Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

Sustainability education in PBL education: the case study of IEM-UMINHO

Sustainability-related skills are becoming more and more relevant for a proficient and professional engineering practice. Industrial engineers in particular, given their broad field of intervention and being at the heart of industrial activity, hold a great deal of potential and responsibility in providing and delivering best industrial practices, that support enhanced industrial systems and products. Therefore making a real contribution in generating wealth and income for all the companies’ stakeholders, including local communities, as well as adding up to more sustainable ecosystems. Previous work by the authors focused on studying the inclusion of this subject on the education of industrial engineers, especially through active-learning methodologies, as well as presenting results on the use of one such approach. The study conducted tried to identify the impacts on sustainability learning using a given specific activity, i.e. a workshop on industrial ecology, held in the 2014/2015 academic year on the Integrated MSc degree on Industrial Engineering and Management at the University of Minho, Portugal. The study uses content analysis of student teams’ reports for two consecutive academic years. The former did not include one such workshop, while the latter did. The Fink taxonomy was used in the discussion of results and reflection. The study outcomes aimed at supporting decision making on worthiness of investment on similar education instruments for sustainability competency development. Some results of the study highlight that: (1) the workshop seem to globally have a positive contribution on the sustainability learning; (2) a number of dimensions of the Life cycle design strategy wheel was developed, but the approach was not broadly used, (3) There was a mismatch on the workshop schedule; (4) students enjoy the workshop; (5) a clearer endorsement on relevance of this aspect is required. Suggestions for future work are also issued.

Gamification e game-based learning: uma abordagem lúdica à aprendizagem

Relatório de estágio de mestrado em Ensino de Informática

O ensino em grupo de instrumentos musicais: um estudo de caso múltiplo em Portugal e no Brasil

Tese de Doutoramento em Estudos da Criança (área de especialização em Educação Musical)

Cross-language influences on new second language vocabulary acquisition: the effect of learning method

Dissertação de mestrado integrado em Psicologia

Epidemiological studies of Mansonella ozzardi (Nematoda, Onchocercidae) in indigenous communities of Pauini municipality, Amazonas, Brazil

The Mansonella ozzardi has a widespread distribution among the indigenous and riverine communities of Amazonas, Brazil. We estimated the prevalence of Mansonella ozzardi in indigenous communities of the Pauini municipality, Amazonas state, Brazil and the rate of parasitic infection in vectors. We collected thick blood smears from individuals from six Apurinã indigenous communities along the Purus River and its tributaries. Collections of simuliids were made and dissected, and the larval instars of M. ozzardi identified. The overall prevalence of M. ozzardi was 28.40%, with the highest incidence among males and agricultural workers. Among age groups, children 2-9 years of age had the lowest incidence, while individuals older than 58 exhibited the highest rates of infection. We found infected simuliids in three communities, with Parasitic Infection Rates (PIR) of 0.34-6.58%. The prevalence of M. ozzardi among the Apurinã people is high, possibly related to the diary activities of the riparian people, where a high abundance of the vectors exists.

Tecnologias para a inclusão: centros de recursos virtuais no apoio às necessidades educativas especiais

Tese de Doutoramento em Ciências da Educação (área de especialização em Tecnologia Educativa).

Adult learning and education policies in Germany, Portugal and Sweden: An analysis of national reports to CONFINTEA VI

The chapter presents a theoretical proposal of three analytical models of Adult Learning and Education (ALE) policies. Some analytical categories and the corresponding dimensions are organised according to the ALE rationale which is typical of each social policy model. Historical, cultural and educational features are mentioned in connexion with the different policy models and its interpretative capacity to making sense of policies and practices implemented in Germany, Portugal and Sweden. !e analysis includes the states of the art and the official representations of ALE produced by the respective national authorities through national reports which were presented to CONFINTEA VI (2009).

Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group.

Literacy practices in adult learning biographies: possibilities and constraints

[Extrat] The answer to the social and economic challenges that it is assumed literacy (or its lack) puts to developed countries deeply concerns public policies of governments namely those of the OECD area. In the last decades, these concerns gave origin to several and diverse monitoring devices, initiatives and programmes for reading (mainly) development, putting a strong stress on education. UNESCO (2006, p. 6), for instance, assumes that the literacy challenge can only be met raising the quality of primary and secondary education and intensifying programmes explicitly oriented towards youth and adult literacy. (...)

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"

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.