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.

Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine learning model developed using ongoing formative assessment scores as indicators of student engagement. Visualisation of the classification tree results is employed as student engagement indicators for instructors to observe and intervene with students. The results of this study showed that ongoing assessment is related to student engagement and subsequent final programming exam performance and possible to identify students at-risk of failing the final exam. Finally, our study identified students impacted by the Covid-19 pandemic. These were students who attended the final programming exam in the semester 2019-2020 and who scored well in formative assessments. Based on these results we present a simple student engagement indicator and its potential application as a student progress monitor for early identification of students at risk.

Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students’ explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students’ recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.

Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers’ keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load, impairing their learning. Our study investigates the impact of the data input method on students’ learning and fluency in solving equations using step-based math ITSs. More specifically, we have considered the standard typing and handwriting input methods. We hypothesized that the students would be more fluent using their handwriting with online recognition to solve math equations than using the typing input method. This fluency indicates a reduction in cognitive load, freeing working memory for logical reasoning instead of interface preconditions, leading to improved learning. We have conducted an experiment with 55 seventh-grade students from a private school to validate the hypothesis, randomly assigned to control and experimental groups. Each group used one of the input methods on two different devices (desktop computers and tablets). Although students using handwriting solved more equations and were faster than students who typed their equations, we could not find statistically significant differences in the learning between students that used typing or handwriting. Additionally, we have found that the input method used in a not ideal device (e.g., handwriting with a computer’s mouse instead of using a touch screen device) can negatively affect the students’ performance.

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.

Education 4.0 (E4) aims to improve the teaching-learning process and democratize access to quality education by using Industry 4.0 technologies in educational environments. The main objective of this article is to propose a framework containing a package of policies and initiatives for the drivers of society (industry, government, and academia) to develop E4. The framework was elaborated through systematic review based on good practices, challenges, and opportunities of E4, which were systematized considering the technical-scientific literature and the authors' experience. The main scientific contribution of this work is the creation of a new block of knowledge about E4 that expands and at the same time deepens the existing literature and can support new research and foster initiatives on the subject. Its main applied contribution is to increase access to quality education through the development of E4.

Integrating computational thinking into K-12 Education has been a widely explored topic in recent years. Particularly, effective assessment of computational thinking can support the understanding of how learners develop computational concepts and practices. Aiming to help advance research on this topic, we propose a data-driven approach to assess computational thinking concepts, based on the automatic analysis of data from learners’ computational artifacts. As a proof of concept, the approach was applied to a Massive Open Online Course (MOOC) to investigate the course’s effectiveness as well as to identify points for improvement. The data analyzed consists of over 3300 projects from the course participants, using the Scratch programming language. From that sample, we found patterns in how computational thinking manifests in projects, which can be used as evidence to guide opportunities for improving course design, as well as insights to support further research on the assessment of computational thinking.

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.

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.

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.