Informatics in Education

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.

Diverse initiatives have emerged to popularize the teaching of computing in K-12 mainly through programming. This, however, may not cover other important core computing competencies, such as Software Engineering (SE). Thus, in order to obtain an overview of the state of the art and practice of teaching SE competences in K-12, we carried out a systematic mapping study. We identified 17 instructional units mostly adopting the waterfall model or agile methodologies focusing on the main phases of the software process. However, there seems to be a lack of details hindering large-scope adoption of these instructional units. Many articles also do not report how the units have been developed and/or evaluated. However, results demonstrating both the viability and the positive contribution of initiating SE education already in K-12, indicate a need for further research in order to improve computing education in schools contributing to the popularization of SE competencies.

The objective of this article is to present the development and evaluation of dETECT (Evaluating TEaching CompuTing), a model for the evaluation of the quality of instructional units for teaching computing in middle school based on the students' perception collected through a measurement instrument. The dETECT model was systematically developed and evaluated based on data collected from 16 case studies in 13 different middle school institutions with responses from 477 students. Our results indicate that the dETECT model is acceptable in terms of reliability (Cronbach's alpha ?=.787) and construct validity, demonstrating an acceptable degree of correlation found between almost all items of the dETECT measurement instrument. These results allow researchers and instructors to rely on the dETECT model in order to evaluate instructional units and, thus, contribute to their improvement and to direct an effective and efficient adoption of teaching computing in middle school.

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.

Despite the fact that digital technologies are more and more used in the learning and education process, there is still lack of professional evaluation tools capable of assessing the quality of used digital teaching aids in a comprehensive and objective manner. Construction of the Comprehensive Evaluation of Electronic Learning Tools and Educational Software (CEELTES) tool was preceded by several surveys and knowledge obtained in the course of creation of digital learning and teaching aids and implementation thereof in the teaching process. The evaluation tool as such consists of sets (catalogues) of criteria divided into four separately assessed areas - the area of technical, technological and user attributes; the area of criteria evaluating the content, operation, information structuring and processing; the area of criteria evaluating the information processing in terms of learning, recognition, and education needs; and, finally, the area of criteria evaluating the psychological and pedagogical aspects of a digital product. The specified areas are assessed independently, separately, by a specialist in the given science discipline. The final evaluation of the assessed digital product objectifies (quantifies) the overall rate of appropriateness of inclusion of a particular digital teaching aid in the teaching process.