
[9] 沈德立, 李洪玉, 庄素芳, 杜辉, 胡建俊, 张维.(2000). 中小学生的智力、学习态度与其数学学业成就的相关性研究. 天津师范大学学报（基础教育版）, 1(2), 1-5.
[10] “数学教学效率论”江西子课题组.(2005). 中学生数学学习效率成因研究. 数学教育学报, 14(3), 53-56.
[11] 司继伟.(2000). 心理因素对高中生学业成就的影响. 心理发展与教育, 2, 9-11.
[12] 王光明.(2005). 重视数学教学效率提高数学教学质量——“数学教学效率论”课题简介. 数学教育学报, 14(3), 47-50.
[13] 王光明, 刁颖.(2009). 高效数学学习的心理特征研究. 数学教育学报, 18(3), 39-41.
[14] 王光明, 廖晶, 黄倩, 王兆云, Douglas McDougall.(2015). 高中生数学学习策略问卷的编制. 数学教育学报, 24(5), 25-36.
[15] 王光明, 佘文娟, 宋金锦.(2014). 基于NVivo10质性分析的高效数学学习心理结构模型. 心理与行为研究, 12(1), 74-79.
[16] 王光明, 宋金锦, 王兆云.(2015). 高中生数学学习非智力特征调查问卷的编制. 数学教育学报, 24(3), 17-27.
[17] 王光明, 佘文娟, 王兆云.（2016）. 高中生数学元认知水平调查问卷的设计与编制. 心理与行为研究, 14(2), 152-161.
[18] 王晓柳, 李宁玉, 郝京华, 吴康宁.(1988). 智力因素, 非智力因素对不同成绩的学生学习的影响及其机制. 华东师范大学学报(教育科学版), 3(2), 57-64.
[19] 王振宏, 刘萍.(2000). 动机因素、学习策略、智力水平对学生专业成就的影响. 心理学报, 32(1), 65-69.
[20] 吴明隆.(2010). 结构方程模型: AMOS的操作与应用(第2版). 重庆: 重庆大学出版社.
[21] 张厚粲, 王晓平.(1989). 瑞文标准推理测验在我国的修订. 心理学报, (2), 113-121.
[22] 张宏如, 沈烈敏.(2005). 学习动机、元认知对学业成就的影响. 心理科学, 28(1), 114-116.
[23] Cohen, J.(1988). Statistical power analysis for the behavioral sciences. Retrieved February 10, 2015 from http://www.lrdc.pitt.edu/schneider/P2465/Readings/Cohen, %201988%20(Statistical%20Power, %20273-406). pdf
[24] Creswell, J. W.(2012). Qualitative inquiry and research design: Choosing among five approaches(3rd ed.). Thousand Oaks, CA: Sage Publication.
[25] Hoffman, B., & Spatariu, A.(2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology, 33(4), 875-893.
[26] Nelson, T. O.(1996). Consciousness and metacognition. American Psychologist, 51(2), 102-116.
[27] Osborne, R. J., & Wittrock, M. C.(1983). Learning science: A generative process. Science education, 67(4), 489-508.

A Research on the Influence Paths of Mathematics Achievements of Highly Effective Mathematics Learners in High School

高效率数学学习高中生数学成绩的影响路径

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