Developmental Trajectory of High School Students’ Academic Motivation and Its Relation with Academic Achievement

doi:10.16382/j.cnki.1000-5560.2022.11.007

Abstract

Abstract:

As a bridge between compulsory education and higher education, high school education plays a vital role in Chinese education system. Whether students can establish adaptive motivational beliefs in high school significantly influences their future academic development. Using latent cross-lagged modeling, the present study investigated the longitudinal interrelation among self-concept, interest value, and effort cost in students’ academic achievement from grade 11 to 12 for both math and English domains. Results based on a sample of 694 Chinese high school students revealed significant reciprocity between self-concept and effort cost in math. In math, effort cost in the second semester of grade 11 also negatively predicted interest value in the first semester of grade 12. In English, the development of motivational beliefs is relatively independent, only self-concept in the first semester of grade 11 negatively predicted effort cost in the second semester of grade 11. In both math and English, self-concept positively predicted academic achievement, whereas effort cost negatively predicted academic achievement. Latent interaction analysis further revealed that there was significant interaction effect between self-concept and effort cost on achievement in the math domain. Findings of the present study highlight the importance of students’ motivational beliefs in influencing their academic achievement. In the meantime, the developmental trajectories of motivational beliefs are dynamic and demonstrate a clear pattern of domain-specificity.

Key words: academic motivation, achievement, high school students, longitudinal study, cross-lagged analysis

Yi Jiang. Developmental Trajectory of High School Students’ Academic Motivation and Its Relation with Academic Achievement[J]. Journal of East China Normal University(Educational Sciences), 2022, 40(11): 80-91.

Figures/Tables 5

References 0

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	数学			英语
	α	M	SD	α	M	SD	1	2	3	4	5	6	7	8	9	10
1.T1学业自我概念	0.87	2.94	1.08	0.91	3.33	1.27	—	0.63^**	?0.60^**	0.45^**	0.32^**	?0.43^**	0.58^**	0.41^**	?0.37^**	0.42^**
2.T1学习兴趣价值	0.93	4.13	1.08	0.96	4.15	1.31	0.47^**	—	?0.46^**	0.33^**	.45^**	?0.26^**	0.36^**	0.51^**	?0.27^**	0.33^**
3.T1努力成本认知	0.86	4.00	1.20	0.95	3.46	1.46	?0.42^**	?0.23^**	—	?0.32^**	?0.27^**	0.49^**	?0.34^**	?0.26^**	0.43^**	?0.32^**
4.T2学业自我概念	0.92	3.38	1.22	0.94	3.68	1.24	0.55^**	0.29^**	?0.31^**	—	0.61^**	?.30^**	0.44^**	0.35^**	?0.34^**	0.32^**
5.T2学习兴趣价值	0.95	4.16	1.17	0.96	4.26	1.20	0.27^**	0.46^**	?0.17^**	0.56^**	—	?0.21^**	0.34^**	0.53^**	?0.29^**	0.25^**
6.T2努力成本认知	0.93	3.69	1.31	0.96	3.36	1.38	?0.30^**	?0.19^**	0.42^**	?0.25^**	?0.10^*	—	?0.29^**	?0.20^**	0.49^**	?0.37^**
7.T3学业自我概念	0.91	3.45	1.18	0.92	3.75	1.20	0.52^**	0.30^**	?0.22^**	0.47^**	0.19^**	?0.26^**	—	0.59^**	?0.34^**	0.30^**
8.T3学习兴趣价值	0.95	4.27	1.12	0.95	4.32	1.12	0.34^**	0.48^**	?0.12^*	0.31^**	0.44^**	?0.20^**	0.53^**	—	?0.22^**	0.26^**
9.T3努力成本认知	0.93	3.73	1.35	0.96	3.29	1.36	?0.36^**	?0.16^**	0.41^**	?0.37^**	?0.26^**	0.46^**	?0.37^**	?0.17^**	—	?0.29^**
10.T4学业成绩	—	104.29	21.24	—	109.90	15.67	0.35^**	0.26^**	?0.21^**	0.22^**	0.14^**	?0.25^**	0.35^**	0.34^**	?0.26^**	—

	模型拟合							模型比较
模型	χ²	df	CFI	TIL	RMSEA	SRMR	模型	Δχ²/Δdf	ΔCFI
学业自我概念
M1 形态等值模型	188.627/219.810	51/51	0.960/0.948	0.948/0.933	0.062/0.069	0.033/0.034	/	/	/
M2 弱等值模型	202.475/225.987	57/57	0.958/0.948	0.951/0.940	0.061/0.065	0.034/0.034	M2-M1	2.31/1.03	?0.002/0.000
M3 强等值模型	208.769/232.111	63/63	0.958/0.948	0.956/0.946	0.058/0.062	0.034/0.034	M3-M2	1.05/1.02	0.000/0.000
学习兴趣价值
M1 形态等值模型	82.081/53.083	51/51	0.993/0.999	0.990/0.999	0.030/0.008	0.024/0.019	/	/	/
M2 弱等值模型	95.843/63.931	57/57	0.991/0.998	0.989/0.990	0.031/0.013	0.027/0.023	M2-M1	2.29/1.81	?0.001/?0.001
M3 强等值模型	101.411/65.932	63/63	0.991/0.999	0.990/0.999	0.030/0.008	0.027/0.023	M3-M2	0.93/0.33	0.001/0.001
努力成本认知
M1 形态等值模型	25.702/40.052	24/24	0.999/0.995	0.999/0.992	0.010/0.031	0.015/0.020	/	/	/
M2 弱等值模型	33.673/46.774	28/28	0.998/0.994	0.997/0.992	0.017/0.031	0.025/0.022	M2-M1	1.99/1.68	?0.001/?0.001
M3 强等值模型	34.431/47.912	32/32	0.999/0.995	0.999/0.994	0.010/0.027	0.025/0.022	M3-M2	0.19/0.28	0.001/0.001

	T4学业成绩
性别	?0.11^*/0.08^
年龄	?0.08/?0.08^**
家庭社会经济地位	?0.06/?0.07
T3学业自我概念	0.18^/0.20^
T3学习兴趣价值	0.21^**/0.11
T3努力成本认知	?0.18^/?0.17^
T3学业自我概念 × T3学习兴趣价值	?0.09/?0.11
T3学业自我概念 × T3努力成本认知	0.11^**/?0.05
T3学习兴趣价值 × T3努力成本认知	?0.00/0.02

[1]	Hao Lei, Xue Li. Does the Digital Tools-supported Teaching Have an Impact on Students’ Learning Results:Meta-Analysis Evidence from 137 Experiments and Quasi Experiments [J]. Journal of East China Normal University(Educational Sciences), 2022, 40(11): 92-109.
[2]	Fan Yang, Yuxuan He, Zhichen Xia. Does Reflection Improve Teachers’ Classroom Behavior Continuously: Based on a Longitudinal Study of Teachers with Different Teaching Philosophy [J]. Journal of East China Normal University(Educational Sciences), 2022, 40(10): 17-28.
[3]	Wanpeng Lei, Hongmei Ma, Huaming Huang. Evaluate Teaching Performance by Residuals of Student Achievement [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(7): 84-91.
[4]	Yi Wei, Bin Luo, Xiuyan Lin, Zhijun Yang, Meng Wang. The Effects of District-level and School-level Teaching-research Activities on Student Achievement: Evidence from Haidian District of Beijing [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(5): 12-54.
[5]	Tian Wang, Qimeng Liu, Haifeng Luo, Jian Liu. Testing the “Thresholds” of High School Students’ Learning Motivation, Learning Pressure and Subjective Well-being: An Example of S Province in East China [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(3): 59-70.
[6]	Yishan Li, Jinsong Liu. Relatives are not Avoided When Talents are Demanded: Does Insider Characteristics Reduce the Quality and Influence of Academic Papers? Examples from Mainland China [J]. Journal of East China Normal University(Educational Sciences), 2021, 39(10): 77-102.
[7]	Zeng Zhaobing, Yao Jijun. Identifying the “Best Evidence”: How to Use Meta-analysis to Conduct a Literature Review—A Case of STEM Education’s Effect on Students’ Academic Achievement [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(6): 70-85.
[8]	Liu Jian, Zhao Liman, Du Xiaofeng, Xu Guanxing. A Study of the Warning Mechanism and the Ideal Matching Pattern between Sleep Time and Academic Achievement of High-school Students [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(3): 71-79.
[9]	Wei Rui, Liu Jian, Bai Xinwen, Ma Xiaoying, Liu Yan, Ma Lihong, Gan Qiuling, Kang Cuiping, Xu Guanxing. The Research Design of the 5Cs Framework for Twenty-first Century Key Competences [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(2): 20-28.
[10]	Zhao Chen, Chen Si, Cao Yan, Catherine Snow, Lu Mai. Targeted Poverty Alleviation through Education: Long-term Effect of China’s “One Village One Preschool” Project on Rural Children’s Academic Achievements [J]. Journal of East China Normal University(Educational Sciences), 2020, 38(2): 114-125.
[11]	SU Na, SHAN Yuping. An Analysis of the Development of Students' Ethics of Responsibility in China [J]. Journal of East China Normal University(Educationa, 2018, 36(4): 122-130+166.
[12]	WANG Jiang-Tao,YU Qi-Ding. A Historical Study of Achievement Exhibitions of Vocational Schools in China (1918-1944) [J]. Journal of East China Normal University(Educationa, 2016, 34(2): 104-110.
[13]	SHEN Yu-Shun. Policy Analysis on Reform of Senior High SchoolEntrance Examination in China [J]. Journal of East China Normal University(Educationa, 2014, 32(3): 26-30.
[14]	CHEN Yong, YAO Mei-Lin. Review on Achievement Goals in Collaborative Learning [J]. Journal of East China Normal University(Educationa, 2011, 29(2): 56-61.