IQ or EQ: Which is More Important for Students’ Learning ?

doi:10.16382/j.cnki.1000-5560.2022.11.006

Abstract

Abstract:

This study of a sample of 860 Chinese secondary school students used Raven reasoning test and Big Five personality scale to investigate the longitudinal relationship between intelligence, personality and students’ academic performance in three subjects (Chinese, Math, and English). This is a three-wave longitudinal study covering one school year. The cross-lagged panel model analyses indicated that: (1) students’ previous intelligence level can significantly and positively predicted their academic performance in all three subjects. That is, students with high intelligence level tended to achieve higher academic performance in three subjects; (2) students’ personality traits, especially openness, also significantly and positively predicted their academic performance in all three subjects; (3) in the long-term run, there is a compensatory interaction between students’ intelligence and openness in the prediction of academic performance. In this regard, both intelligence and personality traits are very important for students’ learning. Educators should not only pay attention to the development of students’ potential and intelligence, but also to the cultivation of students’ personality traits.

Key words: adolescents, intelligence, Big Five personality traits, academic performance, cross-lagged panel model

Jing Zhang. IQ or EQ: Which is More Important for Students’ Learning ?[J]. Journal of East China Normal University(Educational Sciences), 2022, 40(11): 69-79.

Figures/Tables 7

References 0

	Bollen, K. A., & Curran, P. J Autoregressive latent trajectory (alt) models a synthesis of two traditions Sociological Methods & Research 2004 32 336 383 Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (alt) models a synthesis of two traditions. Sociological Methods & Research, 32, 336—383.
	Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research. The Guilford Press: New York, NY, USA.
	Cheung, G. W., & Rensvold, R. B Evaluating goodness-of-fit indexes for testing measurement invariance Structural Equation Modeling 2002 9 233 255 Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233—255. doi: 10.1207/S15328007SEM0902_5
	Deary, I. J., Strand, S., Smith, P., & Fernandes, C Intelligence and educational achievement Intelligence 2007 35 13 21 Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35, 13—21. doi: 10.1016/j.intell.2006.02.001
	De Feyter, T., Caers, R., Vigna, C., & Berings, D Unraveling the impact of the Big Five personality traits on academic performance: The moderating and mediating effects of self-efficacy and academic motivation Learning and Individual Differences 2012 22 439 448 De Feyter, T., Caers, R., Vigna, C., & Berings, D. (2012). Unraveling the impact of the Big Five personality traits on academic performance: The moderating and mediating effects of self-efficacy and academic motivation. Learning and Individual Differences, 22, 439—448. doi: 10.1016/j.lindif.2012.03.013
	De Raad, B., & Schouwenburg, H. C Personality in learning and education: A review European Journal of Personality 1996 10 303 336 De Raad, B., & Schouwenburg, H. C. (1996). Personality in learning and education: A review. European Journal of Personality, 10, 303—336. doi: 10.1002/(SICI)1099-0984(199612)10:5<303::AID-PER262>3.0.CO;2-2
	Digman, J. M Five robust trait dimensions: Development, stability, and utility Journal of Personality 1989 57 195 214 Digman, J. M. (1989). Five robust trait dimensions: Development, stability, and utility. Journal of Personality, 57, 195—214. doi: 10.1111/j.1467-6494.1989.tb00480.x
	Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D Development during adolescence: The impact of stage-environment fit on young adolescents’ experiences in schools and in families American Psychologist 1993 48 90 101 Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: The impact of stage-environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48, 90—101. doi: 10.1037/0003-066X.48.2.90
	Greiff, S., & Neubert, J. C On the relation of complex problem solving, personality, fluid intelligence, and academic achievement Learning and Individual Differences 2014 36 37 48 Greiff, S., & Neubert, J. C. (2014). On the relation of complex problem solving, personality, fluid intelligence, and academic achievement. Learning and Individual Differences, 36, 37—48. doi: 10.1016/j.lindif.2014.08.003
	Heaven, P. C. L., & Ciarrochi, J When IQ is not everything: Intelligence, personality and academic performance at school Personality and Individual Differences 2012 53 518 522 Heaven, P. C. L., & Ciarrochi, J. (2012). When IQ is not everything: Intelligence, personality and academic performance at school. Personality and Individual Differences, 53, 518—522. doi: 10.1016/j.paid.2012.04.024
	Heineck, G., & Anger, S. (2010). The returns to cognitive abilities and personality traits in Germany. EconStor Open Access Articles and Book Chapters.
	Hu, L. T., & Bentler, P. M Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives Structural Equation Modeling-a Multidisciplinary Journal 1999 6 1 55 Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling-a Multidisciplinary Journal, 6, 1—55. doi: 10.1080/10705519909540118
	Kim, L. E., Poropat, A. E., & MacCann, C. (2016). Conscientiousness in education: Its conceptualization, assessment, and utility. In A. A. Lipnevich, F. Preckel, & R. D. Roberts (Eds. ), Springer series on human exceptionality. Psychosocial skills and school systems in the 21st century: Theory, research, and practice (pp. 155−185). Switzerland: Springer.
	Klein, A., & Moosbrugger, H Maximum likelihood estimation of latent interaction effects with the lms method Psychometrika 2000 65 457 474 Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the lms method. Psychometrika, 65, 457—474. doi: 10.1007/BF02296338
	Lechner, C., Danner, D., & Rammstedt, B How is personality related to intelligence and achievement? A replication and extension of Borghans et al. and Salkever Personality and Individual Differences 2017 111 86 91 Lechner, C., Danner, D., & Rammstedt, B. (2017). How is personality related to intelligence and achievement? A replication and extension of Borghans et al. and Salkever. Personality and Individual Differences, 111, 86—91. doi: 10.1016/j.paid.2017.01.040
	Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M Why the items versus parcels controversy needn't be one Psychological Methods 2013 18 285 300 Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why the items versus parcels controversy needn't be one. Psychological Methods, 18, 285—300. doi: 10.1037/a0033266
	Lu, L., Weber, H. S., Spinath, F. M., & Shi, J Predicting school achievement from cognitive and non-cognitive variables in a Chinese sample of elementary school children Intelligence 2011 39 130 140 Lu, L., Weber, H. S., Spinath, F. M., & Shi, J. (2011). Predicting school achievement from cognitive and non-cognitive variables in a Chinese sample of elementary school children. Intelligence, 39, 130—140. doi: 10.1016/j.intell.2011.02.002
	Lubbers, M. J., van der Werf, M. P. C., Kuyper, H., & Hendriks, A. J. Does homework behavior mediate the relation between personality and academic performance? Learning and Individual Differences 2010 20 203 208 Lubbers, M. J., van der Werf, M. P. C., Kuyper, H., & Hendriks, A. J. (2010). Does homework behavior mediate the relation between personality and academic performance?. Learning and Individual Differences, 20, 203—208.
	Marsh, H. W. (2007). Self-concept theory, measurement and research into practice: The role of self-concept in educational psychology. Leicester, UK: British Psychological Society.
	Muthén, L. K. , & Muthén, B. O. (1998—2017). Mplus User’s Guide: 8th Edition. Los Angeles, CA: Muthén & Muthén.
	Poropat, A. E A meta-analysis of the five-factor model of personality and academic performance Psychological Bulletin 2009 135 322 338 Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322—338. doi: 10.1037/a0014996
	Raven, J. (1981). Manual for Raven’s Progressive Matrices and Mill Hill Vocabulary Scales. Oxford, UK: Oxford Psychologists Press.
	Revelle, W., & Zinbarg, R. E Coefficients alpha, beta, omega, and the glb: Comments on sijtsma Psychometrika 2009 74 145 154 Revelle, W., & Zinbarg, R. E. (2009). Coefficients alpha, beta, omega, and the glb: Comments on sijtsma. Psychometrika, 74, 145—154. doi: 10.1007/s11336-008-9102-z
	Richardson, M., Abraham, C., & Bond, R Psychological correlates of university students’ academic performance: A systematic review and meta-analysis Psychological Bulletin 2012 138 353 387 Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353—387. doi: 10.1037/a0026838
	Rosander, P., Bäckström, M., & Stenberg, G Personality traits and general intelligence as predictors of academic performance: A structural equation modelling approach Learning and Individual Differences 2011 21 590 596 Rosander, P., Bäckström, M., & Stenberg, G. (2011). Personality traits and general intelligence as predictors of academic performance: A structural equation modelling approach. Learning and Individual Differences, 21, 590—596. doi: 10.1016/j.lindif.2011.04.004
	Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M Intelligence and school grades: A meta-analysis Intelligence 2015 53 118 137 Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118—137. doi: 10.1016/j.intell.2015.09.002
	Spinath, B., Freudenthaler, H., & Neubauer, A. C Domain-specific school achievement in boys and girls as predicted by intelligence, personality and motivation Personality and Individual Differences 2010 48 481 486 Spinath, B., Freudenthaler, H., & Neubauer, A. C. (2010). Domain-specific school achievement in boys and girls as predicted by intelligence, personality and motivation. Personality and Individual Differences, 48, 481—486. doi: 10.1016/j.paid.2009.11.028
	Taub, G. E., Keith, T. Z., Floyd, R. G., & Mcgrew, K. S Effects of general and broad cognitive abilities on mathematics achievement School Psychology Quarterly 2008 23 187 198 Taub, G. E., Keith, T. Z., Floyd, R. G., & Mcgrew, K. S. (2008). Effects of general and broad cognitive abilities on mathematics achievement. School Psychology Quarterly, 23, 187—198. doi: 10.1037/1045-3830.23.2.187
	Tweed, R. G., & Lehman, D. R Learning considered within a cultural context American Psychologist 2002 57 89 99 Tweed, R. G., & Lehman, D. R. (2002). Learning considered within a cultural context. American Psychologist, 57, 89—99.
	Tett, R. P., & Burnett, D. D. A personality trait-based interactionist model of job performance Journal of Applied Psychology 2003 88 500 517 Tett, R. P., & Burnett, D. D. (2003). A personality trait-based interactionist model of job performance. Journal of Applied Psychology, 88, 500—517.
	Vedel, A. , & Poropat, A. E. (2017). Personality and academic performance. In V. Zeigler-Hill, & T. Shackelford (Eds. ), Encyclopedia of personality and individual differences (pp. 1−9). Springer.
	Yao, R. S., & Liang, L. Y The application and analysis of NEO-FFI in Chinese college students Chinese Journal of Clinical Psychology 2010 18 457 459 Yao, R. S., & Liang, L. Y. (2010). The application and analysis of NEO-FFI in Chinese college students. Chinese Journal of Clinical Psychology, 18, 457—459.
	Zhang, J., & Ziegler, M Interaction effects between openness and fluid intelligence predicting scholastic performance Journal of Intelligence 2015 3 91 110 Zhang, J., & Ziegler, M. (2015). Interaction effects between openness and fluid intelligence predicting scholastic performance. Journal of Intelligence, 3, 91—110. doi: 10.3390/jintelligence3030091
	Ziegler, M., Danay, E., Heene, M., Asendorpf, J., & Bühner, M Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model Journal of Research in Personality 2012 46 173 183 Ziegler, M., Danay, E., Heene, M., Asendorpf, J., & Bühner, M. (2012). Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model. Journal of Research in Personality, 46, 173—183. doi: 10.1016/j.jrp.2012.01.002

变量	平均数	标准差	1	2	3	4	5	6	7
		T1
1. 情绪稳定性	35.16	7.69	(0.85)
2. 外向性	42.28	6.43	?0.38***	(0.81)
3. 开放性	41.60	5.57	?0.10**	0.21***	(0.69)
4. 宜人性	29.70	5.11	0.38***	?0.22***	?0.11*	(0.65)
5. 尽责性	38.34	6.25	?0.43***	0.25***	0.26***	?0.36***	(0.83)
6. 数学成绩	98.07	30.80	?0.14**	0.09*	0.22***	?0.02	0.17**
7. 语文成绩	101.19	20.49	?0.09*	0.17**	0.23***	?0.04	0.10**	0.51***
8. 英语成绩	100.56	32.29	?0.03	0.11**	0.27***	0.07	0.07*	0.55**	0.59***
		T2
1. 情绪稳定性	35.11	7.86	(0.86)
2. 外向性	42.21	6.49	?0.37***	(0.82)
3. 开放性	41.83	5.62	?0.10**	0.21***	(0.70)
4. 宜人性	29.81	5.28	0.43***	?0.28***	?0.23**	(0.68)
5. 尽责性	38.24	6.33	?0.46***	0.28***	0.32***	?0.43***	(0.83)
6. 数学成绩	96.62	32.36	?0.10**	0.10**	0.24***	?0.10**	0.14**
7. 语文成绩	97.93	18.87	?0.03	0.13**	0.20***	?0.06	0.12**	0.52***
8. 英语成绩	98.78	31.86	0.02	0.08*	0.25***	?0.03	0.07*	0.66***	0.60***
		T3
1. 情绪稳定性	35.37	7.33	(0.85)
2. 外向性	42.45	6.11	?0.39***	(0.80)
3. 开放性	41.32	5.36	?0.12**	0.18***	(0.69)
4. 宜人性	29.49	5.08	0.42***	?0.19***	?0.18***	(0.67)
5. 尽责性	39.51	6.02	?0.41***	0.26***	0.37***	?0.37***	(0.83)
6. 数学成绩	89.01	29.14	?0.10**	0.02	0.23***	0.02	0.15**
7. 语文成绩	100.72	18.41	?0.03	0.00	0.16***	?0.04	0.12**	0.55***
8. 英语成绩	93.18	30.54	?0.04	0.02	0.22***	0.04	0.09*	0.72***	0.59***

模型	χ² (df)	RMSEA (90% CI)	CFI	TLI	SRMR	BIC
情绪稳定性
M1: 形态等值模型	28.11 (15)	0.033 [0.013, 0.052]	0.997	0.994	0.019	8716.76
M2: 弱等值模型	42.13 (21)	0.036 [0.020, 0.051]	0.996	0.993	0.047	8690.77
M3: 强等值模型	45.13 (25)	0.032 [0.016, 0.047]	0.996	0.994	0.048	8667.09
外向性
M1: 形态等值模型	23.76 (15)	0.027 [0.000, 0.047]	0.998	0.996	0.022	7266.88
M2: 弱等值模型	26.16 (21)	0.018 [0.000, 0.037]	0.999	0.998	0.030	7229.27
M3: 强等值模型	31.97 (25)	0.019 [0.000, 0.036]	0.999	0.998	0.033	7208.40
开放性
M1: 形态等值模型	38.77 (15)	0.045 [0.028, 0.063]	0.994	0.986	0.032	7018.70
M2: 弱等值模型	44.18 (21)	0.037 [0.022, 0.053]	0.994	0.990	0.045	6984.10
M3: 强等值模型	58.72 (25)	0.041 [0.028, 0.055]	0.992	0.988	0.045	6971.97
宜人性
M1: 形态等值模型	30.30 (15)	0.036 [0.017, 0.054]	0.995	0.988	0.027	8132.77
M2: 弱等值模型	33.97 (21)	0.028 [0.007, 0.045]	0.996	0.993	0.029	8096.43
M3: 强等值模型	42.09 (25)	0.029 [0.012, 0.045]	0.994	0.992	0.028	8077.87
责任心
M1: 形态等值模型	37.97 (15)	0.044 [0.027, 0.062]	0.995	0.989	0.020	6447.70
M2: 弱等值模型	43.24 (21)	0.037 [0.021, 0.052]	0.996	0.992	0.038	6412.96
M3: 强等值模型	280.11 (25)	0.114 [0.102, 0.126]	0.949	0.926	0.136	6623.16
M4: 部分强等值	48.57 (24)	0.036 [0.021, 0.051]	0.995	0.993	0.041	6398.29

学科	模型	χ² (df)	RMSEA (90% CI)	CFI	SRMR	BIC
数学	M1	2254.87 (1011)	0.038[0.036, 0.040]	0.951	0.059	62861.351
	M2					33171.661
语文	M1	2422.20 (1011)	0.041[0.039, 0.043]	0.945	0.059	61008.866
	M2					31291.019
英语	M1	2379.17(1011)	0.040[0.038, 0.042]	0.948	0.060	62273.975
	M2					32577.727

	T1-T2			T2-T3
影响路径	数学	英语	语文	数学	英语	语文
交叉滞后效应—开放性(t-1)作用于学习成绩(t)	0.11***	0.06**	0.09***	0.13***	0.06***	0.09***
交叉滞后效应—尽责性(t-1)作用于学习成绩(t)	?0.01	0.04	0.03	?0.01	0.04	0.03
交叉滞后效应—宜人性(t-1)作用于学习成绩(t)	0.03	0.02	0.04	0.05	0.03	0.06
交叉滞后效应—外向性(t-1)作用于学习成绩(t)	?0.01	?0.03	0.02	?0.01	?0.03	0.02
交叉滞后效应—情绪稳定性(t-1)作用于学习成绩(t)	?0.03	?0.02	0.01	?0.04	?0.02	0.01
交叉滞后效应—智力(t1)作用于学习成绩(t)	0.10***	0.05**	0.02	0.07**	0.05**	0.06*
交互作用-智力(t1)与开放性人格(t)交互作用于学业成绩(t)	?0.05*	?.01	?0.12***	?0.06*	?0.01	?0.13***

[1]	Li Zhao, Yinsheng Liu. The Technological Turn in Educational Dialogue: Transmutation Paths, Application Dilemma and Paradigm Reconfiguration—Logical Reflection and Prospect on the Application of ChatGPT [J]. Journal of East China Normal University(Educational Sciences), 2024, 42(8): 76-84.
[2]	Yingqiang Zhang, Yucong Tang. Based on “Education” or “Labor”: Reflection on Two General Concepts of Labor Education [J]. Journal of East China Normal University(Educational Sciences), 2024, 42(6): 38-50.
[3]	Mingxuan Chen, Liang Zhou. Deep Learning in the Age of Digital Intelligence: From Shallow Memory Towards Deep Understanding [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(8): 53-62.
[4]	Xiaoqing Gu, Yiling Hu, Xiangjun Hao. Is AGI Approaching: Rethinking the Development of AI and Future Education in the Context of the ChatGPT Craze [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(7): 117-130.
[5]	Zhi Zhang. ChatGPT/AIGC Reshape Education: Underlying Logic and Possible Paths [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(7): 131-142.
[6]	Yonghe Zheng, Danhua Zhou, Yonghe Zhang, Xuewei Tian, Jingying Wang, Yi Zheng. ChatGPT from the Perspective of Computational Education: Connotation, Theme, Reflection, and Challenge [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(7): 91-102.
[7]	Zhongjing Huang, Kaiyue Shang, Jing Zhang. Does Growth Mindset Affect Students’ Social and Emotional Skills Development: Empirical Analyses Based on OECD Social and Emotional Skills Study [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(4): 22-32.
[8]	Jiang Bo Penner:, Dai Juan Core Members:, Zhou Aimin , Dong Xiaoyong , Liu Xiaoyu , Hong Daocheng Participants:, Jiang Fei , Zheng Longwei , Zhao Jiabao , Zhang Hengyuan , Liu Yalin , Yuan Zhenguo Consultant:. Artificial Intelligence Curriculum Guidelines for Primary and Secondary Schools [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(3): 121-134.
[9]	Bo Jiang, Yingwen Ding, Yuang Wei. The Core Technology Engine of Digital Transformation in Education: Trustworthy Education Artificial Intelligence [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(3): 52-61.
[10]	Yu Shengquan. The Key Stages of Digital Transformation in Education [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(3): 62-71.
[11]	Yan He, Keman Yuan, Mingming Zhang, Yufang Bian. The Profiles of Parental Control and its Influence on Adolescents’ Adaptation: Based on Latent Transition Analysis [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 25-39.
[12]	Zeyuan Yu, Mingming Na. Situational Learning: Connotation, Value and Implementation [J]. Journal of East China Normal University(Educational Sciences), 2023, 41(1): 89-97.
[13]	Shijin Li, Chenglong Wang, Xiaoqing Gu. Governance of Artificial Intelligence Education: Logical Mechanism and Practical Approach [J]. Journal of East China Normal University(Educational Sciences), 2022, 40(9): 55-66.
[14]	Huiying Cai, Haixia Dong, Xu Chen, Xiaoqing Gu. How to Build Future School: Prospective Analysis and Practical Enlightenment of Application Scenarios of AI in Education [J]. Journal of East China Normal University(Educational Sciences), 2022, 40(9): 45-54.
[15]	Xuesong Zhai, Jiaqi Xu, Yonggu Wang. Research on Learning Affective Computing in Online Education: From the Perspective of Multi-source Data Fusion [J]. Journal of East China Normal University(Educational Sciences), 2022, 40(9): 32-44.