This study evaluates the usability of Alfaba, a multi-sensory educational tool specifically designed to support literacy development for children and young people with Down Syndrome (DS). Alfaba integrates tactile, visual, and auditory feedback, aiming to facilitate letter recognition, phoneme association, and word construction in a manner aligned with the cognitive and motor needs of this population. A usability test was conducted with seven participants aged 7 to 17, focusing on their interactions with Alfaba to assess accessibility, engagement, and areas for improvement. The results highlight that Alfaba's tangible design and real-time feedback encourage independent learning, support error recognition, and foster motor skill development, making it a valuable literacy tool for individuals with DS. Additionally, the study identifies opportunities for enhancement, such as incorporating touchscreen capabilities, gamified elements, and customizable word groups to further engage users. Alfaba's adaptability to the Brazilian Portuguese phonetic structure also positions it as a culturally inclusive tool that addresses the specific phonological needs of Portuguese-speaking children with DS. This study contributes to the field of inclusive educational technology by offering design recommendations that can enhance Alfaba's impact on literacy and proposes directions for future research to further refine and assess its efficacy in controlled educational settings.

Learning analytics (LA) and educational data mining (EDM) are two complementary approaches to modeling and understanding teaching-learning processes and, in general, data from academic environments. LA is applied to data from various sources, which can vary in format, granularity, and structure. Integrating these data is key to addressing the challenge of scalability in LA, a fundamental aspect. To this end, interoperability, understood as the ability of different systems, devices, or applications to connect, interact, and work together effectively, is crucial and generates the need for specifications for the case of academic information systems and Learning Management Systems. According to this context, the objective of this work was to address through a literature review the following main question: What are the main challenges for modeling architecture to support the interoperability of educational data to apply Learning Analytics? To develop the review, the team used Parsifal, an online tool designed to conduct systematic literature reviews in the context of software engineering. The initial search was done in six databases, deciding to include twenty papers in the final report. The results showed that there are still many open spaces for research and development in terms of the design and use of educational data specifications for the subsequent application of LA, to make the transition from models built on data coming from a single source to the construction of models that report results from the integration of several sources using specifications like Caliper Analytics or Experience API.

Alfaba, presented in this paper, is a low-cost, multi-sensory educational tool tailored to enhance literacy in underprivileged children. It leverages neuroplasticity principles, employing a tactile, interactive approach to develop essential neural connections for reading and writing. Tested with 11 children aged 7 to 10 years, its usability evaluation demonstrated effectiveness and user-friendliness, particularly in socially vulnerable contexts. Its cost-effectiveness makes Alfaba accessible in resource-limited settings, aiming to reduce educational disparities. Alfaba's innovative design focuses on providing equal learning opportunities for all children, regardless of socioeconomic background, making it a significant step toward educational equity and demonstrating the inclusive integration of technology in education for widespread impact.

This paper presents the development of an assistive periodic table, an assistive technology tool designed to facilitate the learning of Chemistry for visually impaired students. The results of the tool's development are outlined, along with a description of the conducted test involving congenitally blind students. Finally, we discuss the improvements requested by the involved students and the prospects for extending the tool.

The Covid-19 pandemic has driven students out of schools around the world. In Brazil, a developing country, this dropout has damaged the literacy of students between the ages of 5 and 9. We are running against the clock, and solutions to develop skills to promote reading and writing are fundamental. For students with learning difficulties, the damage is even greater. Dyslexic students have difficulties that naturally make this step even more complex for them. This work presents Alfaba: a tangible solution developed with low-cost hardware that stimulates literacy skills. Alfaba got evaluated by professionals and teachers and prototyped to support not just dyslexic students, but every student that needs to be supported at this stage of their learning journey. Our results show that Alfaba meets the needs of students and that its functions are consistent with the skills to be worked on in the reading and writing process.

This summary refers to a full research article. The article presents a Systematic Literature Review (SLR) that aims to characterize the use of conversational agents (CHATBOTS) in the current scenario of Educational Recommendation Systems (ERS).The objective of this work is to improve the quality of teaching by using chatbots and ERS as valuable tools for teaching, in order to personalize the student learning experience and provide relevant recommendations based on their behavior and learning history, making it more engaging, personalized, and efficient. Following the SLR protocol proposed by Kitchenham, the string and connector chain (“recommendation” OR “recommender”) AND (“chatbot” OR “chatbots” OR “chaterbots”) was planned and used in the search fields of 4 highly relevant academic data repositories in the computing area: Institute of Electrical and Electronic Engineers (IEEE), Scopus, Association for Computing Machinery (ACM), and Science Direct, covering the period from 2018 to 2023, selecting 1,401 published articles, of which, after applying inclusion criteria, 158 were analyzed in the final phase of the review. As main results, we can highlight: Personalization of learning: with the help of chatbots, ERS can analyze student data, such as academic history, test results, and learning preferences, to provide personalized recommendations for educational content, maximizing student learning; Accessibility: Chatbots help make education more accessible to students, as they are available without interruption, which means that students can get help whenever they need it, regardless of the time or location; Engagement: Chatbots help with educational content by providing personalized recommendations for content that is relevant and interesting, keeping students engaged and motivated. Additionally, chatbots can use gamification techniques, such as rewards and competitions, to encourage students to engage more with educational content; Data analysis: Chatbots help collect and analyze data on student performance, tracking student progress and learning activities and signaling according to predefined parameters; Integration with existing technologies, such as learning management systems and online teaching platforms, can help provide a more integrated and unified learning experience for students. In conclusion, the use of chatbots in ERS has the potential to transform education, providing a more personalized, accessible, and efficient learning experience for students, being a complementary tool to help improve the learning experience of students.

Human nature differs from all other beings by its cognitive capacity. Above all, we can say that humans have the “superpower” of knowledge with the possibility of transforming their environment through their skills and expertise. Education is, in its essence, the transmission of knowledge and sharing of experiences. Thus, education needs to be in constant transformation following the evolution of humanity. From this perspective, Maker Education has been gaining popularity and being inserted in formal learning environments in order to develop new ways of teaching and developing skills that the modern world requires. However, well-defined objectives and validations that produce credible evidence are essential for new practices to be substantiated. In education, it must do this through assessment process. Therefore, this paper presents a systematic mapping of the literature aimed at identifying which methodologies, instruments, and technological tools have been used for evaluation in Maker Education. Thus, this paper seeks to contribute to discussions, reflections, and guidance on evaluating learning in Maker Education. A total of 982 research papers were screened, leaving 26 for analysis after inclusion steps. The results of this mapping showed a diversity of methods being applied in the development of maker activities and the frequent use of questionnaires as an assessment tool. In addition, the US was the country with the highest incidence of maker activities, although seven other countries were mentioned. We conclude by reflecting on the existence of a demand for technological tools that support the evaluation process in Maker Education.

Stakeholder engagement is a key aspect for the successful implementation of Learning Analytics (LA) in Higher Education Institutions (HEIs). Studies in Europe and Latin America (LATAM) indicate that, overall, instructors and students have positive views on LA adoption, but there are differences between their ideal expectations and what they consider realistic in the context of their institutions. So far, very little has been found about stakeholders’ views on LA in Brazilian higher education. By replicating the survey conducted in other countries, in seven Brazilian HEIs, we found convergences both with Europe and LATAM, reinforcing the need for local diagnosis and indicating the risk of assuming a ”LATAM identity”. Our findings contribute to building a corpus of knowledge on stakeholders expectations with a contextualised comprehension of the gaps between ideal and predicted scenarios, which can inform institutional policies for LA implementation in Brazil.

This paper depicts a machine learning method for fainting and epileptic seizures automatic recognition. We evaluated five machine learning techniques in order to find out which classification method maximizes the accuracy level and, at the same time, minimizes the computational complexity since the experimental environment has very limited computational resources (processing power). We prototype such method in a wearable device, taking into account F-Score and Accuracy metrics. The experimental evaluation shows that there are no significant difference between KNN, PART, and C4.5. However, KNN has high computational cost when compared to PART and C4.5. PART has low computational cost when compared to C4.5 since it identified less rules.

We introduce innovations in a Digital Teaching Platform (DTP) through tools centred on supporting the teacher. We focus on the utilisation of data about the students and the class in order to recommend actions and content for the teacher. For this, we need a platform with novel capabilities. First, we augment the content delivery application with data collecting capabilities. Second, we create a cloud-based analytics engine that infers student profiles and context parameters from multi-modal sources. Third, we provide a web-based platform for content composition that makes use of the inferred student and context profiles to support teachers in lesson planning. Our solution implements the complete cycle from content composition to delivery and adjustment, allowing for the research and development of new features and intelligences in Digital Education.

This work presents an alternative model to traditional educational recommender systems techniques. The model proposes the description of Learning Objects (LO) and User Profiles as OWL ontology individuals. We propose that the use of ontologies can be very helpful when we seek to accomplish an overall domain interoperability and knowledge description. It is also fundamental for our proposal that a common vocabulary is used. For that, the core of the ontologies are described with the use of the OBAA metadata standard, an extension to IEEE LOM that provides interoperability among hardware platforms for the Brazilian context. Also is part of this work to present a set of practices to develop educational Recommender Systems supported by ontologies.

This work presents a recommender system infrastructure for educational material described with metadata. The idea is to provide a set of flexible premises that allows reasoning and improved personalized results in learning content recommendations. For its operation we propose the use of the OBAA standard, which is an extension of IEEE LOM that provides interoperability among hardware platforms contextualized with the Brazilian Educational domain. The technological core of this infrastructure is based on the use of FOAF to describe user profiles, an OWL Ontology to describe specific domain features as well as to facilitate the reasoning process, a Web Service that connects to a federate educational content repository, and a Collaborative Filtering Algorithm as an algorithm recommendation.