Managing informal learning in engineering contexts: the learners' perspective

According to recent studies, informal learning accounts for more than 75% of our continuous learning through life. However, the awareness of this learning, its benefits and its potential is still not very clear. In engineering contexts, informal learning could play an invaluable role helping students or employees to engage with peers and also with more experience colleagues, exchanging ideas and discussing problems. This work presents an initial set of results of the piloting phase of a project (TRAILER) where an innovative service based on Information & Communication Technologies was developed in order to aid the collection and visibility of informal learning. This set of results concerns engineering contexts (academic and business), from the learners' perspective. The major idea that emerged from these piloting trials was that it represented a good way of collecting, recording and sharing informal learning that otherwise could easily be forgotten. Several benefits were reported between the two communities such as being helpful in managing competences and human resources within an institution.

Some applications of fractional calculus in engineering

Fractional Calculus FC goes back to the beginning of the theory of differential calculus. Nevertheless, the application of FC just emerged in the last two decades, due to the progress in the area of chaos that revealed subtle relationships with the FC concepts. In the field of dynamical systems theory some work has been carried out but the proposed models and algorithms are still in a preliminary stage of establishment. Having these ideas in mind, the paper discusses FC in the study of system dynamics and control. In this perspective, this paper investigates the use of FC in the fields of controller tuning, legged robots, redundant robots, heat diffusion, and digital circuit synthesis.

Structuring and moodleing a course: case studies at the polytechnic of Porto - School of engineering

This work presents a comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, in respect to the usage of a particular Learning Management System, i.e. Moodle, and its impact on students' results. Even though positive correlation factors exist, e.g. between the number of Moodle accesses versus the final exam grade obtained by each student, the explanation behind it may not be straightforward. Mapping this particular factor to course numbers reveals that the quality of the resources might be preponderant and not only their quantity. This paper also addresses teachers who used this platform as a complement to their courses (b-learning) and identifies some particular issues they should be aware in order to potentiate students' engagement and learning.

VISIR deployment in undergraduate engineering practices

Practical sessions are the backbone of qualification in engineering education. It leads to a better understanding and allows mastering scientific concepts and theories. The lack of the availability of practical sessions at many universities and institutions owing to the cost and the unavailability of instructors the most of the time caused a significant decline in experimentation in engineering education over the last decades. Recently, with the progress of computer-based learning, remote laboratories have been proven to be the best alternative to the traditional ones, regarding to its low cost and ubiquity. Some universities have already started to deploy remote labs in their practical sessions. This contribution compiles diverse experiences based on the deployment of the remote laboratory, Virtual Instrument Systems in Reality (VISIR), on the practices of undergraduate engineering grades at various universities within the VISIR community. It aims to show the impact of its usage on engineering education concerning the assessments of students and teachers as well.

Engaging students by moodleing a course? Case studies at the Polytechnic of Porto–School of Engineering

This work presents a comparative study covering four different courses lectured at the Polytechnic of Porto - School of Engineering, regarding the usage of a particular Learning Management System, i.e. Moodle, and its impact on students' results. This study addresses teachers who used this platform as a complement to their courses (b-learning) and identifies some particular issues in order to potentiate students' engagement and learning. Even though positive correlation factors exist, e.g. between the number of Moodle accesses versus the final exam grade obtained by each student, the explanation behind it may not be straightforward. Mapping this particular factor to course numbers reveals that the quality of the resources might be preponderant and not only their quantity. These results point to the fact that some dynamic resources might enlarge students' engagement.

Recomendações personalizadas de alunos em sistemas de hipermédia adaptativa educacional usando Data Mining

O aumento de tecnologias disponíveis na Web favoreceu o aparecimento de diversas formas de informação, recursos e serviços. Este aumento aliado à constante necessidade de formação e evolução das pessoas, quer a nível pessoal como profissional, incentivou o desenvolvimento área de sistemas de hipermédia adaptativa educacional - SHAE. Estes sistemas têm a capacidade de adaptar o ensino consoante o modelo do aluno, características pessoais, necessidades, entre outros aspetos. Os SHAE permitiram introduzir mudanças relativamente à forma de ensino, passando do ensino tradicional que se restringia apenas ao uso de livros escolares até à utilização de ferramentas informáticas que através do acesso à internet disponibilizam material didático, privilegiando o ensino individualizado. Os SHAE geram grande volume de dados, informação contida no modelo do aluno e todos os dados relativos ao processo de aprendizagem de cada aluno. Facilmente estes dados são ignorados e não se procede a uma análise cuidada que permita melhorar o conhecimento do comportamento dos alunos durante o processo de ensino, alterando a forma de aprendizagem de acordo com o aluno e favorecendo a melhoria dos resultados obtidos. O objetivo deste trabalho foi selecionar e aplicar algumas técnicas de Data Mining a um SHAE, PCMAT - Mathematics Collaborative Educational System. A aplicação destas técnicas deram origem a modelos de dados que transformaram os dados em informações úteis e compreensíveis, essenciais para a geração de novos perfis de alunos, padrões de comportamento de alunos, regras de adaptação e pedagógicas. Neste trabalho foram criados alguns modelos de dados recorrendo à técnica de Data Mining de classificação, abordando diferentes algoritmos. Os resultados obtidos permitirão definir novas regras de adaptação e padrões de comportamento dos alunos, poderá melhorar o processo de aprendizagem disponível num SHAE.

Porto Polytechnical Engineering School sustainable development action plan

Porto Polytechnical Engineering School (ISEP), a Global Reporting Initiative training partner in Portugal, has just presented its Sustainable Development Action Plan (PASUS), which main objective is the formation of a new kind of engineers, with a Sustainable Development (SD) philosophy in the core of their academic curricula courses.

Sustainable Development Action Plan at ISEP - From Concept to Engineering Practice

Some of the main challenges in Incorporating Sustainable Development practices into Engineering Education reside in establishing the bridge between concept and application. In particular the relation between value creation and the knowledge economy, innovation and entrepreneurship, as the main vehicles to a relevant application of the sustainable development concept, is not yet part of the majority of the engineering curricula in schools. Porto Polytechnical Engineering School (ISEP), a Global Reporting Initiative training partner in Portugal, as just presented its Sustainable Development Action Plan, with the main objective of creating a new kind of engineers, with Sustainable Development at the core of their degrees. The plan has several issues like publish an annual sustainability report, sustainable buildings, minimization of energy consumption and water policy, waste management, sustainable mobility, green procurement, EMAS certification, research and postgraduate activity and promotion of lectures and seminars in Sustainable Development.

The European Project Semester at ISEP: Learning to Learn Engineering

The European Project Semester at ISEP (EPS@ISEP) is a one semester project-based learning programme addressed to engineering students from diverse scientific backgrounds and nationalities. The students, organized in multicultural teams, are challenged to solve real world multidisciplinary problems, accounting for 30 ECTU. The EPS package, although focused on project development (20 ECTU), includes a series of complementary seminars aimed at fostering soft, project-related and engineering transversal skills (10 ECTU). This paper presents the study plan, resources, operation and results of the EPS@ISEP that was created in 2011 to apply the best engineering education practices and promote the internationalization of ISEP. The results show that the EPS@ISEP students acquire during one semester the scientific, technical and soft competences necessary to propose, design and implement a solution for a multidisciplinary problem.

Assessing Reading Abilities of Mechanical Engineering College Students: A Prospecting Study*

Reading is a basic competence that students have to master to be successful. Despite this fact, recent studies show that there may be a significant decline in the reading abilities of college students, one of the most educated segments of any population. This work is a prospecting study regarding the assessment of reading abilities of college students, namely in the context of Engineering education. Based on an existing screening test for assessing reading difficulties of children and teenagers, this work presents the results obtained by administrating that test to students at a top engineering institution in Portugal. An outcome of this study is the determination of a time range suitable for a massive, time limited, use of the previously mentioned test to assess college students, thus enabling a basic tool that will permit, in future works, to screen reading abilities in wider college populations. This work also shows evidence that ca. 20% of college students present a poor reading performance, revealing a strong need for monitoring college students’ reading abilities along different generations.

Influence of Soil Chemistry and Plant Physiology in the Phytoremediation of Cu, Mn, and Zn

Different anthropogenic sources of metals can result from agricultural, industrial, military, mining and urban activities that contribute to environmental pollution. Plants can be grown for phytoremediation to remove or stabilize contaminants in water and soil. Copper (Cu), manganese (Mn) and zinc (Zn) are trace essential metals for plants, although their role in homeostasis in plants must be strictly regulated to avoid toxicity. In this review, we summarize the processes involved in the bioavailability, uptake, transport and storage of Cu, Mn and Zn in plants. The efficiency of phytoremediation depends on several factors including metal bioavailability and plant uptake, translocation and tolerance mechanisms. Soil parameters, such as clay fraction, organic matter content, oxidation state, pH, redox potential, aeration, and the presence of specific organisms, play fundamental roles in the uptake of trace essential metals. Key processes in the metal homeostasis network in plants have been identified. Membrane transporters involved in the acquisition, transport and storage of trace essential metals are reviewed. Recent advances in understanding the biochemical and molecular mechanisms of Cu, Mn and Zn hyperaccumulation are described. The use of plant-bacteria associations, plant-fungi associations and genetic engineering has opened a new range of opportunities to improve the efficiency of phytoremediation. The main directions for future research are proposed from the investigation of published results.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Optimização do processo de perfuração em maciços rochosos: consequências técnico-económicas

Este estudo centra-se na temática da perfuração de maciços rochosos. A perfuração, dependendo do seu objectivo, diâmetro e comprimento, tem um vasto leque de aplicações incluindo a perfuração para o desmonte com explosivos e colocação de elementos de contenção. Este estudo esta focado na perfuração, tendo normalmente um papel preponderante no rendimento dos ciclos de trabalho em que esta inserida. O processo de perfuração implica uma estreita interacção das ferramentas de perfuração com maciço rochoso a perfurar, como tal um conhecimento rigoroso dos parâmetros geológicos, geotécnicos, geomecânicos do maciço rochoso e preponderante para auxiliar a gestão, do ponto de vista técnico-económico, da operação de perfuração. Este estudo tem como principal objectivo a optimização técnico-económica desta operação através da introdução de processos baseados nas boas práticas de engenharia na perfuração de maciços rochosos, nomeadamente a definição e introdução do processo de afiação dos bits no ciclo de perfuração. Na caracterização do maciço rochoso, para além da pesquisa e recolha bibliográfica pretende-se recorrer a ensaios ”in situ” e laboratoriais que permitam obter informacao útil para a avaliação da perfurabilidade e abrasividade do maciço rochoso.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.