Evaluation Model of Virtual Learning Environments: A Pilot Study

Virtual learning environments (VLE) have frequently been used in educational practices, and the evaluation of their effectiveness as instruments to support learning gains must consider several dimensions. This paper presents an evaluation model for VLE, called MA-AVA (Model for the Evaluation of VLE), built after a review of the literature and focused on verifying students\u27 learning gains. The MA-AVA evaluation model was applied in a pilot study to an undergraduate engineering class, using a VLE, Educ-MAS-GA, in the discipline of Analytical Geometry. The results indicate that, although students\u27 perception of learning in VLE is relevant, the knowledge acquired is more subtle and difficult to assess. Therefore, a VLE learning evaluation model should include different dimensions of learning, such as the students’ perceptions and their measures of learning gain

Stop-Dengue: Game for Children and Adolescents with Down Syndrome

This work aims at presenting an educational game for people with down syndrome with a focus on learning how to prevent and diagnose, viral Dengue disease. STOP-Dengue game was developed considering bibliographic research, a search for apps classified as games on the Google Play platform for smartphones, and a survey applied to a group of parents and caregivers to identify the best kind of educational stimuli for children and adolescents with down syndrome. The game presents details about Dengue viral disease, encouraging the active participation of children in the learning of this subject. A group of students with down syndrome participated in the evaluation of STOP-Dengue. The results showed that participants learned the concepts about Dengue, and the majority were satisfied with the game

Understanding the Strategic Actor Diagram: an Exercise of Meta Modeling

i-star (i*) modeling uses the actor concept to ground the intentions of a given Universe of Discourse. Our work contributes to the understanding of the actor concept as used in i*. We have used a collaborative approach to better understand the actor concept. The authors met 9 times to discuss the topic. The goal was to discuss i* meta-models, which was later specialized to discuss actor modeling. After the meetings and after one week of collaborative work using a collaboration based editor, “Writely”, we have agreed on presenting our model from two different perspectives, but both using UML as the meta language. We understand that these models, designed by consensus, represent what we have labeled the SA Diagram or the Strategic Actor Diagram. The article presents the models we have arrived as well as the process we have used. We believe that making this process transparent will help to shed light not only on the concept of actor, but on the process of meta-modeling as well

Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period

Abstract Diagnostic and decision-making processes in the 2019 Coronavirus treatment have combined new standards using patient chest images, clinical and laboratory data. This work presents a systematic review aimed at studying the Artificial Intelligence (AI) approaches to the patients’ diagnosis or evolution with Coronavirus 2019. Five electronic databases were searched, from December 2019 to October 2020, considering the beginning of the pandemic when there was no vaccine influencing the exploration of Artificial Intelligence-based techniques. The first search collected 839 papers. Next, the abstracts were reviewed, and 138 remained after the inclusion/exclusion criteria was performed. After thorough reading and review by a second group of reviewers, 64 met the study objectives. These papers were carefully analyzed to identify the AI techniques used to interpret the images, clinical and laboratory data, considering a distribution regarding two variables: (i) diagnosis or outcome and (ii) the type of data: clinical, laboratory, or imaging (chest computed tomography, chest X-ray, or ultrasound). The data type most used was chest CT scans, followed by chest X-ray. The chest CT scan was the only data type that was used for diagnosis, outcome, or both. A few works combine Clinical and Laboratory data, and the most used laboratory tests were C-reactive protein. AI techniques have been increasingly explored in medical image annotation to overcome the need for specialized manual work. In this context, 25 machine learning (ML) techniques with a highest frequency of usage were identified, ranging from the most classic ones, such as Logistic Regression, to the most current ones, such as those that explore Deep Learning. Most imaging works explored convolutional neural networks (CNN), such as VGG and Resnet. Then transfer learning which stands out among the techniques related to deep learning has the second highest frequency of use. In general, classification tasks adopted two or three datasets. COVID-19 related data is present in all papers, while pneumonia is the most common non-COVID-19 class among them