VirRAD: A New Paradigm for Technology Enhanced Learning

In most e-learning scenarios, communication and on-line collaboration is seen as an add-on feature to resource based learning. This paper will endeavour to present a pedagogical framework for inverting this view and putting communities of practice as the basic paradigm for e-learning. It will present an approach currently being used in the development of a virtual Radiopharmacy community, called VirRAD, and will discuss how theory can lead to an instructional design approach to support technologically enhanced learning.(DIPF/Orig.)

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

Construção da plataforma de e-Learning do Instituto Politécnico de Santarém

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

Different Transitions towards Learning at University: Exploring the Heterogeneity of Motivational Processes

Transitions processes in higher education are characterized by new learning situations which pose challenges to most students. This chapter explores the heterogeneity of reactions to these challenges from a perspective of regulation processes. The Integrated Model of Learning and Action is used to identity different patterns of motivational regulation amongst students at university by using mixed distribution models. Six subpopulations of motivational regulation could be identified: students with self-determined, pragmatic, strategic, negative, anxious and insecure learning motivation. Findings about these patterns can be used to design didactic measures that will support students’ learning processes.

Action-Emotion Style, Learning Approach and Coping Strategies, in Undergraduate University Students

Action-Emotion Style (AES) is an affective-motivational construct that describes the achievement motivation that is characteristic of students in their interaction with stressful situations. Using elements from the Type-A Behavior Pattern (TABP), characteristics of competitiveness and overwork occur in different combinations with emotions of impatience and hostility, leading to a classification containing five categories of action-emotion style (Type B, Impatient-hostile type, Medium type, Competitive-Overworking type and Type A). The objective of the present research is to establish how characteristics of action-emotion style relate to learning approach (deep and surface approaches) and to coping strategies (emotion-focused and problem-focused). The sample was composed of 225 students from the Psychology degree program. Pearson correlation analyses, ANOVAs and MANOVAs were used. Results showed that competitiveness-overwork characteristics have a significant positive association with the deep approach and with problem-focused strategies, while impatience-hostility is thus related to surface approach and emotion-focused strategies. The level of action-emotion style had a significant main effect. The results verified our hypotheses with reference to the relationships between action-emotion style, learning approaches and coping strategies.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Learning Sustainability with EPS@ISEP – Development of an Insectarium

8th International Symposium on Project Approaches in Engineering Education (PAEE)

Learning Sustainability with EPS@ISEP – Development of a Water Disinfection System

Proceedings of the 8th International Symposium on Project Approaches in Engineering Education (PAEE), Guimarães, 2016

Education and Training in Occupational Health Psychology: The Case for E-Learning

For the discipline of occupational health psychology (OHP) to continue to evolve and to serve workers effectively it is imperative that education and training provision is available that enables students to acquire knowledge and skills, free of geographical and temporal constraints. This chapter begins with a brief introduction to the historical development of education and training in OHP in Europe. The review culminates with the assertion that higher education institutions are now required to act innovatively in regard to the expansion of provision. One such initiative involves the introduction of e-learning. A case study concerning the implementation of a Masters degree in OHP by e-learning is presented. On the outcomes of the case study, recommendations are offered for the design and implementation of such courses. The chapter concludes by raising some further questions that need to be addressed for education and training provision in OHP to continue to expand.

Virtuellt lärande på distans : en intervjustudie med finländska gymnasiestuderande.

The aim of this study was to gain a deeper understanding of the learning experiences of upper secondary school students in a virtual learning environment. The focus of the study is younger students aged 16–18. Virtual learning environments are defined as collaborative, interactive and communicative digital environments. The main research question was to distinguish the meaning of learning given by the participants. Did the participants perceive learning potential in the virtual learning environment, and if so, what signifies learning potential? Sub-questions were: What enhances learning? What might inhibit learning in a distance course? How do the participants relate to their role as distant learners? Four upper secondary schools in Finland took part in the study. Thirteen upper secondary students were interviewed after a distance course in social studies. During the analysis, four main categories were identified: responsibility, freedom, time and communication. A constructivist approach to learning was adopted while analysing the interviews, and the categories were understood through cognitive, affective and social dimensions of learning. The implications of the study are that a student-centred pedagogy and a social constructivist course design have the potential to motivate students to interact to learn, while the software, such as Second Life, Google+ and Wikibooks, offers them the possibility to do so. The study introduces an empirically supported concept, virtual learning. Virtual learning assumes an active learner who manages different learning spaces while communicating with people and metacognitively assessing the learning process. At the same time, students get used to the virtual and everchanging nature of information and knowledge.

Teaching and learning sexuality in nursing education

Sexuality is recognized as part of holistic nursing care, but its inclusion in clinical practice and nursing training is inconsistent. Based on the question "How students and teachers acknowledge sexuality in teaching and learning?", we developed a study in order to characterize the process of teaching and learning sexuality in a micro perspective of cur- riculum development. We used a mixed methods design with a sequential strategy: QUAN → qual of descriptive and explanatory type. 646 students and teachers participated. The quantitative component used ques- tionnaire surveys. Document analysis was used in the additional component. A curricular dimension of sexuality emerges guided by a behaviourist line and based on a biological vision. The issues considered safe are highlighted and framed in steps of adolescence and adulthood and more attached to female sexuality and the procreative aspect. There is in emergence a hidden curriculum by reference to content from other dimensions of sexuality but less often expressed. Theoretical learning follows a communicational model of reality through ab- straction strategies, which infers a deductive method of learning, with a behaviourist approach to assessment. Clinical teaching ad- dresses sexuality in combination with reproductive health nursing. The influencing factors of teaching and learning of sexuality were also explored. We conclude that the vision of female sexuality taught and learned in relation to women has a projection of care in clinical practice based on the same principles.

Design editorial para crianças dos 6 aos 8 anos

Dissertação de Mestrado para obtenção do grau de Mestre em Design de Comunicação, apresentada na Universidade de Lisboa - Faculdade de Arquitectura.

Perceived benefits, motivations and preferences for foreign language learning by older adults. Insights from an initiative in Cuenca, Ecuador

El presente estudio analiza las percepciones y actitudes que tienen los adultos mayores de la ciudad de Cuenca, Ecuador hacia el aprendizaje del inglés. Un total de 151 adultos mayores (con edad promedio de 70.3 años) respondió a un cuestionario con 50 ítems. Se llevó a cabo análisis factoriales, de regresión múltiple y cluster con el propósito de definir las dimensiones subyacentes en las percepciones, motivaciones y ambiciones de los adultos mayores para aprender un idioma extranjero, y su relación con las características sociodemográficas de los participantes. Los resultados señalan que el interés por estudiar un idioma extranjero está basado en la percepción de que aquello mejora la interacción social de las personas, su desarrollo personal, el funcionamiento y mantenimiento de la mente y memoria, y que activa y vuelve su vida más dinámica. Los resultados además revelaron que la principal motivación de los participantes para tomar un curso de inglés está relacionada con el potencial de usar este idioma en la vida diaria y el de leer profusamente en esa lengua extranjera. La duración del curso y la obtención de un certificado fueron factores determinantes que permitieron agrupar a los participantes en función de sus preferencias en lo que respecta al diseño práctico de un curso de inglés. Adicionalmente, la edad y el nivel de instrucción fueron variables determinantes de motivación que influyeron en la mayor parte de las respuestas dadas por los participantes.

Music Learning Tools for Android Devices

In this paper, a musical learning application for mobile devices is presented. The main objective is to design and develop an application capable of offering exercises to practice and improve a selection of music skills, to users interested in music learning and training. The selected music skills are rhythm, melodic dictation and singing. The application includes an audio signal analysis system implemented making use of the Goertzel algorithm which is employed in singing exercises to check if the user sings the right musical note. This application also includes a graphical interface to represent musical symbols. A set of tests were conducted to check the usefulness of the application as musical learning tool. A group of users with different music knowledge have tested the system and reported to have found it effective, easy and accessible.

THE EFFECTS OF MODELING INSTRUCTION ON STUDENT LEARNING OF A NEWTONIAN FORCE CONCEPT

This study explores the effects of modeling instruction on student learning in physics. Multiple representations grounded in physical contexts were employed by students to analyze the results of inquiry lab investigations. Class whiteboard discussions geared toward a class consensus following Socratic dialogue were implemented throughout the modeling cycle. Lab investigations designed to address student preconceptions related to Newton’s Third Law were implemented. Student achievement was measured based on normalized gains on the Force Concept Inventory. Normalized FCI gains achieved by students in this study were comparable to those achieved by students of other novice modelers. Physics students who had taken a modeling Intro to Physics course scored significantly higher on the FCI posttest than those who had not. The FCI results also provided insight into deeply rooted student preconceptions related to Newton’s Third Law. Implications for instruction and the design of lab investigations related to Newton’s Third Law are discussed.