Generative artificial intelligence encompasses techniques for automatically generating content such as image, music, and text. A common application of these techniques, especially involving text generation, is the construction of conversational agents. In education, generative artificial intelligence has the potential to transform the learning process and enable new forms of teaching, such as the practical application of concepts studied in class and the acquisition of new knowledge through the interaction with computational tools. In Brazil, literature teachers often complain about students' resistance to the content and insufficient reading practice. To address these challenges, this work presents a serious game in which students can chat with Machado de Assis, an important writer of the Brazilian literature. The game was developed by integrating with ChatGPT. The validation was conducted with 15 high school students for them to interpret the book "Dom Casmurro". The MEEGA+ instrument was used to evaluate the usability and the experience with the game. The main findings were that students better understood the book, found the game easy to use, and felt immersed in the conversation with Machado de Assis, although they assessed the texts as long and tiring. In a world dominated by faster and visually stimulating forms of entertainment, such as video games and social networks, students may have different expectations regarding the reading experience. If they are not used to reading denser and more introspective texts, they may find it difficult to maintain interest when chatting with Machado de Assis. Finally, another implicit point in the game dynamics is observed: the (re)signification of the methodology to be adopted by the teacher, who will perceive the need to complement interactions with the writer in class according to what he can or should answer, tending to responses that stimulate students to study more about what is within the realm of knowledge about Machado de Assis.

Tuning an algorithm requires to evaluate it under different configurations on several problem instances. Such evaluations are costly. A way to reduce the configuration time when developing tuners is to use surrogate models, which map configuration-instance pairs to the approximate algorithm performance and thus allow to replace algorithm runs by fast calls to the model. Most applications of surrogate models found in the literature focus on predicting algorithm running time; much less effort has been devoted to predicting the quality of solutions of optimization algorithms. In this paper, we present a comparative study of surrogate models for predicting solution quality. We evaluate several surrogate models from the literature, including random forests, gradient boosting methods, and neural networks, and compare ways of handling different classes of parameters, data imputation strategies, and codification of instances. We demonstrate for two heuristic algorithms that the best models can accurately reproduce effects observed when tuning with the ground truth. Our code is available ( https://github.com/gutodelazeri/oracle ).

One of the biggest problems in teaching Requirements Engineering at universities is the lack of access to real projects, which could allow students to interact with stakeholders and practice communication skills while conducting requirements elicitation. This article presents a serious game for practicing requirements elicitation through guided interviews with stakeholders. Two versions of the game were developed. The Immersive VR game was designed to run on mobile phones with Google Cardboard. A Non-Immersive version was also developed to run on Windows. Systematic literature reviews point out a lack of immersive VR games for learning Software Engineering. Moreover, the game proposed here is a pioneer in the addressed subject, including the use of low-cost technologies, such as Google Cardboard. The game was tested for 20 minutes by 46 students in two classes of the Bachelor's Degree in Software Engineering. After the test in the first class, the game was improved, and another test was conducted. The purpose of the validation was to verify the sense of presence among the students through a presence questionnaire (PQ). The highest averages were identified in the non-immersive version. For the immersive version, the statistically significant lowest evaluated values pertained to movement within the environment and the feeling of disorientation at the end of the experience. This indicates the need for improvements regarding movement. The literature suggests two directions: the first is to reduce the duration of experimentation with immersive environments, and the second suggests exploring alternative mechanisms for movement within the environment. It is important to remember that the students experimented with their own mobile phones, that is, they did not use dedicated VR equipment. Therefore, it can be concluded that developing VR solutions for these devices requires more restrictive decisions.

Para construir um futuro próspero e inovador é importante incentivar desde cedo nas crianças o desenvolvimento do espírito empreendedor, despertando sua criatividade, capacidade de liderança e perseverança para enfrentar desafios e transformar ideias em realidade. Os jogos permitem que os estudantes, através de simulações sobre situações de negócio, possam desenvolver na prática algumas dessas competências. A nova Base Nacional Comum Curricular (BNCC) aponta na Educação Empreendedora a oferta de situações propícias a práticas interdisciplinares, promovendo no aluno um posicionamento proativo em busca da sua evolução. Nesse artigo será apresentado um jogo para dispositivos móveis para o público do Ensino Fundamental II, que abrange os conteúdos de processo produtivo, oferta e demanda como oportunidades de negócios, e fluxo de caixa. O jogo foi testado durante uma semana com 13 alunos do nono ano da Escola Básica Municipal Professor Antonio Rohden de Braço do Norte/SC. Foram realizados testes de conhecimento antes e depois do jogo, e usado o instrumento MEEGA+ Kids para avaliar a qualidade do jogo em termos de usabilidade e experiência de jogo. Os testes apresentaram uma melhoria estatisticamente significativa do conhecimento. A escala MEEGA+ resultou numa avaliação do jogo como de boa qualidade.

Worldwide, recent changes in the work environment affected workspace ergonomics conditions over long periods of time. This extended period of bad ergonomic conditions hindered the ability to maintain good posture, aggravating the postural challenges of the typical office worker that spends 15 h seated each day and leading to a surge of the prevalence of lower back and neck pain. Bad posture initially leads to muscle, disc, and joint pain, and can evolve to serious conditions. Therefore, the monitoring of spatial and temporal characteristics of the hip and back is of utmost importance for injury prevention. We developed an IoT platform that employs a sensor fusion array methods to collect specific postural information during long term usage. The collected data was used to assemble a Postural Dashboard, employing data visualization and Exploratory Data Analysis (EDA) to provide descriptive statistics data and allow the investigation of a user’s long-term postural patterns.

Offline algorithm configuration methods search for fixed parameter values for a given set of problem instances. For each parameter, such methods perform an equivalent to a constant regression, since the parameter value remains constant for any problem instance. However, optimal parameter values may depend on instance features, such as the instance size. In this paper, we represent parameters by non-constant models, which set the parameter values according to the instance size. Instead of searching for parameter values directly, the configuration process calibrates such models. In particular, we propose a simple yet effective linear model, which approximates linear relations between instance size and optimal parameter values. For modeling nonlinear relations, we propose piecewise and log-log linear models. The evaluation of the proposed methods on four configuration scenarios show good performance gains in comparison to traditional instance-independent algorithm configuration with comparable tuning effort.

Desenvolvido pelo Departamento de Tireoide da Sociedade Brasileira de Endocrinologia e Metabologia (SBEM), o Guia Prático em Doenças da Tireoide está integrado ao rol de materiais de divulgação científica e educação médica lançados pela instituição nos últimos anos. Em 28 capítulos elaborados por professores, pesquisadores e endocrinologistas com reconhecimento mundial e larga experiência clínica, o guia contempla tópicos que se estendem da fisiologia tireoidiana à abordagem diagnóstica e terapêutica dos pacientes com as mais diversas doenças da tireoide. A confiabilidade dos dados compilados e o sólido conhecimento de seus colaboradores condicionam este livro como auxílio essencial para consulta durante o atendimento dos pacientes em consultórios, ambulatórios ou enfermarias, além de oportunidade única de atualização a estudantes, médicos residentes e profissionais com particular interesse na Tireoidologia.

Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.

We propose a set of capping methods to speed-up the automatic configuration of optimization algorithms. First, we build a performance envelope based on previous executions of known configurations, which defines the minimum required performance for new configurations. Then, we use the performance envelope to evaluate new configurations, stopping poor performers early. We propose different methods to aggregate previous executions into a performance envelope, and evaluate them on several configuration scenarios. The proposed methods produce solutions of the same or better quality as configuring without capping, but reduce the effort required to configure optimization algorithms.

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

This paper introduces acviz, a tool that helps to analyze the automatic configuration of algorithms with irace. It provides a visual representation of the configuration process, allowing users to extract useful information, e.g. how the configurations evolve over time. When test data is available, acviz also shows the performance of each configuration on the test instances. Using this visualization, users can analyze and compare the quality of the resulting configurations and observe the performance differences on training and test instances.

This paper presents a set of capping methods to speed-up the automated configuration of optimization algorithms. These methods use known previous executions to compute a performance envelope, which is used to evaluate new executions and early stop those with unsatisfactory performance. Preliminary experiments on six scenarios show that the capping methods save up to 78% of the configuration effort, while finding configurations of the same quality.

The study of graphs and their algorithms is very important in many areas, such as engineering and computing. Given that students present difficulties in learning these concepts, this work proposes a computational and visual tool for supporting the teaching of graphs and, specially, the well known algorithm of Dijkstra.

This Research to Practice Paper presents the results of the evaluation of pre-university factors that impact the initial motivation of undergraduate students in computing. Although there are studies in the literature that have investigated some previous factors, this paper replicates a previous work that aims to consolidate several pre-university factors and, as the main differential, uses the AMS (Academic Motivation Scale), a scale already consolidated in the literature to measure students' initial motivation, and evaluate the relation between motivation and candidate factors. We applied a questionnaire to 159 students from different computing programs in ten universities, which evaluates 20 factors divided into 4 groups: personal and demographic data, taste and knowledge of the program and area, computing experience, and school performance. To evaluate the correlation between factors and motivation, we used Spearman's coefficient, t student test, and ANOVA to evaluate the correlation between factors and motivation. As main results, we found significant variation in the initial motivation according to following factors: taste for programming and technology, knowledge about the undergraduate program content, correct perception about computing professionals, knowledge and experience in computer programming, and general school performance.

A way of minimizing the opportunity of cheating in exams is to assign different tests to students. The likelihood of cheating then depends on the proximity of the students' desks, and the similarity of the tests. The test-assignment problem is to find an assignment of tests to desks that minimizes that total likelihood of cheating. The problem is a variant of a graph coloring problem and is NP-hard. We propose a new heuristic solution for this problem. Our approach differs from the usual way of designing heuristics in two ways. First, we reduce test-assignment to the more general unconstrained binary quadratic programming. Second, we search for a good heuristic using an automatic algorithm configuration tool that evolves heuristics in a space of algorithms built from known components for binary quadratic programming. The best hybrid heuristics found repeatedly recombine elements of a population of elite solutions and improve them by a tabu search. Computational tests suggest that the resulting algorithms are competitive with existing heuristics that have been designed manually.

Automatic methods have been applied to find good heuristic algorithms to combinatorial optimization problems. These methods aim at reducing human efforts in the trial-and-error search for promising heuristic strategies. We propose a grammar-based approach to the automatic design of heuristics and apply it to binary quadratic programming. The grammar represents the search space of algorithms and parameter values. A solution is represented as a sequence of categorical choices, which encode the decisions taken in the grammar to generate a complete algorithm. We use an iterated F-race to evolve solutions and tune parameter values. Experiments show that our approach can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

This paper presents a case study with the evaluation of factors that impact on the motivation of 112 undergraduate students in a Software Engineering program. Considering that motivation and engagement are key aspects of students success, the goal of this paper is the identification of the factors that contribute to students' motivation and engagement. We applied a questionnaire which evaluates 48 motivational factors divided into 6 groups: personal and demographic data, general perception of motivation, perception about the university, student behavior, perception about program and perception about class/teacher. As results, after apply statistic tests, we found 15 factors with significant variance in the type of students motivation, only 3 factors with variance in the approval rate, and 5 factors with variance in the overall grade average. We conclude that, for the sample used, the perception of student behavior (engagement) is associated with his performance. The type of motivation is associated mainly with the perception about the program, classes, and faculty. The intention of dropout is mainly associated with the perception of the classes and faculty.

This Research Paper presents the evaluation of an instrument to identify the impact of motivation and engagement factors in undergraduate students in computing. Although researches indicate a direct impact of motivation and engagement on student performance and retention, few studies have been found that address which factors are relevant in this process. The instrument is a questionnaire based on the compilation of several works of the literature containing 48 items divided into 6 groups: personal and demographic data, general perception about motivation, perception about the university, student behavior, perception about program and perception about classes/teachers. The questionnaire evaluation is based on a case study with 112 undergraduate students in Software Engineering. As a result, we found that the questionnaire can be considered reliable (Cronbach's alpha =.8904). Considering the validity of constructs, we found an acceptable degree of correlation between the most pair of items in each group (averaging 63%). We also found that the item-total correlation coefficient was only not adequate for one factor group, indicating satisfactory correlation for all other items. Finally, we found that the number of factors is coherent, but there are several items from different groups strongly correlated, indicating the need for a reorganization.

This paper presents a non-prioritized belief change operator, designed specifically for incorporating new information from many heterogeneous sources in an uncertain environment. We take into account that sources may be untrustworthy and provide a principled method for dealing with the reception of contradictory information. We specify a novel Data-Oriented Belief Revision Operator, that uses a trust model, subjective logic, and a preference-based argumentation framework to evaluate novel information and change the agent's belief set accordingly. We apply this belief change operator in a collaborative traffic scenario, where we show that (1) some form of trust-based non-prioritized belief change operator is necessary, and (2) in a direct comparison between our operator and a previous proposition, our operator performs at least as well in all scenarios, and significantly better in some.

The impact of an increasing share of electric vehicles (EVs) is not fully known. Issues related to engine and battery point out that drivers of EVs may consider different route choices in order to take advantage of energy recovery by regenerative braking, for instance. Because not only travel time but also energy consumption may matter, this work proposes a bi-objective traffic assignment method that computes equilibrium flows in scenarios with a mixed population of vehicles, considering physical aspects and energy recovery. We present two methods of route choice for EVs and propose a bi-objective shortest path algorithm for them. Experiments show a small increase in travel time in congested scenarios, compensated by an expressive decrease in energy consumption in real-world networks. In uncongested scenarios, the travel time remains while the energy consumption decreases.