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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]

Informatics or computer science (the terms informatics and computer science are used as synonyms in this paper) in Austrian secondary schools is often considered “difficult” and “not interesting” because of mainly mathematical and economical contents. But informatics is much more; it is used in almost every part of the working life and can also cooperate with every subject at school. It offers a wide range of possibilities for COOL (COoperative Open Learning), an Austrian teaching model based on the Dalton Plan that provides also cooperation between different subject matters. Besides the COOL concept this paper describes some ideas for cooperation between informatics and foreign languages at secondary schools and at University level besides just ICT (Information and Communication Technology) or computer-assisted language learning. The presented tasks are part of the project “COOL Informatics” (according to COoperative Open Learning) that aims at developing COOL teaching material based on neurodidactical (Neurodidactics is a relatively young research field that combines the findings of brain research, pedagogy, cognitive psychology and other related fields.) principles for informatics and cross-curricular cooperation from primary school up to University. On the basis of the developed material the following main research questions of the project shall be examined in the course of the next school year: (1) Can COoperative Open Learning methods help to enhance learning? (2) Can learning be enhanced by considering neurodidactical principles in the design of teaching material and in the classroom? As the first test phase of the developed material is still in progress the evaluation is not available at the moment. But the first informal feedback of a vocational school and a programming course at the University shows that it is worth fostering COoperative Open Learning and cross-curricular cooperation. [Copyright &y& Elsevier]

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]

Programming is more than just coding, for, it exposes students to computational thinking which involves problem-solving using computer science concepts like abstraction and decomposition. Even for non-computing majors, computational thinking is applicable and useful in their daily lives. The three dimensions of computational thinking are computational concepts, computational practices and computational perspectives. In recent years, the availability of free and user-friendly programming languages has fuelled the interest of researchers and educators to explore how computational thinking can be introduced in K-12 contexts. Through an analysis of 27 available intervention studies, this paper presents the current trends of empirical research in the development of computational thinking through programming and suggests possible research and instructional implications. From the review, we propose that more K-12 intervention studies centering on computational practices and computational perspectives could be conducted in the regular classroom. To better examine these two dimensions, students could be asked to verbalize their thought process using think aloud protocol while programming and their on-screen programming activity could be captured and analyzed. Predetermined categories based on both past and recent programming studies could be used to guide the analysis of the qualitative data. As for the instructional implication, it is proposed that a constructionism-based problem-solving learning environment, with information processing, scaffolding and reflection activities, could be designed to foster computational practices and computational perspectives. [ABSTRACT FROM AUTHOR]