Design a computer programming learning environment for Massive Open Online Courses

Teaching and learning computer programming is as challenging as difficult. Assessing the work of students and providing individualised feedback to all is time-consuming and error prone for teachers and frequently involves a time delay. The existent tools and specifications prove to be insufficient in complex evaluation domains where there is a greater need to practice. At the same time Massive Open Online Courses (MOOC) are appearing revealing a new way of learning, more dynamic and more accessible. However this new paradigm raises serious questions regarding the monitoring of student progress and its timely feedback. This paper provides a conceptual design model for a computer programming learning environment. This environment uses the portal interface design model gathering information from a network of services such as repositories and program evaluators. The design model includes also the integration with learning management systems, a central piece in the MOOC realm, endowing the model with characteristics such as scalability, collaboration and interoperability. This model is not limited to the domain of computer programming and can be adapted to any complex area that requires systematic evaluation with immediate feedback.

O desenvolvimento da competência intercultural através do Task-Based learning na aula de língua estrangeira

Relatório de Estágio apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ensino de Inglês e de Língua Estrangeira (Francês) no 3.º Ciclo do Ensino Básico e no Ensino Secundário

Massive Open Online Course management: learning science and engineering through peer-reviewed projects

Massive Open Online Courses (MOOC) are gaining prominence in transversal teaching-learning strategies. However, there are many issues still debated, namely assessment, recognized largely as a cornerstone in Education. The large number of students involved requires a redefinition of strategies that often use approaches based on tasks or challenging projects. In these conditions and due to this approach, assessment is made through peer-reviewed assignments and quizzes online. The peer-reviewed assignments are often based upon sample answers or topics, which guide the student in the task of evaluating peers. This chapter analyzes the grading and evaluation in MOOCs, especially in science and engineering courses, within the context of education and grading methodologies and discusses possible perspectives to pursue grading quality in massive e-learning courses.

A learning approach to swarm-based path detection and tracking

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Experience with the first Internet-based course at the Faculty of Medicine, University of São Paulo

PURPOSE: The Internet expands the range and flexibility of teaching options and enhances the ability to process the ever-increasing volume of medical knowledge. The aim of this study is to describe and discuss our experience with transforming a traditional medical training course into an Internet-based course. METHOD: Sixty-nine students were enrolled for a one-month course. They answered pre- and post-course questionnaires and took a multiple-choice test to evaluate the acquired knowledge. RESULTS: Students reported that the primary value for them of this Internet-based course was that they could choose the time of their class attendance (67%). The vast majority (94%) had a private computer and were used to visiting the Internet (75%) before the course. During the course, visits were mainly during the weekends (35%) and on the last week before the test (29%). Thirty-one percent reported that they could learn by reading only from the computer screen, without the necessity of printed material. Students were satisfied with this teaching method as evidenced by the 89% who reported enjoying the experience and the 88% who said they would enroll for another course via the Internet. The most positive aspect was freedom of scheduling, and the most negative was the lack of personal contact with the teacher. From the 80 multiple-choice questions, the mean of correct answers was 45.5, and of incorrect, 34.5. CONCLUSIONS: This study demonstrates that students can successfully learn with distance learning. It provides useful information for developing other Internet-based courses. The importance of this new tool for education in a large country like Brazil seems clear.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

A construção de um currículo de project-based learning: o currículo ideal e o currículo formal

O objetivo deste trabalho é apresentar os resultados da análise das concepções de dois protagonistas de uma reforma curricular que está sendo implementada numa escola de engenharia. A principal característica do novo currículo é o uso de projetos e oficinas como atividades complementares a serem realizadas pelos estudantes. As atividades complementares acontecerão em paralelo ao trabalho realizado nas disciplinas sem que haja uma relação de interdisciplinaridade. O novo currículo está sendo implantado desde fevereiro de 2015. Segundo Pacheco (2005) há dois momentos, dentre outros, no processo de mudança curricular, o currículo “ideal”, determinado por dimensões epistemológica, política, econômica, ideológica, técnica, estética, e histórica e, que recebe influência direta daquele que idealiza e cria o novo currículo e, o currículo “formal” que se traduz na prática implementada na escola. São essas duas etapas estudadas nesta pesquisa. Para isso serão considerados como fontes de dados dois protagonistas, um mais ligado à concepção do currículo e outro da sua implementação, a partir dos quais se busca compreender as motivações, crenças e percepções que, por sua vez, determinam a reforma curricular. Entrevistas semiestruturadas foram utilizadas como técnica de pesquisa, com o propósito de se entender a gênese da proposta e as mudanças entre essas duas etapas. Os dados revelam que mudanças aconteceram desde a idealização até a formalização do currículo, motivadas por demandas do processo de implementação, revela ainda diferenças na visão de currículo e a motivação para romper com padrões na formação de engenheiros no Brasil.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

A 3D computer assisted Orthopedic Surgery Planning approach based on planar radiography

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)