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The introduction of computer programming in K-12 has become mainstream in the last years, as countries around the world are making coding part of their curriculum. Nevertheless, there is a lack of empirical studies that investigate how learning to program at an early age affects other school subjects. In this regard, this paper compares three quasi-experimental research designs conducted in three different schools (n = 129 students from 2nd and 6th grade), in order to assess the impact of introducing programming with Scratch at different stages and in several subjects. While both 6th grade experimental groups working with coding activities showed a statistically significant improvement in terms of academic performance, this was not the case in the 2nd grade classroom. Notable disparity was also found regarding the subject in which the programming activities were included, as in social studies the effect size was double that in mathematics.

Computational Thinking (CT) is crucial for the advancement of the STEM field, where there continues to be a lack of female representation. Teaching and learning (T/L) of CT should incorporate didactic strategies that aim to eliminate gender biases and integrate girls/women into this context. In response to the question, "What didactic strategies have been implemented in the T/L of CT in primary and secondary education?". A systematic review was conducted following a PRISMA protocol specifically designed for this review. The study analysed Didactic Strategies (DS) and examined whether they incorporated the so‐called "minimum actions" (MA) proposed in this article as a strategy to integrate females into CT. The findings revealed a limited number of studies that implemented these actions. In conclusion, there is an urgent need to continue developing DS that incorporate these MA to effectively integrate girls and women into CT, allowing them to play a central role in its advancement and development. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) is being consolidated as a key set of problem-solving skills that must be developed by the students to excel in our software-driven society. However, in psychological terms, CT is still a poorly defined construct, given that its nomological network has not been established yet. In a previous paper, we started to address this issue studying the correlations between CT and some fundamental cognitive variables, such as primary mental abilities and problem-solving ability. The current work deepens in the same direction as it aims to extend the nomological network of CT with non-cognitive factors, through the study of the correlations between CT, self-efficacy and the several dimensions from the ‘Big Five’ model of human personality: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. To do so, the Computational Thinking Test (CTt) and some additional self-efficacy items are administered on a sample of 1251 Spanish students from 5th to 10th grade ( N = 1251), and the Big Five Questionnaire-Children version (BFQ-C) is also taken by a subsample from the above ( n = 99). Results show statistically significant correlations between CT and self-efficacy perception relative to CT performance ( r s = 0.41), in which gender differences in favor of males are found ( d = 0.42). Moreover, results show statistically significant correlations between CT and: Openness to Experience ( r = 0.41), Extraversion ( r = 0.30), and Conscientiousness ( r = 0.27). These findings are consistent with the existing literature except for the unexpected correlation between CT and the Extraversion factor of personality, which is consequently discussed in detail. Overall, our findings corroborate the existence of a non-cognitive side of CT that should be taken into account by educational policies and interventions aimed at fostering CT. As a final contribution, the extended nomological network of CT integrating cognitive and non-cognitive variables is depicted. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) is being located at the focus of educational innovation, as a set of problem-solving skills that must be acquired by the new generations of students to thrive in a digital world full of objects driven by software. However, there is still no consensus on a CT definition or how to measure it. In response, we attempt to address both issues from a psychometric approach. On the one hand, a Computational Thinking Test (CTt) is administered on a sample of 1,251 Spanish students from 5th to 10th grade, so its descriptive statistics and reliability are reported in this paper. On the second hand, the criterion validity of the CTt is studied with respect to other standardized psychological tests: the Primary Mental Abilities (PMA) battery, and the RP30 problem-solving test. Thus, it is intended to provide a new instrument for CT measurement and additionally give evidence of the nature of CT through its associations with key related psychological constructs. Results show statistically significant correlations at least moderately intense between CT and: spatial ability ( r = 0.44), reasoning ability ( r = 0.44), and problem-solving ability ( r = 0.67). These results are consistent with recent theoretical proposals linking CT to some components of the Cattel-Horn-Carroll (CHC) model of intelligence, and corroborate the conceptualization of CT as a problem-solving ability. [ABSTRACT FROM AUTHOR]

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There is increasing effort to integrate Computational Thinking (CT) curricula across all education levels. Therefore, research on CT assessment has lately progressed towards developing and validating reliable CT assessment tools, which are crucial for evaluating students' potential learning progress and thus the effectiveness of suggested curricular programs. Several CT assessment tools were developed for elementary, high-school, and university students over the last years. Moreover, associations between CT scores and other cognitive abilities were unraveled. However, studies on the topic in primary school level are scarce. Like the general concept of intelligence, CT remains broadly defined as the ability to combine algorithmic operations to form complex solutions in order to solve problems effectively, utilizing concepts of computer science with or without the use of computers. In this study, we aimed at specifying a cognitive definition of CT, focusing on the under-investigated population of primary school children. Since validated assessment tools for this age group were not available, we adapted a validated CT test, which was initially designed for middle school students. In our study participated 192 third and fourth graders. The analyses revealed promising results on the reliability of the adapted CT assessment for primary school students. Moreover, findings indicated CT's positive associations with i. complex numerical abilities, ii. verbal reasoning abilities, and iii. non-verbal visuospatial abilities. Our results indicate similarities but also differences in associations of CT with other cognitive abilities in primary school children compared to other age groups. In summary: i. numerical abilities seem to associate with CT at the primary school level, whereas this seems not the case later on in education, ii. verbal abilities seem to associate with CT both along primary and secondary education levels, and iii. non-verbal reasoning abilities seem to associate with CT from primary education level to the university level and beyond. These differences imply that several basic cognitive abilities support CT abilities and CT development differentially across ages. • The cognitive development of computational thinking is differential across age. • Computational thinking builds on a convolute of several other cognitive abilities. • Computational thinking is associated with verbal reasoning abilities. • Computational thinking is associated with non-verbal visuospatial abilities. • Computational thinking is correlated to numerical abilities in primary school. [ABSTRACT FROM AUTHOR]