Cognitive and regulatory characteristics and mathematical performance in high school students

Morosanovа, Varvara I.; Fomina, Tatiana; Kovas, Yulia; Bogdanova, O. Yu.

doi:10.1016/j.paid.2015.10.034

Cited by 29 publications

(25 citation statements)

References 38 publications

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“…Non-verbal intelligence was moderately associated with computerized test scores and teachers' ratings (0.32 < r < 0.37). The results regarding the relationship between intelligence scores and the computerized test scores are in line with the studies: the correlation between the computerized test and intelligence on a Russian sample reaches 0.30 [14]. However, in this study no association between non-verbal intelligence and BSE scores was found [14].…”

Section: Correlation Between Different Variables Of Math Achievementsupporting

confidence: 90%

“…The results regarding the relationship between intelligence scores and the computerized test scores are in line with the studies: the correlation between the computerized test and intelligence on a Russian sample reaches 0.30 [14]. However, in this study no association between non-verbal intelligence and BSE scores was found [14]. This discrepancy can point to ongoing development of the BSE by the Ministry of education and science of the Russian Federation.…”

Section: Correlation Between Different Variables Of Math Achievementsupporting

confidence: 88%

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The relationship between non-verbal intelligence and mathematical achievement in high school students

et al. 2016

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Abstract. The current study focuses on the relationship between non-verbal intelligence and math performance at school measured with a number of methods such as teachers' evaluation, computerized math tasks and standardized state exams for high school students. The study involved 174 high school students in the last obligatory year of formal education at school in Russia (mean age = 15.77 years, SD = 0.47, 44.8% males) from secondary public school in the Moscow region. It was shown that non-verbal intelligence is a significant predictor of different measures of math performance at high school age. At the same time, we found that the contribution of non-verbal intelligence varies for different measures of math performance: from high values for of the Basic State Exam in Math scores to lower values for teachers' quarterly ratings. Correlation between math performance measured by teachers' ratings, computerized math tasks and standardized state exams was found to be moderate. It is possible that different types and configurations of math tasks require different types of cognitive, motivational and emotional resources at high school age.

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Section: Correlation Between Different Variables Of Math Achievementsupporting

confidence: 90%

Section: Correlation Between Different Variables Of Math Achievementsupporting

confidence: 88%

The relationship between non-verbal intelligence and mathematical achievement in high school students

et al. 2016

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“…On the other hand, in the older school age, these differences become minimal. In our previous study on the sample of senior schoolchildren, we did not observe significant differences in the components of self-regulation in boys and girls (Morosanova et al, 2016).…”

Section: Resultscontrasting

confidence: 53%

Self-Regulation And Personality Characteristics Of Elementary School Children With School Life Satisfaction

Fomina¹

2017

The European Proceedings of Social and Behavioural Sciences

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This article presents research results on the relationship between the conscious self-regulation of learning activity, personal characteristics, and life satisfaction in younger schoolchildren (N = 156, 85 girls, 71 boys, aged 10-11 years). Russian adaptation of Multidimensional Students Life Satisfaction Scale(MSLSS), Russian version of the «Big Five Questionnaire -Children (BFQ-C)» and Morosanova's Self-Regulation Profile Questionnaire -Junior was used. The focus is made on studying self-regulation and achieving educational goals in the context of the subjective well-being problem. Significant links have been revealed between the personality characteristics, regulatory features and life satisfaction indicators in three subscales: family life, school life, and self-attitude. The largest number of significant links was found for the School Life Satisfaction Subscale. Gender differences were discovered in the manifestations of self-regulation, personal characteristics, and the indicators of life satisfaction in younger schoolchildren: girls have significantly higher level of School Life Satisfaction, some regulatory processes and indicators of Agreeableness and Neuroticism. The study displays specificity of regulatory and personality characteristics of students with high and low level of School Life Satisfaction (SLS). Students with high level of SLS are characterized by a higher development of regulatory processes, selfregulation in general and those particular personality characteristics as Agreeableness, Conscientiousness, Openness.

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“…Metacognitive knowledge (Özsoy, 2011;Schneider & Artelt, 2010) and cognitive regulation (Morosanova, Fomina, Kovas, & Bogdanova, 2016) are both associated with higher levels of mathematical achievement. Numerical metacognitive knowledge in young children (aged five) can predict levels of mathematical knowledge at school (Vo, Li, Kornell, Pouget, & Cantlon, 2014).…”

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between metacognitive and collaborative talk during group mathematical problem-solving – what do we mean bycollaborativemetacognition?

Smith

Mancy

2018

Research in Mathematics Education

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The purpose of this study was to enhance our understanding of the relationship between collaborative talk and metacognitive talk during group mathematical problem-solving. Research suggests that collaborative talk may mediate the use of metacognitive talk, which in turn is associated with improved learning outcomes. However, our understanding of the role of group work on the individual use of metacognition during problem-solving has been limited because research has focused on either the individual or the group as a collective. Here, primary students (aged nine to 10) were video-recorded in a naturalistic classroom setting during group mathematical problem-solving sessions. Student talk was coded for metacognitive, cognitive and social content, and also for collaborative content. Compared with cognitive talk, we found that metacognitive talk was more likely to meet the criteria to be considered collaborative, with a higher probability of being both preceded by and followed by collaborative talk. Our results suggest that collaborative metacognition arises from combined individual and group processes.ARTICLE HISTORY

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Cognitive and regulatory characteristics and mathematical performance in high school students

Cited by 29 publications

References 38 publications

The relationship between non-verbal intelligence and mathematical achievement in high school students

The relationship between non-verbal intelligence and mathematical achievement in high school students

Self-Regulation And Personality Characteristics Of Elementary School Children With School Life Satisfaction

Exploring the relationship between metacognitive and collaborative talk during group mathematical problem-solving – what do we mean bycollaborativemetacognition?

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