What Shapes the Innovative Ability of Primary and Secondary School Students: An Empirical Study Based on Machine Learning

doi:10.16382/j.cnki.1000-5560.2026.07.005

Abstract

Abstract:

The key to the early cultivation of innovative talents is to promote the development of primary and secondary school students’ innovative ability through appropriate education. In order to discover and cultivate the top innovative talents and consolidate the foundation of talent cultivation, based on the survey data of 7552 students, the study focuses on the heterogeneity of internal and external components of the innovative ability, and empirically examines the development mechanism of the students’ innovative ability by machine learning. The study found that, first, the development of students’ innovative ability is a complex process of long-term interaction between various internal and external factors, in which the contribution rate of the internal component of an individual (83.09%) is greater than that of the external environment component (13.44%). Second, the individual’s internal component, such as learning interest and optimism, is the most critical, while the external environment component, teacher support, has the highest contribution rate. Third, in terms of the stage of development of innovativeness, there is temporal fluctuation in the contribution of the factors. Among them, students’ resilience trait and leisure time become more and more critical with the extension of school stages, and teacher support is more important in primary and junior high school. Finally, from the perspective of the prediction model of innovation ability, there is a non-linear relationship between knowledge base, intrinsic motivation, interpersonal support and leisure time and innovation ability. In this regard, we should adhere to the concept of cultivating students’ innovative ability by combining inclusiveness with stratification, improve the cultivation mechanism of endogenous-exogenous dual empowerment, and build the cultivation mode of segmented-through mutual articulation, so that the top innovative talents can continually emerge.

Key words: students’ innovative ability, early cultivation of top innovative talents, machine learning, influencing factors

Jia Qian, Xiaonan Cui. What Shapes the Innovative Ability of Primary and Secondary School Students: An Empirical Study Based on Machine Learning[J]. Journal of East China Normal University(Educationa, 2026, 44(7): 61-75.

Figures/Tables 8

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			均值	标准差	最小值	最大值
学生创新能力			7.334	2.267	0	10
内部个体成分	知识基础	语文成绩	6.313	1.758	0	10
		数学成绩	5.063	2.126	0	10
		英语成绩	4.636	1.909	0	10
	人格特质	乐观	3.951	0.934	1	5
		韧性	3.875	1.034	1	5
		信心	3.907	1.071	1	5
	内在动机	学习兴趣	3.655	1.170	1	5
	内在动机	个人教育期望	17.882	3.869	0	23
外部环境成分	课堂教学	课堂包容氛围	4.171	0.642	2	5
	人际支持	亲子关系	4.474	0.846	1	5
		教师支持	3.687	1.032	1	5
		家校合作	2.449	0.995	1	4
	闲暇时间	课外阅读	8.870	7.790	0	38.5
		体育锻炼	8.527	7.667	0	38.5
		上网娱乐	6.253	8.368	0	38.5
背景禀赋		男生	0.494	−	−	−
		小学	0.413	−	−	−
		初中	0.338	−	−	−
		高中	0.249	−	−	−
		独生子女	0.411	−	−	−
		留守儿童	0.474	−	−	−
		家庭社会经济地位	4.824	1.332	0	10
样本量			7552

		模型1	模型2
知识基础	语文成绩	0.026^**	0.006^*
		(0.013)	(0.003)
	数学成绩	−0.010	−0.002
		(0.011)	(0.003)
	英语成绩	−0.008	−0.004
		(0.011)	(0.003)
人格特质	乐观	0.875^***	0.139^***
		(0.034)	(0.007)
	韧性	0.308^***	0.056^***
		(0.031)	(0.007)
	信心	0.074^***	0.016^***
		(0.025)	(0.006)
内在动机	学习兴趣	0.519^***	0.068^***
		(0.030)	(0.006)
	个人教育期望	0.025^***	0.005^***
		(0.006)	(0.001)
课堂教学	课堂包容氛围	0.055^*	0.012
课堂教学		(0.028)	(0.007)
人际支持	亲子关系	0.008	0.002
		(0.027)	(0.007)
	教师支持	0.132^***	0.021^***
		(0.025)	(0.006)
	家校合作	0.053^***	0.012^***
		(0.018)	(0.004)
闲暇时间	课外阅读	0.010^***	0.003^***
		(0.003)	(0.001)
	体育锻炼	0.008^***	0.002^***
		(0.003)	(0.001)
	上网娱乐	0.015^***	0.003^***
		(0.003)	(0.001)
背景禀赋	男生	−0.046	−0.013
		(0.035)	(0.009)
	小学	0.113^**	0.099^***
		(0.053)	(0.014)
	初中	0.203^***	0.084^***
		(0.053)	(0.014)
	独生子女	0.082^**	0.009
		(0.034)	(0.009)
	留守儿童	−0.083^**	−0.018^**
		(0.034)	(0.009)
	家庭社会经济地位	0.067^***	0.015^***
		(0.014)	(0.004)
常数项		−1.446^***
		(0.204)
样本量		7552	7552
R²		0.591

	$ R_{IS}^{2} $	$ R_{OOS}^{2} $	$ {EVS}_{OOS} $	$ {MSE}_{OOS} $	$ {MAE}_{OOS} $	$ {MedAE}_{OOS} $
OLS回归	0.600	0.552	0.552	0.023	0.108	0.074
LASSO回归	0.543	0.512	0.512	0.025	0.124	0.101
岭回归	0.599	0.553	0.553	0.023	0.108	0.075
随机森林	0.581	0.534	0.535	0.024	0.109	0.071
GBRT	0.612	0.563	0.563	0.022	0.106	0.069
XGBoost	0.617	0.563	0.563	0.022	0.105	0.067

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