Informatics in Education

In Education 4.0, a personalized learning process is expected, and that students are the protagonist. In this new education format, it is necessary to prepare students with the skills and competencies of the 21st-Century, such as teamwork, creativity, and autonomy. One of the ways to develop skills and competencies in students can be through block programming, which can be used with emerging technologies such as robotics and IoT and in an interdisciplinary way. Thus, block programming in High School is important because it is possible to work on aspects such as problem-solving, algorithmic thinking, among other skills (Perin et al., 2021), which are necessary in the contemporary world. Thus, our Systematic Mapping Study (SMS) aims to identify which block programming tools support of Education 4.0 in High School. Overall, 46 papers were selected, and data were extracted. Based on the results, a total of 24 identified block programming tools that can be used in high school collaboratively and playfully and with an interdisciplinary methodology. Moreover, it was possible to see that most studies address block programming with high school students, demonstrating a lack of studies that address block programming with teachers. This SMS contributed to identifying block programming tools, emerging technologies, audience (teacher or student), and learning spaces where block programming is being worked on.

Computing science which focuses on computational thinking, has been a compulsory subject in the Thai science curriculum since 2018. This study is an initial program to explore how and to what extend computing science that focused on STEM education learning approach can develop pre-service teachers' computational thinking. The online STEM-based activity-Computing Science Teacher Training (CSTT) Program was developed into a two-day course. The computational thinking test (CTT) data indicated pre-service teachers’ fundamental skills of computational thinking: decomposition, algorithms, pattern recognition, pattern generalization and abstractions. The post-test mean score was higher than the pre-test mean score from 9.27 to 10.9 or 13.58 percentage change. The content analysis indicated that there were five key characteristics founded in the online training program comprised: (1) technical support such as online meeting program, equipment, trainer ICT skills (2) learning management system such as Google Classroom, creating classroom section in code.org (3) the link among policy, curriculum and implementation (4) pre-service teachers' participation and (5) rigor and relevance of how to integrate the applications of computing science into the classroom.

The Computational Thinking (CT) teaching approach allows students to practice problem-solving in a way that they can use the Computer Science mindset. In this sense, Collaborative Learning has a lot to contribute to educational activities involving the CT. This article presents the design and evaluation of a Collaborative Learning framework for the development of CT skills in students. To design the proposed strategy, several fundamental features of the Collaborative Learning concept of the literature have been studied and sketched. The strategy was applied to middle school students through a digital games programming workshop. Data were collected by three means: (1) collecting artifacts produced during activities; (2) recording of game programming sessions; and (3) applying a structured interview to students. The data analysis showed evidence that the strategy was able to mobilize Computational Thinking skills in addition to mobilizing collaborative skills in learners.

The purpose of the study is to examine the effect of unplugged coding activities carried out with middle school students on their computational thinking skills. This study employed nested-mixed design, which is a mixed research method; the data were supported by including the qualitative phase into an experimental study. In this frame, a group of 114 middle school students consisting of 5th graders were given coding training titled "Kesfet Project - I Discover Coding" by using unplugged coding content. The Computational Thinking Scale was applied to the students at the beginning and end of the training; the results obtained from the scale were analyzed by means of a paired t test. Finally, it was found out that unplugged coding activities had a positive effect on the improvement of computational thinking skills of the students. An examination of the sub-factors revealed that there is statistically no significant change in the problem solving skill despite the positive impact observed on creativity, algorithmic thinking, collaboration and critical thinking skills. Following the analysis of observation and daily data, the findings obtained revealed that the students usually displayed high levels of motivation and class participation in unplugged coding activities, they had difficulty in concretizing certain concepts as well as subjects requiring mathematical knowledge; various teaching methods and techniques were used in classes; the students liked the activities especially due to their appealing nature and their relation to daily life; however, there were occasional problems with scheduling of activities and teamwork due to over-crowded class size; the students experienced problems in achieving outcomes such as perceiving the relationship between computer science and mathematics and analyzing the given problem, and could have difficulty in associating between computer science and mathematics or between the subjects learned and the computer lesson, and in analyzing a given problem.

Information and communications technologies today are used in virtually any university course when students prepare their papers. ICT is also needed after people are graduated from university and enter the job market. This author is an instructor in the field of informatics related to health care and social sciences at the Riga Stradins University. In practice, he has found that after completing informatics courses (IC) at the university level, students and practicing specialists at various levels find it hard to decide on what data processing method to use in order to interpret extracted results in the relevant area of specialisation. There are various data processing methods in the literature, presented individually and without adequate linkages. The author has found in practice that when such assignments are handled, there is closer linkage among data processing methods than the literature would suggest.