Informatics in Education

According to the United Nations’ sustainable development goals education has a central role and progress has been made to offer a quality educational lifelong learning path to all. Unfortunately, recent crises, namely the pandemic and wars, have hampered progress and a prompt recovery is mandatory. Similarly, OECD recommendations on creating better opportunities for young people1 addressing key areas such as: ensuring relevant knowledge and allowing to develop appropriate skills and competencies; supporting youth in the transition to the labor market; promoting social inclusion. In this regard computing is considered important with a central role both as a discipline “per se” and as a supporting cognitive tool for all knowledge domains. The informatics reference framework for schools (Caspersen, 2022) offers a solid foundation, as does the STEM teaching framework (Tasiopoulou, 2022). Considering the current shortage in computing and information technology professionals and the projected need of a highly skilled workforce with increasing cognitive competencies, the importance of a quality lifelong education, including computing, is considered mandatory. An alliance between the educational system, from school to universities both formal and informal, and the Information Technology (IT) sectors has the potential for a win-win collaboration offering a more focused education with the right mix of foundational competencies and cutting-edge technical skills. Supporting all learners in improving their education by offering both quality content, pedagogies, technologies, and financial support is of highest importance and should be considered central to any organization’s corporate social responsibility agenda. In this respect the guest editors would like to rise a call for action for an even greater collaboration between the whole educational system and etenterprises with the ultimate goal of reducing the number of young people who are neither employed nor in education and training. The work for this special issue has been embraced with the aim to contributing with a grain of sand in this direction.

This study aims to provide a deeper understanding about the Bebras tasks, which is one of the computational thinking (CT) unplugged activities, in terms of age level, task category, and CT skills. Explanatory sequential mixed method was adopted in the study in order to collect data according to the research questions. The participants of the study were 113,653 school students from different age levels. Anonymous data was collected electronically from the Turkey 2019 Bebras challenge. Factor analysis was employed to reveal the construct validity to determine how accurately the tool measured the abstract psychological characteristics of the participants. In addition, the item discrimination index was calculated to measure how discriminating the items in the challenge were. Qualitative data gathered through the national Bebras workshop was analysed according to content analysis. The findings highlighted some interesting points about the implications of the Bebras Challenge for Turkey, which are discussed in detail. Furthermore, common problems of Bebras tasks are identified and possible suggestions for improvement are listed.

Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students’ explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students’ recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.

This study aims to explore the usability of the virtual robotics programming curriculum (VRP-C) for robotics programming teaching. Pre-service computer science (CS) teachers were trained for robotics programming teaching by using VRP-C in a scientific education activity. After training, views of the participants were revealed by using a scale and an evaluation form consisting of open-ended questions. Results show that VRP-C is compatible with the curriculum for robotics programming teaching in schools, and pre-service CS teachers tend to use VRP-C in their courses. They think that VRP-C will be beneficial for robotics programming teaching in terms of content, functionality, and cost. Compatibility, visual design, feedback, time management, fiction, gamification, and cost are the characteristics that increase the usability of VRP-C. VRP-C can be used as an online tool for robotics programming training due to the necessity of transition to distance education because of the COVID-19 pandemic.

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.

Problem-solving and critical thinking are associated with 21st century skills and have gained popularity as computational thinking skills in recent decades. Having such skills has become a must for all ages/grade levels. This study was conducted to examine the effects of grade level, gender, chronotype, and time on computational thinking skills. To this end, the study was designed to follow a longitudinal research model. Participants were 436 secondary school students. Computational thinking test scores were collected from the students at certain time intervals. Results indicate that computational thinking skills are independent of gender, time, and chronotype but differ significantly depending on grade level. The interaction between grade level and time of testing also has a significant impact on computational thinking skills. The difference in grade level can be interpreted as taking an information technologies course increases computational thinking. The results suggest that such courses should be promoted to children at a young age. The joint effect of gender, grade level, and chronotype were not statistically significant and it is recommended to conduct future studies to investigate this result.

The new Croatian Informatics curriculum, which introduces computational thinking concepts into learning outcomes has been put into practice. A computational thinking assessment model reflecting the learning outcomes of the Croatian curriculum was created using an evidence-centered design approach. The possibility of assessing the computational thinking concepts, abstraction, decomposition, and algorithmic thinking, in an actual classroom situation and examples of such assessment is increasingly coming to the forefront of computer science educational research. Precisely for that purpose, the research was conducted. Research data are collected through the test and questionnaire of 407 pupils (10 middle schools, age 12), analysed by exploratory factor analysis and non-parametric tests. Results showed that the presented model was suitable to assess the understanding of the concepts of abstraction and algorithmic thinking, independently of the previous experience with programming languages and pupil's gender, while assessment of decomposition needs more work and improvement, some recommendations are provided. Also, it received positive feedback from pupils and teachers what implicated that such an assessment model could help teachers in building a real-time measurement instrument.

User-centricity and usability are a premise of digitalization, a current trend for business model innovation based on advanced digital technologies. The article addresses a gap in the literature, in which descriptions of the cases of updating university curricula in usability are lacking. This gap also exists in the practice. The study uses the example of a project for revising the content of usability courses at the University of Turku as a case. The research objective is to explore an integrative approach to usability education. For this, we consider the data collected via interviews with the faculty teaching usability subjects. Thematic analysis is applied to examine the interview outcomes. Recommendations as to updating usability curricula are provided.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.

In a previous publication we examined the connections between high-school computer science (CS) and computing higher education. The results were promising—students who were exposed to computing in high school were more likely to take one of the computing disciplines. However, these correlations were not necessarily causal. Possibly those students who took CS courses, and especially high-level CS courses in high school, were already a priori inclined to pursue computing education. This uncertainty led us to pursue the current research. We aimed at finding those factors that induced students to choose CS at high school and later at higher-education institutes. We present quantitative findings obtained from analyzing freshmen computing students' responses to a designated questionnaire. The findings show that not only did high-school CS studies have a major impact on students’ choice whether to study computing in higher education—it may have also improved their view of the discipline.