Informatics in Education

Critical thinking is a fundamental skill for 21st-century citizens, and it should be promoted from elementary school and developed in computing education. However, assessing the development of critical thinking in educational contexts presents unique challenges. In this study, a systematic mapping was carried out to investigate how to assess the development of critical thinking, or some of its skills, in K-12 computing teaching. The results indicate that primary studies on the development of critical thinking in K-12 computing education are concentrated in Asian countries, mainly focusing on teaching concepts such as algorithms and programming. Moreover, the studies do not present a fixed set of critical thinking skills assessed, and the skills are selected according to specific teaching and research needs. Most of the studies adopted student self-assessment using instruments that are well-known in the literature for assessing critical thinking. Many studies measured the quality of instruments for their research, obtaining favorable results and demonstrating consistency. However, the research points to a need for more diversity in assessment methods beyond student self-assessment. The findings suggest a need for more comprehensive and diverse critical thinking assessments in K-12 computing education, covering different educational stages and computing education concepts. This research aims to guide educators and researchers in developing more effective critical thinking assessments for K-12 computing education.

The insertion of Machine Learning (ML) in everyday life demonstrates the importance of popularizing an understanding of ML already in school. Accompanying this trend arises the need to assess the students’ learning. Yet, so far, few assessments have been proposed, most lacking an evaluation. Therefore, we evaluate the reliability and validity of an automated assessment of the students’ learning of an image classification model created as a learning outcome of the “ML for All!” course. Results based on data collected from 240 students indicate that the assessment can be considered reliable (coefficient Omega = 0.834/Cronbach's alpha α=0.83). We also identified moderate to strong convergent and discriminant validity based on the polychoric correlation matrix. Factor analyses indicate two underlying factors “Data Management and Model Training” and “Performance Interpretation”, completing each other. These results can guide the improvement of assessments, as well as the decision on the application of this model in order to support ML education as part of a comprehensive assessment.

In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students’ perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students’ answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.

Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in accordance to the specific characteristics of these students in order to help them to achieve the learning goals. Aiming at bringing computing education to all middle and high-school students, we performed a systematic literature review, in order to analyze the content, pedagogy, technology, as well as the main findings of instructional units that teach computing in this context. First results show that these students are able to learn computing, including concepts ranging from algorithms and programming languages to artificial intelligence. Difficulties are mainly linked to the lack of infrastructure and the lack of pre-existing knowledge in using IT as well as creating computing artifacts. Solutions include centralized teaching in assistive centers as well as a stronger emphasis on unplugged strategies. However, there seems to be a lack of more research on teaching computing to students from a low socio-economic status background, unlocking their potential as well to foster their participation in an increasing IT market.

Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.

In today’s society, creativity plays a key role, emphasizing the importance of its development in K-12 education. Computing education may be an alternative for students to extend their creativity by solving problems and creating computational artifacts. Yet, there is little systematic evidence available to support this claim, also due to the lack of assessment models. This article presents SCORE, a model for the assessment of creativity in the context of computing education in K-12. Based on a mapping study, the model and a self-assessment questionnaire are systematically developed. The evaluation, based on 76 responses from K-12 students, indicates a high internal reliability (Cronbach’s alpha = 0.961) and confirmed the validity of the instrument suggesting only the exclusion of 3 items that do not seem to be measuring the concept. As such, the model represents a first step aiming at the systematic improvement of teaching creativity as part of computing education.

Although Machine Learning (ML) has already become part of our daily lives, few are familiar with this technology. Thus, in order to help students to understand ML, its potential, and limitations and to empower them to become creators of intelligent solutions, diverse courses for teaching ML in K-12 have emerged. Yet, a question less considered is how to assess the learning of ML. Therefore, we performed a systematic mapping identifying 27 instructional units, which also present a quantitative assessment of the students’ learning. The simplest assessments range from quizzes to performance-based assessments assessing the learning of basic ML concepts, approaches, and in some cases ethical issues and the impact of ML on lower cognitive levels. Feedback is mostly limited to the indication of the correctness of the answers and only a few assessments are automated. These results indicate a need for more rigorous and comprehensive research in this area.

Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early to ML concepts helping students to understand its potential and limits. Thus, in order to obtain an overview of the state of the art on teaching Machine Learning concepts in elementary to high school, we carried out a systematic mapping study. We identified 30 instructional units mostly focusing on ML basics and neural networks. Considering the complexity of ML concepts, several instructional units cover only the most accessible processes, such as data management or present model learning and testing on an abstract level black-boxing some of the underlying ML processes. Results demonstrate that teaching ML in school can increase understanding and interest in this knowledge area as well as contextualize ML concepts through their societal impact.

Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.