Informatics in Education

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.

Interfaces with good usability help their users complete more tasks in less time and with less effort, which gives them greater satisfaction. Given the vast array of options available to users today, usability is an important interface feature that may lead to the commercial success or failure of a software system. Despite its importance, few educational tools are available to help usability teachers and students. Knowing how to measure interface usability is one of the basic concepts that students should learn when they study the theme. This paper presents UsabilityZero, a web application to support the teaching of usability concepts to undergraduate students. By using UsabilityZero, students interact with a system displaying a reduced usability interface and, later, with the same system exhibiting an increased usability interface. Considering the use of UsabilityZero by 64 students, the differences between the interface with reduced and increased usability were: (i) 61.5% decrease in the number of clicks; (ii) 62.2% decrease in the time to perform tasks; (iii) 92.9% effectiveness increase; and (iv) a 277.3% satisfaction increase. During their experience with UsabilityZero, students learn how to measure efficiency, effectiveness, and satisfaction of user interfaces. After using the application, Information Systems and Computer Science students who had never been in touch with the subject could identify key usability aspects. The students’ perception of efficiency, effectiveness, and satisfaction as usability measures was higher than 80%. Also, they could identify some usability criteria and understand how measurements change when some of them are present in the interface design. As a result, over 92% of these students said they recognized the importance of usability to the quality of a software product, and 79% declared that their experience with the application would contribute to their professional lives.

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.

Programming is one of the basic subjects in most informatics, computer science mathematics and technical faculties' curricula. Integrated overview of the models for teaching programming, problems in teaching and suggested solutions were presented in this paper. Research covered current state of 1019 programming subjects in 715 study programmes at total of 218 faculties and 143 universities in 35 European countries that were analyzed. It was concluded that while most of the programmes highly support object-oriented paradigm of programming, introductory programming subjects are mainly based on imperative paradigm.