Informatics in Education

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.

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.

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.

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.

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.

The contents taught in the programming subjects have a great relevance in the formation of computing students. However, these subjects are characterized by high failure rates, as they require logical reasoning and mathematical knowledge. Thus, establishing knowledge through the subject of algorithms can help students to overcome these difficulties and absorb the contents and skills required. Thus, this work aims to present and discuss the results of a second experiment on the application of a teaching plan composed of several active methodologies (Virtual Learning Environments, Coding Dojo, Gamification, Problem-Based Learning, Flipped Classroom and Serious Games) in an algorithms subject. Based on this experiment, it was evaluated whether there were learning gains compared to the learning acquired with the traditional method. Finally, an analysis was performed using the two-tailed Student-t approach, used for independent samples, which presented statistically significant results.