Learning management systems in higher education

The use of Laptops and the Internet has produced the technological conditions for instructors and students can take advantage from the diversity of online information, communication, collaboration and sharing with others. The integration of Internet services in the teaching practices can be responsible for thematic, social and digital improvement for the agents involved. There are many benefits when we use a Learning Management Systems (LMS) such as Moodle, to support the lectures in higher education. We also will consider its implications for student support and online interaction, leading educational agents to a collaborating of different learning environments, where they can combine face-to-face instruction with computer-mediated instruction, blended-learning, and increases the possibilities for better quality and quantity of human communication in a learning background. In general components of learning management systems contain synchronous and asynchronous communication tools, management features, and assessment utilities. These assessment utilities allow lecturers to systematize basic assessment tasks. Assessments can be straightaway delivered to the student, and upon conclusion, immediately returned with grades and detailed feedback. Therefore learning management systems can also be used for assessment purposes in Higher Education.

Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Virtual Communities of Practice: Design and Development of Support Platforms and Participation Fostering

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

B-learning quality: dimensions, criteria and pedagogical approach

Measuring the quality of a b-learning environment is critical to determine the success of a b-learning course. Several initiatives have been recently conducted on benchmarking and quality in e-learning. Despite these efforts in defining and examining quality issues concerning online courses, a defining instrument to evaluate quality is one of the key challenges for blended learning, since it incorporates both traditional and online instruction methods. For this paper, six frameworks for quality assessment of technological enhanced learning were examined and compared regarding similarities and differences. These frameworks aim at the same global objective: the quality of e-learning environment/products. They present different perspectives but also many common issues. Some of them are more specific and related to the course and other are more global and related to institutional aspects. In this work we collected and arrange all the quality criteria identified in order to get a more complete framework and determine if it fits our b-learning environment. We also included elements related to our own b-learning research and experience, acquired during more than 10 years of experience. As a result we have create a new quality reference with a set of dimensions and criteria that should be taken into account when you are analyzing, designing, developing, implementing and evaluating a b-learning environment. Besides these perspectives on what to do when you are developing a b-learning environment we have also included pedagogical issues in order to give directions on how to do it to reach the success of the learning. The information, concepts and procedures here presented give support to teachers and instructors, which intend to validate the quality of their blended learning courses.

Mobile learning: recetividade e predisposição do ensino superior para a sua utilização

A evolução dos dispositivos móveis e a mudança de paradigma educacional, permitiu o surgimento de um novo conceito no processo de ensino e aprendizagem, o mobile learning. O mobile learning pode ser visto como um conceito multidisciplinar, dependendo da perspetiva de cada autor, pois ainda não existe um consenso em relação à definição do conceito. No entanto, todos os autores concordam que o mobile learning consiste na aquisição de conhecimento ou competência através do uso de tecnologias móveis, em qualquer lugar e momento. A presente investigação, de natureza exploratória, pretendeu estudar a receptividade e predisposição dos estudantes e docentes do ensino superior para com a utilização do mobile learning, uma vez que o ensino superior parece ser o ambiente ideal para a realização deste estudo. Por um lado, devido à democratização dos dispositivos móveis, por outro, porque o Instituto Politécnico do Porto pretende vir a implementar um projeto de mobile learning, enquadrado no e- IPP. Deste modo, para a concretização desta investigação, foi realizada uma revisão bibliográfica exaustiva que serviu de base de sustentação para todo o trabalho, complementada com um questionário, de forma a dar resposta às questões de investigação. Depois de recolhidos todos os resultados obtidos através do questionário, procedeu-se à análise e discussão mesmos, bem como às respectivas conclusões.

Short-term Wind Forecasting to Support Virtual Power Player Operation

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evalu-ated and compared with a previous developed forecast-ing model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind short-term forecasting.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

The impact of firm-promoted accreditation of prior learning processes on the worker-organization relationship

The process of Competences Recognition, Validation and Certification , also known as Accreditation of Prior Learning (APL), is an innovative means of attaining school certificates for individuals without an academic background. The main objective of this process is to validate what people have learned in informal contexts, in order to attribute academic certificates. With the increasing interest of the qualification of workers and governmental support, more and more Portuguese organizations promote this process within their facilities and their work hours. This study explores the relationship between the promotion of this Human Resource Development Programme and employee’s attitudes (Job Satisfaction and Organizational Commitment) and behaviours (Extra-role Organizational Citizenship Behaviours) towards the organization they work for. Results of a cross-sectional survey of Portuguese Industrial Workers (N=135) showed that statistical significant results are in the higher levels of Voice Behaviours (a dimension of Extra-role Organizational Citizenship Behaviour in the groups of workers who were involved or had graduated from the firm promoted APL process.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

The Support Role in Clinical Supervision of Nursing Students: Determinant in the Development of Emotional Skills

The problematic situation faced by clients, associated to the processes of health and disease, is expressed through emotions that nursing students have to deal with in the course of care and their formative experiences in clinical teaching. Students have learning needs not only to manage emotions in the context of customer care, but also in terms of their own internal world, emotional conflicts, emotional stress and burn-out. With the present literature review, we intend to explore existing evidence regarding the ways in which the nurse supervisor's support towards nursing students potentiates the development of their competences for the performance of emotional labour. These skills prove to be the key in the ability to manage the emotionally intense situations of care practice and the support function of the nursing supervisor contributes to the development of such competences.

Learning Activities with Semantic. Hypermedia in Higher Education

The increasing use of information and communication technologies (ICT) in diverse professional and personal contexts calls for new knowledge, and a set of abilities, competences and attitudes, for an active and participative citizenship. In this context it is acknowledged that universities have an important role innovating in the educational use of digital media to promote an inclusive digital literacy. The educational potential of digital technologies and resources has been recognized by both researchers and practitioners. Multiple pedagogical models and research approaches have already contributed to put in evidence the importance of adapting instructional and learning practices and processes to concrete contexts and educational goals. Still, academic and scientific communities believe further investments in ICT research is needed in higher education. This study focuses on educational models that may contribute to support digital technology uses, where these can have cognitive and educational relevance when compared to analogical technologies. A teaching and learning model, centered in the active role of the students in the exploration, production, presentation and discussion of interactive multimedia materials, was developed and applied using the internet and exploring emergent semantic hypermedia formats. The research approach focused on the definition of design principles for developing class activities that were applied in three different iterations in undergraduate courses from two institutions, namely the University of Texas at Austin, USA and the University of Lisbon, Portugal. The analysis of this study made possible to evaluate the potential and efficacy of the model proposed and the authoring tool chosen in the support of metacognitive skills and attitudes related to information structuring and management, storytelling and communication, using computers and the internet.

The tutor's role in student learning

Higher education in Portugal, in the last forty years, has undergone profound changes with the enlargement of public higher education network, the appearance of new institutions, the quantity and the heterogeneity of students. The implementation of the Bologna Process in European community countries led to the redesign of higher education Portuguese courses as well as their corresponding curricula. In recent years, the use of Project-led education was one of the most significant changes in teaching and learning, particularly in engineering in higher education in Portugal. This teaching methodology encourages students and teachers to undertake new roles, new responsibilities and a new learning perspective. This study aims at understanding whether the role of the tutor is to be suitable to the needs and expectations of Project-led education students. These changes however are not only structural. At the University of Minho, new teaching and learning methodologies were adopted, which could guide the training of professionals on to the twenty-first century. The opportunity arising from the implementation of Project-led education in Engineering methodology was used in the University of Minho’s courses. This teaching method is intended to provide students with educational support programs that benefit the academic performance, allowing the opportunity to upgrade, train and develop the ability to study and learn more effectively. Through the Project-led education it is possible to provide students with techniques and procedures and develop the ability to communicate orally and in writing. Students and teachers have assumed new roles in the teaching-learning process allowing in one hand the students to explore, discover and question themselves about some knowledge and on the other hand the teachers to change to a tutor, a companion and to a student project guide. Therefore, surveys were analyzed, comprising questions about the most significant contribution of the tutor as well as if there are some initial expectations that have not been foreseen by the tutor.