Informatics in Education

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.

This study reports the findings of a program that aims to develop pre-service science teachers’ computational problem-solving skills and views on using information and communications technology in science education. To this end, pre-service science teachers were trained on computational thinking, computational problem solving, designing an algorithm, and Python coding, and then they were asked to solve problem situations determined within the science education program using the computational problem-solving process. The study was conducted in a faculty of education in Turkey and carried out conducted in an elective course in the spring semester of the 2019 - 2020 academic year (in an online platform due to the Covid-19 Pandemic). 38 pre-service science teachers were included in the study. In this process, pre-service science teachers’ conceptual development levels regarding computational thinking and their views regarding the use of ICT in schools were collected quantitatively. The development of computational problem-solving skills of pre-service science teachers was scored by a rubric developed in this study. According to the analyzes, pre-service science teachers increased knowledge of computational thinking (t = -5,969, p = .000), enhanced views regarding the use of ICT in schools (t = -2,436, p = .020), and developed computational problem-solving skills (χ2(2) = 9.000, p = 0,011). These findings have the potential to provide evidence on how computational problem-solving skills can be integrated into science teacher education programs.

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.

The purpose of this study was to investigate the effect of digital concept cartoons and maps in eliminating misconceptions of secondary school students. The research was conducted with 67 students who were studying at three different branches of 7th grade of secondary school. The research was conducted according to semi-experimental design with pre-test, post-test control group, and quantitative and qualitative research methods (mixed pattern) were used together. Accordingly, the mathematics classes in the Study Group I were conducted by the DCC method and the mathematics courses in the Study Group II were conducted by the DCM, and the mathematics courses in the control group were processed by traditional teaching method. In order to determine the students’ misconceptions before and after the experiment, Misconception Test was used which was applied as Pre-test and Post-test. In addition, students’ opinions and observation processes related to the use of DCC and DCM in mathematics class were included in the experimental process. As a result of the data analysis, there was no statistically significant difference between Study Group I, Study Group II and control group when the results of the Misconception test of the control and study groups were compared. In addition, students stated that the use of DCC and DCM in mathematics course have advantages such as making the courses enjoyable, drawing attention, increasing interest in the course, and visualizing the course topics. In the direction of the findings obtained from the research, various suggestions were made to the teachers and researchers about the use of DCC and DCM in secondary school mathematics courses.

With the growing search for qualified professionals in the exact area, teaching in STEM (Science, Technology, Engineering, and Mathematics) areas is gaining importance. In parallel, it appears that drones are an increasingly present reality in the civil area; however, there are few scientific studies of their application in the pedagogical environment, and their insertion is still practically nil in the school environment. Thus, this work aims to analyze the feasibility of using a set of technologies based on drones, designed based on the theory of significant learning through the use of active methodologies. The study was carried out with 30 high school students and followed a line of quali-quantitative analysis, in which the quantitative data were collected from the results obtained in a pre and post-test and the qualitative ones through recordings during the interventions, observations of the researcher, and a semi-structured press interview. Finally, a triangulation between the methodologies was carried out, looking for congruent aspects between the different techniques used. As a result, it was found that the workshops with the platform based on drones helped in the understanding, construction, and interpretation of the content covered, and it can be concluded that there is a significant relationship between the use of the technological set proposed in the pedagogical process and the possibility of significant learning in the STEM areas by the students.

Research trends on computational thinking (CT) and its learning strategies are showing an increase. The strategies are varying, for example is using games to provide enjoyment, engagement, and experience. To improve the high level of immersion and presence of game objects, learning strategies through games can be improved by virtual reality (VR) technology and its application. However, a systematic review that specifically discusses game based in VR (GBiVR) settings is lacking. This paper reports previous studies systematically about the strategies used to learn CT through games and VR applications. 15 papers were selected through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. As the result, this study proposes a conceptual framework for designing a strategy to learn CT through GBiVR settings. The framework consists of critical aspects of variables that can be considered in the learning environment like game elements, VR features, and CT skills. All the aspects are discussed below.

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies’ application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students’ abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

Nowadays, solving problems is substantial for the social relationship human. Computational Thinking (CT) emerges as an interdisciplinary thought process encompassing mental abilities to help students solve and understand problems. Researchers invest in the methodological proposal of activities aimed at CT stimulation, educational approaches, and the conception of technologies that support these activities’ execution. Educational Robotics (ER) is one of these technologies that stand out at different educational levels to favor teamwork, logical thinking, and creativity, skills intimately articulated with the computing paradigm. The main objective of this work is to investigate the impact of ER activities on CT development and subjects learning in the Technical and Vocational Education in High School. For this, we accomplished a study of intervention research type with students and teachers analyzing quantitative and qualitative aspects. The results indicate that the introduction of ER can favor students in the development of CT skills and learning High School subjects.

Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs, and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

This paper proposes and validates a short and simple Expectancy-Value-Cost scale, called EVC Light. The scale measures the motivation of students in computing courses, allowing the easy and weekly application across a course. One of the factors related directly to the high rate of failure and dropout in computing courses is student motivation. However, measuring motivation is complex, there are several scales already carried out to do that job, but only a few of them consider the longitudinal follow-up of motivation throughout the courses. The EVC Light was applied to 245 undergraduate students from four universities. The Omega coefficient, scale items intercorrelation, item-total correlation, and factor analysis are used to validate and measure the reliability of the instrument. Confirmatory and exploratory factor analyses supported the structure, consistency, and validity of the EVC Light scale. Moreover, a significant relationship between motivation and student results was identified, based mainly on the Expectancy and Cost factors.