This research-to-practice full paper presents a comprehensive and objective account of a study that collates, analyses, and synthesizes all available evidence on a specific research question. In light of the growing demand for inclusive solutions for the deaf community, driven by the impact of the global pandemic, technologies such as Speech-to-Text (STT) and Natural Language Processing (NLP) assume a pivotal role in promoting accessibility and effective communication. A systematic literature review was conducted to analyze tools that utilize natural language processing (NLP) and speech-to-text (STT) to translate Portuguese into Brazilian Sign Language (Libras) and evaluate their effectiveness. Some tools were identified, including OpenSigns, Hand Talk, and Virtual Sign, each of which exhibited certain limitations and challenges. To evaluate the quality of the translations, metrics such as Bilingual Evaluation Understudy (BLEU) score, Word Error Rate (WER), and Character Error Rate (CER) were employed. Furthermore, the potential of the Transformer and LightConv models as promising avenues for enhancing the efficiency and accuracy of translations was under-scored. Nevertheless, considerable challenges persist, including the adaptation of cultural and grammatical nuances and the expansion of the character vocabulary to elevate translation quality.

This innovative practice full paper focuses on developing a Portuguese-to-Libras translation model designed to facilitate the participation of deaf students in academic conferences in the absence of sign language interpreters. The pursuit of remote solutions, such as virtual translators for simultaneous interpretation from Portuguese to Libras, is intended to facilitate the inclusion of deaf students in higher education during the pandemic, despite the presence of bureaucratic barriers and interpreter shortages. The education of deaf individuals presents a distinctive set of challenges that necessitate the development of innovative technological and methodological approaches to enhance inclusion and promote academic success. Some studies have investigated these issues and put forward proposals for improving the educational experience of deaf individuals. The overarching objective of the research was to apply natural language processing (NLP) techniques, utilizing the Transformer deep learning neural network architecture, to translate Brazilian Portuguese into Libras, thereby facilitating real-time translation and emulating the actions of human interpreters through a virtual character. The specific objectives were to create a dataset comprising Portuguese sentences and their corresponding Libras representations, develop a speech-to-text algorithm, identify and adapt suitable models for translation, and evaluate the performance of translations and the integration of speech-to-text with the NLP model. These objectives were established to ensure accuracy and efficacy in translation and system integration. This research project entailed the construction of an automated translation model from Portuguese to Libras, based on the T5 architecture from Hugging Face. Additionally, a speech recognition system was developed to transcribe audio into text. The translation accuracy, evaluated using the BLEU metric, was 75.32%, which was compared with the glossed sentences in Libras used as the linguistic corpus. However, the model encountered challenges with diacritics and specific vocabulary. The simultaneous interpreter, which combined speech-to-text with the NLP model, demonstrated promising results in real-time translations to Libras.

This full paper describes a complete article in the research category. This work presents a Bibliometric Review that aims to characterize the current scenario of academic research in Artificial Intelligence in education to support educators in the teaching and learning processes in Special Education in different contexts, becoming a tool with potential use in cases of students with Autism Spectrum Disorder (ASD). In this sense, this work aims to contribute to a better understanding of research associated with the area of Artificial Intelligence aimed at educational processes involving students with autism. To do this, we used the EndNote reference manager, an online tool designed to support researchers in conducting Literature Reviews, following the bibliometric protocol proposed by Guedes and Borschiver (2005). From the definition of search strings, (“artificial intelligence”) AND (“autism” OR “ASD” OR “autism spectrum disorder”) AND (“education” OR “teaching”), scientific databases were explored like Web of Science (WoS), Scopus, ERIC, Emerald, Scielo, Portal CAPES, and IEEE, to locate existing studies on the topic, between 2019 and 2024. As a result, 298 articles were mapped and 20 scientific works were selected that address the aforementioned specific theme, written by 89 authors belonging to 50 institutions in 19 countries on four continents, enabling the creation of a significant theoretical basis. By analyzing the information contained in scientific publications in this specific field, it was possible to infer that research is divided into distinct areas of concentration: 1) the exploration of algorithms and data analysis based on Artificial Intelligence for analytical-predictive issues in the field of special education; 2) the use of robots in the teaching and learning processes of students with autism; 3) the development of personalized educational intervention models for learning pedagogical, social and communication skills. The data show that research on the use of Artificial Intelligence in the education of autistic people can still be explored, presenting itself as a promising area for future investigations. The present study facilitates understanding of the current state of knowledge in this area, by identifying gaps in areas that require investigation, thus promoting the development of further research. Finally, the data observed with this research will serve as a basis for the development of future AI projects in the teaching and learning process for autistic students.

This abstract refers to a full article in the research category. The human being is endowed with five senses, which allow him to capture the reality of the world around him. Vision is one of the most important senses of the individual for his development, both cognitive and social, and research shows that a significant portion of the population has some type of visual impairment, from low vision to total blindness. People with some type of disability face numerous difficulties daily, from locomotion, communication, access to information, social rights such as culture, leisure, education, and above all, respect from society, and in this way, Assistive Technology (AT) resources arise to minimize these difficulties, thus enabling better conditions for people in this condition. The article presents a Systematic Literature Review (SLR), which deals with the use of Didactic Resources for the visually impaired, the use of the Internet of Things, and how they can contribute to improving student learning. Through RSL, it was possible to identify the most used Assistive Technology resources and understand the use of these Didactic Resources aimed at visually impaired students. Identifying the use of Digital Technologies integrated with several resources was also possible. To carry out the searches, six databases (National and International) were defined, and the period covered by the research was from 2019 to 2022. The studies considered are annals of events, periodicals, theses, dissertations, and scientific journals with publications in the field of education and/or computing, thus covering the most relevant works in the area. The search string used was ((“Visual impairment” OR Blind) AND ((“Assistive Technology” OR “Didactic Resources”) AND (“Internet of Things” OR IoT))). As a result of the search process, 485 studies were obtained, which applied the inclusion and exclusion criteria, resulting in a total of 27 studies that answered the research questions of this study, making it possible to identify the most used Assistive Technology resources and also to understand the use of Didactic Resources aimed at visually impaired students, in addition to identifying the use of Digital Technologies integrated with several of these resources.

Studies have been indicating the use of digital resources for interventions with children diagnosed with Autism Spectrum Disorder (ASD), especially when it comes to supporting communication and social interaction. However, there is research that suggests the use of digital environments not only in these aspects but also to assist them in their academic knowledge, such as in their mathematical skills, specifically related to geometric thinking. From this context, Active Methodologies point to various ways of rethinking traditional teaching, one of them being gamification, which can be understood as the use of game elements in non-game situations. Gamification has shown significant benefits for this audience, proving to be an effective tool to aid in the development and learning of these individuals. Therefore, this article in its complete form presents an excerpt from a doctoral thesis that seeks to understand the geometric thinking of nine autistic participants through gamified activities in their real mode. The research is divided into two stages: the first stage involves a preliminary survey with five teachers through structured interviews, aiming to investigate the demand for gamified activities for students with ASD, as well as indicating possibilities for such activities. From the Discursive Textual Analysis (DTA) of these interviews, three categories emerged: opportunities, skills, and limitations. As for the second stage, conducted with nine participants, who are students with ASD, interventions were carried out with these activities in their real mode, which will be implemented digitally later. Through the researcher's observation analysis, based on recorded videos of the interventions, three subcategories related to the initial categories can be identified: potential interactions (opportunities); strategies for geometric thinking (skills); and gamification in the real mode (limitations). The first subcategory emerged due to the need to describe how students with ASD interacted both with the researcher and with the activity. Given the need to showcase how the students solved the activities, as well as the strategies they employed to complete the pairings correctly, the second subcategory emerged. Lastly, the third subcategory arises from limitations that were observed during the interventions, which were still in the real mode and should be taken into account when implementing the digital phase. Therefore, it is evident that there are some points to consider for the activity in its digital mode: implementing gamified activities that incorporate elements from the participants' daily lives (ADLs); understanding the strategies employed by the participants to solve gamified activities; being mindful of the weaknesses of the gamified activity in its real mode, allowing for the provision of resources in various formats.

This summary presents a complete article, and its category is research. With the growing number of conversational virtual assistants used in virtual learning environments (VLE), the humanization of these chatbots brings several benefits to their users, such as greater satisfaction with the service and assistance in the teaching-learning process, as shown by several studies cited in the work. For the humanization of these chatbots, it is necessary to classify the texts typed by the users, and for that, the best model must be used to identify the emotion in the message. This article presents tests developed to investigate, using Machine Learning, which classification model obtains the best performance for the proposed problem of classification of feelings. The experiments compared the accuracies between a recurrent neural network (RNN) and the Naive Bayes algorithm. The RNN was developed in Python using the Keras library. The developed model has approximately 300 neurons in the first layer and 128 in the second, totaling 640,000 and 130,000 parameters, respectively. In the third layer, three neurons were applied, leaving a total of 771,971 parameters to be estimated. Multinomial Naive Bayes was used, as it allows a non-binary output; it was implemented using the Scikit-Learn library. The alpha used was 0.001 because, after some adjustments to improve its result, it was the one that presented the best accuracy. Three datasets taken from Twitter were used to perform the tests. It followed (Wilson et al., 2019) as a basis for an acceptable accuracy result, where the minimum value is 85%. The recurrent neural network model presented an accuracy of 86.83% and the Naive Bayes algorithm 76.57%. After performing each experiment, the F1 Score metric was used through the Scikit-Learn library to confirm the results. With the analysis of the three experiments, it was observed that the neural network met the need for classifying feelings satisfactorily. At the same time, the Naive Bayes model did not reach the expected accuracy. The contribution of this article is to implement a model capable of recognizing the emotions present in the text when interacting with the chatbot to offer better service to users.

The deaf community has conquered rights in the area of education and with the entry of deaf people into universities, there is a need to develop new signs in Libras for technical terms in the various areas of knowledge. This paper aims to present iTecDeaf, a technical glossary with a dynamic and bilingual interface for the deaf, based on Vygotsky's theory that emphasized the use of signs and symbols for the cognitive development of the individual. It is characterized by the use of videos, animations in sign languages and fingerspelling in written form, providing relevant and easy-to-understand content for its users. In validation, the predictive evaluation was used to identify problems in the interface and in the design, carried out by 5 experts with knowledge about the deaf public in which a checklist of 10 questions was applied as an evaluative instrument. iTecDeaf showed a positive result of 86% and proved to be a learning mediator instrument that enables signs through a differentiated interface with the perspective of contributing to the academic activities of deaf students, interpreters and teachers.

This Innovative Practice of a Full Paper presents new ways of teaching classes. The teaching methodology of the algorithm disciplines of the first years of Computer Engineering courses directly affects student performance throughout the course. The current education system is in transition, taking slow steps in new ways of learning. By promoting self-reflection and critical thinking, students can develop problem-solving skills and observe an application of the tools learned. A proposed methodology includes new structuring of the discipline "Algorithms and Data Structures I" at the Universidade Federal do Rio Grande (FURG), with the learning of a modern programming language and the transformation of the classic classroom model to new methods and dynamics. Computational thinking is fundamental at the beginning of the course. From this, all students receive a introduction course at the beginning of the college year to immerse in dynamics to practice logic. Classes began, students received an extension to the basic learning of the discipline (the fundamentals of algorithms and Python as a modern programming language) and at the same time they received an assignment, a problem resolution, where they had space and time to develop a game with the theme of their choice. The activity requires teamwork and creativity. As lectures run as workshops, allowing collaboration and project creation, with the help of teachers and older students as tutors. To complete the program, an event made available by the university was used as an environment, for visitors, scholars as well for high school juniors interested in software, to attend and learn about. As a preliminary result of the new implementation methodology, after 4 years, there was an increase in the approval and average grade of learners.

This is a work in progress and the category is research. This paper presents a systematic review on the usage of agile methodologies in educational environments. During history, multiple strategies were created in order to solve the necessity of helping students on building their knowledge. The arrival of new technologies opens new possibilities due to the easy access to information, and this fact transforms student's profiles worldwide. In this context, it is important to find new ways to assist learners through the journey of learning while improving their motivation. Recently, the application of agile project management methodologies, such as Scrum and Extreme Programming, has emerged as an option to be adopted inside the classroom aiming to improve learning. Due to their capabilities of flexibilization of student's self-regulation, these methodologies have become an interesting option in an educational environment hence their high adaptability to requirements changes that may occur during the time. In other words, new requirements can be perceived by the teacher, for instance, learning a new tool that was not planned on before, and, therefore, the class plan can be modified to accommodate this new necessity. Through the mapping of the literature it was possible to obtain a knowledge base on the subject and to understand the current state of the research on the topic.

This Research Full Paper presents the design and evaluation of an auditory interface that allows blind people to perceive workspace awareness elements in a shared environment for modeling diagrams. Approximately 36 million Brazilians are visually impaired, where half a million are blind, and the enrollment of the blind at Brazilian higher public education institutions has been increasing. However, accessible tools for diagram authoring are scarce, despite their necessity on STEM courses. There are studies and new developments regarding the perception and authoring of diagrammatic information, but they do not consider collaborative features. Therefore, this study proposes the sonification of workspace awareness elements as a solution to enable accessible groupware features for the blind. It was designed auditory cues for three awareness categories (who, what, and where) using abstract sounds and favoring suitable metaphors for an intuitive mapping. The designed auditory cues were implemented in a prototype application for collaborative diagram modeling and evaluated through an objective usability test. The results demonstrated that participants could reasonably distinguish who made what and where. Besides, it was possible to identify the awareness categories which need more work.

This abstract is related to a work in progress and the category is research. The continuous search for methods to spur students' motivation and concentration at the diverse array of academic study fields has brought the need for innovations in the educational environment, in this context, it emerges the gamification concept. This work aims to identify the concept of gamification and its applicability specifically geared towards Engineering courses. To achieve this goal, a bibliographic review was carried out in two stages. The first one looked for studies that addressed the definition of techniques based on games in general, their relation with the teaching process and possibilities of use in educational environments, whether virtual or not. In a second moment, the work listed several job prospects of gamification directed specifically to the Engineering context. For this purpose, several repositories were mapped and 38 scientific papers were selected. Through the mapping of the literature it was possible to obtain a knowledge base on the subject and, from this, to trace the potentialities not yet fully explored in which one can glimpse a field for the continuity of the research.

The deaf have conquered rights in the area of education and, in recent years, with the increase of the enrollment of deaf people in higher education institutions, there is a need to develop new signs in Brazilian Sign Language (LIBRAS) for specific technical terms of several areas of knowledge and related to computing this scenario is no different. Motivated by the entrance of a deaf student in Information Systems undergraduate program of a Public Institution located in the south of Brazil, this work aims to elaborate technical signals of the Information System courses in LIBRAS and to develop a glossary with an interface adapted for the deaf. The construction of this repository of signs in LIBRAS is based on Vygotsky, who worked on defectology and emphasized the use of signs and symbols for the individual's cognitive development. The deaf student used the glossary in the classroom, which allowed a better understanding of the contents presented. Pedagogical evaluation was performed through descriptive analysis and data collection using the intensive direct observation method and questionnaires. The computational glossary in deaf education presents the potential to develop the capacities that are deprived by the existing communication difficulties, allowing interaction between deaf students, teachers and interpreters.

This Research to Practice Full Paper introduces an approach for the early identification of students at risk of dropping out of their distance learning courses. Students dropout in university courses have long been a major issue leading to social, academic and financial impacts. Predictive modelling analysis has been used as a tool to identify at an early stage probable cases of dropout. In distance learning, this type of analysis is made possible mostly because of the increasing adoption rate of Virtual Learning Environments (VLEs) where data on the student academic activities can be continuously recorded. This paper discusses the design and validation of a Learning Analytics system for early identification of students at risk of dropping out. The case study presented relies on data collected from two postgraduate courses as part of Brazils Open University. The methodology for the system design and validation comprises the steps to build an intelligent data pipeline. Jupyter Notebooks have been d used as the data science analysis environment in order to create data pipelines and have their performance evaluated. Results obtained from the validation of models built out of eight machine learning techniques show an average accuracy of 84% among the set of ML techniques tested. The highest accuracy is delivered by the Extra Trees classifier with 88%. Logistic Regression performed the worst performance with an accuracy of 79%.

This complete research work presents the use of chatbot in the educational context for automation of care to the student of higher education. Due to the evolution of technologies in educational and professional contexts, new teaching/learning methods have been an emerging demand for today's society. The expansion of learning modalities such as face-to-face, combined and distance learning as well as demand anywhere, at any time, has been a premise in these education systems. With the advancement of virtual learning environments (VLEs), successful interaction depends on solutions that enable efficient communication between the student and the educational context. One way to achieve this goal has been to use chatbots to respond to students' questions in the educational field. This has become possible through artificial intelligence and natural language processing embodied in simple service platforms. The purpose of this article is to develop a chatbot that interacts with students through text messages, on subjects in a closed context, to students' doubts about the Higher Education Institution Course. To validate chatbot performance, it will be submitted to a set of questions of the same context that were not correctly answered in the previous interactions. The results of these interactions will be analyzed in order to verify the efficiency and contribution of a chatbot as a support tool for the student. It is hoped that this study contributes to solutions for assistance automation in the learning context.

This abstract refers to a full paper in the research category. This paper presents a Systematic Review (SR) that aims at characterising the current scenario of Educational Recommendation Systems (ERS). ERS has recently attracted the attention of researchers in developing methods that contribute to the educational environment. Recommendation systems capable of searching and selecting relevant contents are one of the core technological resources offered to the education sector. Such systems which were initially designed for commercial applications (e.g. e-commerce) have now been used in educational application-driven research. For instance, a teacher might rethink methodologies and practises tailored to a set of pedagogical needs. These resources are enablers for the tools development that contribute to better educational practices. The goal of this SR is to provide an analysis of the current research efforts in the field of educational recommendation systems. With the string search of ”educational support” AND “recommendation system” used in four academic databases (Brazil and International) 2016 and 2018. The SR returned 439 papers of which 54 were selected for further analysis. The results show that the profile-based filtering technique for recommendation is the most used, occurring 27.78% of the studies. In 44.44% of these studies, the target audience has been the students. Furthermore, the usual evaluation method was experimental achieving 41% of the selected papers. The results demonstrate applications in learning situations as the recommendation of digital resources, related to learning styles and based on ability and performance.

This paper presents an analysis of candidates to the National Entrance Exam for universities in Brazil, called ENEM. Besides evaluating the performance of high school students, the implementation of ENEM was aimed to increase the population's access to higher education. By analyzing the information of ENEM candidates, we observe how the student's profile haschanged over the years in terms of education level, social inclusion, and the total of students per region. Our analysis considers information extracted from ENEM log files, from 1998 to 2017. The collected data provides details about the candidates, including age range, gender, the region of birth, and scores divided by subject. The total amount of data stored in the logs exceeds 64 GB. Handling such a volume of information requires high computational power. Thus, to extract and analyze the data, we implemented a distributed analyzer using Apache Spark. The analyzer adopts a MapReduce strategy, distributing jobs among worker nodes, which run independently and process part of the data in parallel with other workers. As a result, we present a quantitative analysis of ENEM candidates and describe how their profile has changed since ENEM was implemented, twentyyears ago.

This summary refers to a complete research paper. In 2010, the World Health Organization (WHO) estimated that 19 million children under the age of 15 were visually impaired, with about 39 million blind and 246 million people with severe or moderate vision loss. These numbers suggest the potential size and potential impact of the visually impaired on a day-today basis. The situation has seen some improvement in recent years with increasing access to formal education, from primary school to higher education. As a result, the demand for innovative, technology-based assistance tools that enhance the user experience and the quality of education has increased. This work takes a step towards bridging a technological gap with the design and validation of a system of artificial neural networks, called as Deep Neural Networks (DNNs), capable of identifying the main Cartesian curves of the mathematics curriculum. The Cartesian set comprises 6 degrees of rational algebraic curves, in addition to a total of 42 conic curves, for this paper will be presented two sets of curves, parabolas and ellipses. The development of this gave some methodological steps. First, relevant artificial neural networks were investigated considering the needs of the user and the space of the problem - computer efficiency, loss rate and precision of the generated models used as selection metrics for each neural network tested. The selected network models were InceptionV3, MobileNETV2, VGG16, and VGG19. With average accuracy of 94% and 20% of mean loss, the selected network for the application was VGG16.

This research category full paper brings a landscape of the primary studies which aim to turn diagrams accessible for blind people. There are a considerable number of people with visual impairments. However, was noticed poor accessibility features in tools to work with diagrams, which are typically used to present concepts and structured data, especially for courses in STEM fields. Thus, this systematic literature review gathered the studies about this matter in the last six years, to reveal the approaches being used to make diagrams accessible for blind people, checking, in addition to perception, if interaction and collaboration have been considered. The initial search and screening resulted in 369 studies, out of which 26 were selected for the final analysis. The results presented the preponderance of the auditory approach followed by refreshable Braille displays to achieve perception. Concerning interaction, the keyboard was the main input interface, whereas four out of nine studies found considered it. From the four studies which treated the possibility of simultaneous users working on the diagrammatic representation, only one considered distributed collaboration. Finally, the conclusion is that may there is a research gap in the collaboration, which could guide further developments.

This research full paper presents that Brazilian deaf community has conquered rights in the area of education, and in recent years, with the increase of the enrollment of deaf people in educational establishments, there is a need to elaborate new specific signs of LIBRAS for technical terms of specific courses in different areas. The objective of this work is develop a computational glossary of signals in LIBRAS with the reserved words of the SuperLOGO programming environments and the NXT robotics software. In addition, programming logic and geometry learning in deaf students will be stimulated. The work will be carried out in a bilingual high school, in the mathematics discipline. To validate the glossary and the development process of the students' knowledge will be used categories of pedagogical evaluation and content analysis. The use of these programming softwares combined with the glossary demonstrated the ability to build knowledge in programming logic and geometry that would be hampered by the language barrier. We conclude that the signs developed for the glossary enable the deaf to access complex terms of computing, providing the same possibilities of educational development as their fellow listeners.

This Research to Practice Complete Work presents the category of Innovate Practice. Digital social grouping is now a common practice for exchanging any kind of data between group connections. However, this has posed some key issues for education professionals on the best path to the current educational system to fully embrace this practice and move on. [1] It suggests that “the school model as we know it is over. It makes no sense to students, does not produce expected results, nor prepares the new generations for the challenges of contemporary life.” To fill this gap, an innovative teaching model emerged as an alternative approach centered on the student and reinforced by the pedagogical and technological infrastructures. This work presents a framework as a computational tool to build and provide custom educational interfaces based on the concept of persuasion to push users to the most relevant content (eg, direction). This structure is based on participatory elements from various disciplines that cross the fields of education, computational engineering and psychology. The proposed framework shows that user interactions explored from the persuasion, engagement, and content customization techniques take into account the skills, needs and interests make teaching more attractive and applicable.

Three-dimensional virtual worlds have been studied by many researchers around the world, including in educational contexts. A range of possibilities have emerged from this type of environment, such as improvements in distance education and other educational technologies. But there are some problems related to the use of this environment, such as complex authorship tools, which need to be discussed and considered to create situations that encourage use. The goal of this study was to find, analyze, and discuss the studies that focused on these environments, with authorship as the main theme. With this in mind, we conducted a systematic review of published articles in six repositories to extract studies for subsequent analysis using text mining and graph analysis software.