This research-to-practice full paper is elaborated from the next problem, 'Augmented Reality's technology can assist in understanding the contents of Chemistry in a more concise way? The Case Study was conducted in a small-town school. The absence of structures, such as teaching laboratories, limited Internet access, or an insufficient number of computers is common in many small cities such as the one chosen. There is also a lack of teachers trained in pedagogical methods that make the student take an interest in learning. On the other hand, most young people and teachers have cell phones and enjoy recreational activities. So, we combine these various aspects to promote learning in a playful, cooperative way and based on a relevant learning theory. This paper is aimed at analyzing learning supported by Augmented Reality (AR) for Chemistry teaching. It was a quantitative study using Augmented Reality with the intent to check the benefits that AR brings to education. We focused on Organic Reactions because many students have difficulties with this topic and we evaluate that this topic is appropriate for using Augmented Reality. First, a pedagogical approach had been discussed with teachers of the proposed discipline and from that, an app for the Android system was developed, to assist in teaching Organic Compound Reactions, uniting Vygotsky concepts on the Proximal Development Zone. The proposal is integrated to the movement based on learning by design in basic schools, STEM, the system promotes students' abilities to work with technology to understand the teaching of Chemistry. Evaluations were carried out with 54 high school students during the semester, and the results obtained on the technology used identified that 84% of students considered the contents of chemistry relevant. A significant part suggested that the approach using Augmented Reality could be used in teaching other subjects. It was possible to observe the learning with cooperative characteristics highlighting the importance of working with this perspective, uniting attractive technologies that can collaborate effectively for the construction of knowledge in school disciplines.

This full paper of innovate-to-practice category presents a Multiagent System for recommending learning objects in Virtual Learning Environments (VLE), aiming to improve the customization of instructional guidance on educational content according to the student's profile. The methodology was initially research aimed at identifying the motivators of the students' performance and their weaknesses, adopting a personalized student model based on the level of knowledge, and providing predictive models to monitor the student's progress in the curriculum. This framework provides a distributed architecture, and consists of three layers: 1) Administrative layer; 2) Storage layer; 3) Pedagogical layer. For the recommendation of learning objects, a collaborative filter was used, which constitutes a successful technique in several recommendation applications, seeking similarities in users' habits to predict their future decisions.

This full paper of research category presents a proposal for a smart recommendation model to personalize the provision of teaching materials in Virtual Learning Environments (VLE), to continually learn from student feedback. This research presents a model that is divided into 4 parts: 1) Student model; 2) Domain model; 3) Mining module; and 4) Recommendation module. This research contributes to focus on the development of cognitive skills related to learning styles, aiming to help students understand the strengths and weaknesses of their cognitive and objective - cognitive strategies, with the construction of behavioral patterns generated from the exchange of messages information between the student and mining modules. It is hoped that with the implementation of e-learning models, this proposal can contribute to assist in guiding the teaching and learning process towards mastering a curriculum, with interactive regulations, and the acquisition of the corresponding skills through the discovery of knowledge and automation of the recommendation process. Besides, contributing to self-esteem and, mainly, helps to train professionals better prepared for the job market. Our focus is to describe complex processing of recommendations in e-learning environments in terms of knowledge, not the details of its implementation.

This Innovative Practice Category Full Paper presents the proposal of a model to develop adaptive educational resources, with the aim to promote robotic teaching in STEM (Science, Technology, Engineering, and Mathematics) courses. This model was designed to be integrated with a Learning Management System, to suggest customized activities and teaching materials according to students' learning cognitive styles, and it is extensible, can be applied in others areas, not limited to robotics. This model aims at stimulating the teaching programming with robots, providing an environment that students can remotely program robots through a programming language, monitor their evolution in the environment and evaluate the activities that have been suggested. A feasibility study was realized with 6 professors to validate this proposal, and the UTAUT model was applied with 24 students, to validate the acceptance model study.

This full paper of research to practice category describes an experiment using an Intelligent Tutoring System to support teaching and learning that evolve solutions of first-degree equations. The difficulties encountered in solving algebra problems can make students consider math and related science a difficult task. We propose a Tutor to promote the learning of this class of problems. In this article, we describe the functioning and architecture of the tutor composed by the Interface, Student Model, Tutorial Strategies and Domain Model. Also, a Virtual Assistant was included to motivate students in the learning process. The tutoring strategies that comprise the tutor are projected from models found in the literature. To approximate the interactions of the tutor with the actions of the teachers, Lev Vygotsky's Concept of Zone Proximal Development was used. The methodology used in this project was the case study applied in a real-life context, obtaining a combination of quantitative and qualitative evidence about technical and pedagogical usability that show the degree of satisfaction of the students regarding the proposed approach.

This Full Paper of Research Practice Category presents a proposal for the mediation of learning through a remote access experiment. The proposal is targeted at Physics students at Secondary School and provides students with a reflective experience about basic kinematics concepts from the actual execution of experiments. The mediation experience will allow interaction between students and teacher, especially when there is a need for student orientation in experimental activities. The project is based on Vygotsky's ideas on learning with mediation, support and the Zone of Proximal Development. A feasibility study was conducted with a group of experienced Physics teachers, and a prototype was implemented.

This full paper of research category presents a Systematic Mapping Study of the state-of-the-art of research on adaptation content in Robotic Systems, in the fields of cognitive profile, learning style and student model. In general, teachers develop the material that is to be taught and organize pedagogical activities for the students. As a consequence, students have a learning environment that does not take into account activities that have already been completed, actions taken, learning preferences and cognitive profile. Activities and teaching materials in robotic environments are the same for all students, and may end up being insufficient for the effective learning of each one. The initial search of four main academic databases resulted in 137 papers out of which 15 papers were included in the final analysis. Our article presents different researches, which involve adapting content for learning in robotic environments.

This paper presents the development of a Robotics and Programming Laboratory with the support of Multiagent Systems (MAS) to help users learn programming skills using robots. The developed laboratory is available in a Learning Management System (LMS) allowing people from geographically distant locations to gain experience with experiments of this nature. The choice of Robotics Education (RE) as object of scientific experimentation linked to teaching and learning systems has shown to bring significant improvement in the learning process. The system makes available on the Internet a space where it is possible to practice programming using Lego Mindstorms robots in the Python programming language. The system suggests several challenges based on the score and profile of users. Its features were tested with promising results, where users have felt very interested and motivated to continue learning programming and robotics. The work developed presents advances over remote robotics laboratories found in the literature, since it allows the robots to be remotely programmed through the programming language NXT-Python, and not only by using command arrows.

A frequent concern among students and teachers working with educational robotics is about the results gathered through practical activities. Currently, educational institutions and robotics competitions lack of mechanisms that can evaluate the robot's behavior during the accomplishment of the proposed challenges. In this work, we propose the development of a technological solution using Computational Vision, whose purpose is to monitor and evaluate the performance of mobile robots during the execution of Robotics Pedagogic tasks. The developed system, named MonitoRE — Monitoring System for Educational Robotics, was tested on three categories of pedagogic robotics environments, obtaining promising results. Our tool uses an Absolute Location Method with descriptors based on color and shape to analyze the task environments, mapping the path taken by the robot, evaluating the achievements in the proposed tasks. The experiments carried out indicate that the adopted method is effective, performing satisfactory results in robotic monitoring. In addition, it was found that teachers and students felt more motivated, demonstrating interest in using monitored task environments, because it ease the understanding of the difficulties faced by the moving robot in completing the activities, assisting students in the teaching-learning process.