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Computational thinking (CT) is an important 21st-century skill. This paper aims at more useful CT assessment. Available evaluation instruments are reviewed; two generally accepted CT evaluation tools are selected for a comprehensive CT assessment: the CTt, a performance test, and the CTS, a self-assessment instrument. The sample comprises 202 high school students from German-speaking Switzerland. Concerning the CTt, Rasch-scalability is demonstrated. Utilizing the approach of the PISA studies, proficiency levels are formed that comprise tasks with specific characteristics that students are systematically able to master. This could help teachers to offer individual support to their students. In terms of the CTS, the original version is refined using confirmatory factor and measurement-invariance analysis. A latent profile analysis yielded four profiles, two of which are of particular interest. One profile comprises students with, on the one hand, moderate to high creative thinking ability, cooperativity, and critical thinking skills and, on the other hand, low algorithmic thinking ability. The second remarkable profile consists of students with particularly low cooperativity. Based on these strength and weakness profiles, teachers could offer support tailored to student needs.

Given the potential applications of generative AI (GenAI) in education and its rising interest in research, this systematic review mapped the thematic landscape of 407 publications indexed in the Web of Science, ScienceDirect and Scopus. Using EPPI Reviewer, publication type, educational level, disciplines, research areas and applications of GenAI were extracted. Eight discursive themes were identified, predominantly focused on 'application, impact and potential', 'ethical implication and risks', 'perspectives and experiences', 'institutional and individual adoption', and 'performance and intelligence'. GenAI was conceptualised as a tool for 'pedagogical enhancement', 'specialised training and practices', 'writing assistance and productivity', 'professional skills and development', and as an 'interdisciplinary learning tool'. Key gaps highlighted include a paucity of research and discussions on GenAI in K‐12 education; a limited exploration of GenAI's impact using experimental procedures; and a limited exploration of the potential and ethical concerns of GenAI from the lens of cultural dimensions. Promising opportunities for future research are highlighted. [ABSTRACT FROM AUTHOR]

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]

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.

Introduction: With the increasing amount of research around Computational Thinking (CT) and endeavors introducing CT into curricula worldwide, assessing CT at all levels of formal education is of utmost importance to ensure that CT-related learning objectives are met. This has contributed to a progressive increase in the number of validated and reliable CT assessments for K-12, including primary school. Researchers and practitioners are thus required to choose among multiple instruments, often overlapping in their age validity. Methods: In this study, we compare the psychometric properties of two of these instruments: the Beginners' CT test (BCTt), developed for grades 1-6, and the competent CT test (cCTt), validated for grades 3-4. Classical Test Theory and ItemResponse Theory (IRT) were employed on data acquired from 575 students in grades 3-4 to compare the properties of the two instruments and refine the limits of their validity. Results: The findings (i) establish the detailed psychometric properties of the BCTt in grades 3-4 for the first time, and (ii) through a comparison with students from the same country, indicate that the cCTt should be preferred for grades 3-4 as the cCTt is able to discriminate between students of low and medium ability. Conversely, while the BCTt, which is easier, shows a ceiling effect, it is better suited to discriminate between students in the low ability range. For these grades, the BCTt can thus be employed as a screening mechanism to identify low ability students. Discussion: In addition to providing recomendations for use of these instruments, the findings highlight the importance of comparing the psychometric properties of existing assessments, so that researchers and practitioners, including teachers and policy makers involved in digital education curricular reforms, may take informed decisions when selecting assessments. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) offers new and thriving opportunities, but introduces also new risks and ethical issues that should be dealt with. We argue that the introduction of AI contents at schools through practical, hands-on, projects is the way to go to educate conscientious and critical citizens of the future, to awaken vocations among youth people, as well as to foster students' computational thinking skills. However, most existing programming platforms for education lack some of the required educational features to develop a complete understanding of AI. In this paper we present LearningML, a new platform aimed at learning supervised Machine Learning (ML), one of the most successful AI techniques that is in the basis of almost every current AI application. This work describes the main functionalities of the tool and discusses some decisions taken during its design. For its conception, we have taken into account lessons learned from the research literature on introducing AI in school and from the analysis of other educational tools built with the aim to allow learners to use ML. We offer as well some promising results obtained after a preliminary testing pilot workshop. Finally, the next steps in the development of LearningML are presented, focused on the face and instructional validation of the tool.

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. [ABSTRACT FROM AUTHOR]

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