Informatics in Education

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.

Knowledge about Machine Learning is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in Brazil. Results show that these students can understand how ML works and execute the main steps of a human-centered process for developing an image classification model. No substantial differences regarding class periods, educational stage, and sex assigned at birth were observed. The course was perceived as fun and motivating, especially to girls. Despite the limitations in this context, the results show that they can be overcome. Mitigating solutions involve partnerships between social institutions and university, an adapted pedagogical approach as well as increased on-by-one assistance. These findings can be used to guide course designs for teaching ML in the context of underprivileged students from a low socio-economic status background and thus contribute to the inclusion of these students.

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.

Background: Petri nets are a formal specification technique for modelling of control processes and modern flexible manufacturing systems. Interpreted Petri nets take into account input and output signals, allowing to apply them in any control system or even in control part of a cyber-physical system. Due to the fact that Petri nets are not used in the industrial practice, the students sometimes lack motivation to learn them. Contributions: In the paper we propose how to help students learn interpreted Petri nets with Minecraft (as a game-based learning). We show how interpreted Petri nets can be modelled in Minecraft and how they communicate with the surrounding environment via input and output signals to visualize control processes. The proposed approach has been validated experimentally among university students. Hypotheses: (1) Creating interpreted Petri net models with Minecraft helps to understand the basic principles; (2) Minecraft makes the course more attractive. Methodology: Students were divided into an experimental group (with game-based learning) and a control group (with traditional learning). The experimental group filled in a knowledge test twice (on the entry and on the exit) and a questionnaire. The control group filled in the same knowledge test at the end of the course. Findings: The observations confirm that the Minecraft-based teaching of interpreted Petri nets allows to gain better results in final tests, making at the same time the course more attractive and enjoyable.

Computational thinking (CT) has been introduced in primary schools worldwide. However, rich classroom-based evidence and research on how to assess and support students’ CT through programming are particularly scarce. This empirical study investigates 4th grade students’ (N = 57) CT in a comparatively comprehensive and fine-grained manner by assessing their Scratch projects (N = 325) with a framework that was revised from previous studies to aim towards enhancing CT. The results demonstrate in detail the various coding patterns and code constructs the students programmed in assorted projects throughout a programming course and the extent to which they had conceptual encounters with CT. Notably, the projects indicated CT diversely, and the students altogether encountered dissimilar areas in CT. To target the acquisition of CT broadly, manifold programming activities are necessary to introduce in the classroom. Furthermore, we discuss the possibilities of applying the assessment framework employed herein to support CT education through Scratch in classrooms.

In recent years, Artificial Intelligence (AI) has shown significant progress and its potential is growing. An application area of AI is Natural Language Processing (NLP). Voice assistants incorporate AI by using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporates them in households nowadays. Most common devices with voice assistants are smart speakers and they have just started to be used in schools and universities. The purpose of this paper is to study how voice assistants and smart speakers are used in everyday life and whether there is potential in order for them to be used for educational purposes.

Connecting theory and practice in teaching is sometimes difficult, as it requires expensive or delicate equipment, thus limiting the teacher to giving demonstrations in which students are passive participants. Numerical mathematics, as an applied discipline, should be taught on real world examples. By using inexpensive Arduino hardware, we can create simple experiments that are easily reproduced by students. Furthermore, the experiments generate tangible data, which can be processed numerically. The choice of the software used for numerical processing is also an important issue. We present several exercises in numerical mathematics that are based on experiments in electrical engineering with Arduino, and show how to turn them into motivational examples. We also present our experiences in teaching using the developed exercises, as well as some important points and conclusions, which stem from discussions with the participating students and teachers.

This work presents a systematic review whose objective was to identify heuristics applicable to the evaluation of the usability of educational games. Heuristics are usability engineering methods that aim to detect problems in the use of a system during its development and / or when its interface is in interaction with the user. Therefore, applying heuristics is an essential part of developing digital educational games. Search sources were articles available in all the databases present in the Capes / MEC / Brazil periodicals portal, in the available languages. The descriptors adopted were "educational games", "heuristic" and "usability" in Boolean search in titles, abstracts and keywords, with AND operator, for publications starting in 2014. The inclusion criteria were: (a) articles with a clear description of the methodology used in the usability analysis; (b) studies presenting primary data and (c) articles whose focus corresponds to the investigated question. Two examiners conducted the searches in the databases and a third the evaluation and general review of the data. Initially, 93 articles were identified, of which 19 were repeated, 5 were literature reviews. Of the 69 that remained, 57 were elected as not eligible with only 12 selected for full studies, of which 6 entered the final review. With this review we can deduce that the field of heuristics and usability for educational games is still little explored, with few specific evaluations validated or in the process of validation, requiring greater investment in the area. Through this review, we found at least one heuristic that meets the usability evaluation of educational software: Game User Experience Satisfaction Scale (GUESS).

Amount of educational data has been constantly increasing for years in all domains and kinds of education (formal or informal) and educational activities (teaching, learning, assessment, use of social media and collaboration and so on). Accordingly, Learning Analytics (LA) become a powerful mechanism for supporting learners, instructors, teachers, learning system designers and developers to better understand educational processes and predict learners' needs and performances. In this paper, we analyze the important dimensions and objectives of LA, application possibilities and some challenges to the beneficial exploitation of educational data. The required skills and capabilities that make meaningful use of LA techniques and technologies in this domain are considered and identified. Presented findings can act as a valuable guide for setting up LA services in support of educational practice. Also, they can be used as learner guidance, in quality assurance, curriculum development, and in improving learning process effectiveness and efficiency. Finally, this paper proposes the unavoidable constraints that affect LA technologies in education.

The European Commission Science Hub has been promoting Computational Thinking (CT) as an important 21st century skill or competence. However, "despite the high interest in developing computational thinking among schoolchildren and the large public and private investment in CT initiatives, there are a number of issues and challenges for the integration of CT in the school curricula". On the other hand, the Digital Competence (DC) Framework 2.0 (DigCom) is promoted in the same European Commission Science Hub portal. It shows that both topics have many things in common. Thus, there is the need of research on the relationship between CT and digital competence.