Innovations in technology: a friendly math project and the learning analytics ���challenge���

Currently the world around us "reboots" every minute and ���staying at the forefront��� seems to be a very arduous task. The continuous and ���speeded��� progress of society requires, from all the actors, a dynamic and efficient attitude both in terms progress monitoring and moving adaptation. With regard to education, no matter how updated we are in relation to the contents, the didactic strategies and technological resources, we are inevitably compelled to adapt to new paradigms and rethink the traditional teaching methods. It is in this context that the contribution of e-learning platforms arises. Here teachers and students have at their disposal new ways to enhance the teaching and learning process, and these platforms are seen, at the present time, as significant virtual teaching and learning supporting environments. This paper presents a Project and attempts to illustrate the potential that new technologies present as a ���backing��� tool in different stages of teaching and learning at different levels and areas of knowledge, particularly in Mathematics. We intend to promote a constructive discussion moment, exposing our actual perception - that the use of the Learning Management System Moodle, by Higher Education teachers, as supplementary teaching-learning environment for virtual classroom sessions can contribute for greater efficiency and effectiveness of teaching practice and to improve student achievement. Regarding the Learning analytics experience we will present a few results obtained with some assessment Learning Analytics tools, where we profoundly felt that the assessment of students��� performance in online learning environments is a challenging and demanding task.

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.

Odin: a service for gamification of learning activities

Existing gami���cation services have features that preclude their use by e-learning tools. Odin is a gami���cation service that mimics the API of state-of-the-art services without these limitations. This paper describes Odin, its role in an e-learning system architecture requiring gami���cation, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) speci���cation.

Integrating rich learning applications in LMS

Currently, a learning management system (LMS) plays a central role in any e-learning environment. These environments include systems to handle the pedagogic aspects of the teaching���learning process (e.g. specialized tutors, simulation games) and the academic aspects (e.g. academic management systems). Thus, the potential for interoperability is an important, although over looked, aspect of an LMS. In this paper, we make a comparative study of the interoperability level of the most relevant LMS. We start by defining an application and a specification model. For the application model, we create a basic application that acts as a tool provider for LMS integration. The specification model acts as the API that the LMS should implement to communicate with the tool provider. Based on researches, we select the Learning Tools Interoperability (LTI) from IMS. Finally, we compare the LMS interoperability level defined as the effort made to integrate the application on the study LMS.

Interactional strategies between terminologist and domain specialist. Putting order into our universe: the concept of Blended Learning

A presente comunica����o visa discutir as mais-valias de um desenho metodol��gico sustentado numa abordagem conceptual da Terminologia aplicado ao exerc��cio de harmoniza����o da defini����o do cen��rio educativo mais promissor do Ensino Superior actual: o blended learning. Sendo a Terminologia uma disciplina que se ocupa da representa����o, da descri����o e da defini����o do conhecimento especializado atrav��s da l��ngua a ess��ncia deste dom��nio do saber responde a uma necessidade fundamental da sociedade actual: putting order into our universe, nas palavras de Nuopponen (2011). No contexto descrito, os conceitos, enquanto elementos da estrutura do conhecimento (Sager, 1990) constituem um objecto de investiga����o de complexidade n��o despicienda, pois apesar do postulado de que a l��ngua constitui uma ferramenta fundamental para descrever e organizar o conhecimento, o princ��pio isom��rfico n��o pode ser tomado como adquirido. A abordagem conceptual em Terminologia prop��e uma vis��o precisa do papel da l��ngua no trabalho terminol��gico, sendo premissa basilar que n��o existe uma correspond��ncia un��voca entre os elementos atom��sticos do conhecimento e os elementos da express��o lingu��stica. �� pela raz��es enunciadas que as op����es metodol��gicas circunscritas �� an��lise do texto de especialidade ser��o consideradas imprecisas. Nesta reflex��o perspectiva-se que o conceito-chave de uma abordagem conceptual do trabalho terminol��gico implica a combina����o de um processo de elicita����o do conhecimento t��cito atrav��s de uma negocia����o discursiva orientada para o conceito e a an��lise de corpora textuais. Defende-se consequentemente que as estrat��gias de interac����o entre termin��logo e especialista de dom��nio merecem aten����o detalhada pelo facto de se reflectirem com expressividade na qualidade dos resultados obtidos. Na sequ��ncia do exposto, o modelo metodol��gico que propomos sustenta-se em tr��s etapas que privilegiam um refinamento dessa interac����o permitindo ao termin��logo afirmar-se como sujeito conceptualizador, decisor e interventor: (1) etapa explorat��ria do dom��nio-objecto de estudo; (2) etapa de an��lise onamasiol��gica de evid��ncia textual e discursiva; (3) etapa de modeliza����o e de valida����o de resultados. Defender-se-�� a produtividade de uma sequ��ncia c��clica entre a an��lise textual e discursiva para fins onomasiol��gicos, a interac����o colaborativa e a introspec����o.

Learning sustainability by developing a solar dryer for microalgae retrieval

The development of nations depends on energy consumption, which is generally based on fossil fuels. This dependency produces irreversible and dramatic effects on the environment, e.g. large greenhouse gas emissions, which in turn cause global warming and climate changes, responsible for the rise of the sea level, floods, and other extreme weather events. Transportation is one of the main uses of energy, and its excessive fossil fuel dependency is driving the search for alternative and sustainable sources of energy such as microalgae, from which biodiesel, among other useful compounds, can be obtained. The process includes harvesting and drying, two energy consuming steps, which are, therefore, expensive and unsustainable. The goal of this EPS@ISEP Spring 2013 project was to develop a solar microalgae dryer for the microalgae laboratory of ISEP. A multinational team of five students from distinct fields of study was responsible for designing and building the solar microalgae dryer prototype. The prototype includes a control system to ensure that the microalgae are not destroyed during the drying process. The solar microalgae dryer works as a distiller, extracting the excess water from the microalgae suspension. This paper details the design steps, the building technologies, the ethical and sustainable concerns and compares the prototype with existing solutions. The proposed sustainable microalgae drying process is competitive as far as energy usage is concerned. Finally, the project contributed to increase the deontological ethics, social compromise skills and sustainable development awareness of the students.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players��� bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method���s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts��� negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems��� technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players��� decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator���MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts��� negotiations.

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.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.