Exploring the learning potential of an artificial life simulation

CULTURE is an Artificial Life simulation that aims to provide primary school children with opportunities to become actively engaged in the high-order thinking processes of problem solving and critical thinking. A preliminary evaluation of CULTURE has found that it offers the freedom for children to take part in process-oriented learning experiences. Through providing children with opportunities to make inferences, validate results, explain discoveries and analyse situations, CULTURE encourages the development of high-order thinking skills. The evaluation found that CULTURE allows users to autonomously explore the important scientific concepts of life and living, and energy and change within a software environment that children find enjoyable and easy to use.

An evaluation of active lecturing in a computer networks course

The article describes an attempt to improve student learning outcomes in a computer networks course by making lectures more active learning experiences. Quick quizzes, group and individual exercises, the review of student questions, as well as multiple breaks, were incorporated into the weekly three-hour lectures. Student responses to the modified lectures was overwhelmingly positive: over 85% of respondents agreed that the lectures aided understanding, with large majorities of the respondents finding the individual activities useful to their learning. Although student examination performance improved over the previous year, performance on an examination question that was designed to examine deep understanding remained unchanged.

Making distance learning E.R.O.T.I.C.: Applying interpretation principles to distance learning

Distance learners are self-directed learners traditionally taught via study books, collections of readings, and exercises to test understanding of learning packages. Despite advances in e-Learning environments and computer-based teaching interfaces, distance learners still lack opportunities to participate in exercises and debates available to classroom learners, particularly through non-text based learning techniques. Effective distance teaching requires flexible learning opportunities. Using arguments developed in interpretation literature, we argue that effective distance learning must also be Entertaining, Relevant, Organised, Thematic, Involving and Creative—E.R.O.T.I.C. (after Ham, 1992). We discuss an experiment undertaken with distance learners at The University of Queensland Gatton Campus, where we initiated an E.R.O.T.I.C. external teaching package aimed at engaging distance learners but using multimedia, including but not limited to text-based learning tools. Student responses to non-text media were positive.

Learning techniques for automatic email message tagging

A organização automática de mensagens de correio electrónico é um desafio actual na área da aprendizagem automática. O número excessivo de mensagens afecta cada vez mais utilizadores, especialmente os que usam o correio electrónico como ferramenta de comunicação e trabalho. Esta tese aborda o problema da organização automática de mensagens de correio electrónico propondo uma solução que tem como objectivo a etiquetagem automática de mensagens. A etiquetagem automática é feita com recurso às pastas de correio electrónico anteriormente criadas pelos utilizadores, tratando-as como etiquetas, e à sugestão de múltiplas etiquetas para cada mensagem (top-N). São estudadas várias técnicas de aprendizagem e os vários campos que compõe uma mensagem de correio electrónico são analisados de forma a determinar a sua adequação como elementos de classificação. O foco deste trabalho recai sobre os campos textuais (o assunto e o corpo das mensagens), estudando-se diferentes formas de representação, selecção de características e algoritmos de classificação. É ainda efectuada a avaliação dos campos de participantes através de algoritmos de classificação que os representam usando o modelo vectorial ou como um grafo. Os vários campos são combinados para classificação utilizando a técnica de combinação de classificadores Votação por Maioria. Os testes são efectuados com um subconjunto de mensagens de correio electrónico da Enron e um conjunto de dados privados disponibilizados pelo Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Estes conjuntos são analisados de forma a perceber as características dos dados. A avaliação do sistema é realizada através da percentagem de acerto dos classificadores. Os resultados obtidos apresentam melhorias significativas em comparação com os trabalhos relacionados.

Learning to manage in knowledge based organizations: the role of portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm

Teaching and learning mathematics using moodle

This paper summarizes a project that is contributing to a change in the way of teaching and learning Mathematics. Mathematics is a subject of the Accounting and Administration course. In this subject we teach: Functions and Algebra. The aim is that the student understand the basic concepts and is able to apply them in other issues, when possible, establishing a bridge between the issues that they have studied and their application in Accounting. As from this year, the Accounting course falls under in Bologna Process. The teacher and the student roles have changed. The time for theoretical and practical classes has been reduced, so it was necessary to modify the way of teaching and learning. In the theoretical classes we use systems of multimedia projection to present the concepts, and in the practical classes we solve exercises. We also use the Excel and the mathematical open source software wxMaxima. To supplement our theoretical and practical classes we have developed a project called MatActiva based on the Moodle platform offered by PAOL - Projecto de Apoio Online (Online Support Project). With the creation of this new project we wanted to take advantage already obtained results with the previous experiences, giving to the students opportunities to complement their study in Mathematics. One of the great objectives is to motivate students, encourage them to overcome theirs difficulties through an auto-study giving them more confidence. In the MatActiva project the students have a big collection of information about the way of the subject works, which includes the objectives, the program, recommended bibliography, evaluation method and summaries. It works as material support for the practical and theoretical classes, the slides of the theoretical classes are available, the sheets with exercises for the students to do in the classroom and complementary exercises, as well as the exams of previous years. Students can also do diagnostic tests and evaluation tests online. Our approach is a reflexive one, based on the professional experience of the teachers that explore and incorporate new tools of Moodle with their students and coordinate the project MatActiva.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Reasserting curriculum design through virtual learning environments : The case of MAPE

II European Conference on Curriculum Studies. "Curriculum studies: Policies, perspectives and practices”. Porto, FPCEUP, October 16th - 17th.

Planning and implementing a new assessment strategy using an e-learning platform

One of the most difficult issues of e-Learning is the students’ assessment. Being this an outstanding task regarding theoretical topics, it becomes even more challenging when the topics under evaluation are practical. ISCAP’s Information Systems Department is composed of about twenty teachers who have been for several years using an e-learning environment (at the moment Moodle 2.3) combined with traditional assessment. They are now planning and implementing a new e-learning assessment strategy. This effort was undertaken in order to evaluate a practical topic (the use of spreadsheets to solve management problems) common to shared courses of several undergraduate degree programs. The same team group is already experienced in the assessment of theoretical information systems topics using the b-learning platform. Therefore, this project works as an extension to previous experiences being the team aware of the additional difficulties due to the practical nature of the topics. This paper describes this project and presents two cycles of the action research methodology, used to conduct the research. The first cycle goal was to produce a database of questions. When it was implemented in order to be used with a pilot group of students, several problems were identified. Subsequently, the second cycle consisted in solving the identified problems preparing the database and all the players to a broader scope implementation. For each cycle, all the phases, its drawbacks and achievements are described. This paper suits all those who are or are planning to be in the process of shifting their assessment strategy from a traditional to one supported by an e-learning platform.

A distributed system for learning programming on-line

Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the “UVA On-line Judge”, an existing on-line programming trainer with an important number of problems and users, into an effective educational environment consisting of the e-learning platform Moodle and the competitive learning tool QUESTOURnament. The result is the EduJudge system which allows teachers to apply different pedagogical approaches using a proven e-learning platform, makes problems easy to search through an effective search engine, and provides an automated evaluation of the solutions submitted to these problems. The final objective is to provide new learning strategies to motivate students and present programming as an easy and attractive challenge. EduJudge has been tried and tested in three algorithms and programming courses in three different Engineering degrees. The students’ motivation and satisfaction levels were analysed alongside the effects of the EduJudge system on students’ academic outcomes. Results indicate that both students and teachers found that among other multiple benefits the EduJudge system facilitates the learning process. Furthermore, the experi- ment also showed an improvement in students’ academic outcomes. It must be noted that the students’ level of satisfaction did not depend on their computer skills or their gender.

Ensemble: an e-learning framework

E-Learning frameworks are conceptual tools to organize networks of elearning services. Most frameworks cover areas that go beyond the scope of e-learning, from course to financial management, and neglects the typical activities in everyday life of teachers and students at schools such as the creation, delivery, resolution and evaluation of assignments. This paper presents the Ensemble framework - an e-learning framework exclusively focused on the teaching-learning process through the coordination of pedagogical services. The framework presents an abstract data, integration and evaluation model based on content and communications specifications. These specifications must base the implementation of networks in specialized domains with complex evaluations. In this paper we specialize the framework for two domains with complex evaluation: computer programming and computer-aided design (CAD). For each domain we highlight two Ensemble hotspots: data and evaluations procedures. In the former we formally describe the exercise and present possible extensions. In the latter, we describe the automatic evaluation procedures.

O desenvolvimento de um modelo de ensino virtual num contexto de investimento incipiente em e - learning: progressos e desafios

Este artigo relata o desenvolvimento de um modelo de ensino virtual em curso na Universidade dos Açores. Depois de ter sido adotado na lecionação de disciplinas da área da Teoria e Desenvolvimento Curricular em regime de e-learning e b-learning, o modelo foi, no ano académico de 2014/15, estendido à lecionação de outras disciplinas. Além de descrever o modelo e explicar a sua evolução, o artigo destaca a sua adoção no contexto particular de uma disciplina cuja componente online foi lecionada em circunstâncias especialmente desafiadoras. Neste sentido, explica o processo de avaliação da experiência, discute os seus resultados e sugere pistas de melhoria. Essa avaliação enquadra-se num processo de investigação do design curricular – a metodologia que tem sido usada para estudar o desenvolvimento do modelo.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Integration of e-learning systems with repositories of learning objects

This paper describes a communication model to integrate repositories of programming problems with other e-Learning software components. The motivation for this work comes from the EduJudge project that aims to connect an existing repository of programming problems to learning management systems. When trying to use the existing repositories of learning objects we realized that they are mainly specialized search engines and lack features for integration with other e-Learning systems. With this model we intend to clarify the main features of a programming problem repository, in order to enable the design and development of software components that use it. The two main points of this model are the definition of programming problems as learning objects and the definition of the core functions exposed by the repository. In both cases, this model follows the existing specifications of the IMS standard and proposes extensions to deal with the special requirements of automatic evaluation and grading of programming exercises. In the definition of programming problems as learning objects we introduced a new schema for meta-data. This schema is used to represent meta-data related to automatic evaluation that cannot be conveniently represented using the standard: the type of automatic evaluation; the requirements of the evaluation engine; or the roles of different assets - tests cases, program solutions, etc. In the definition of the core functions we used two different web services flavours - SOAP and REST - and described each function as an operation for each type of interface. We describe also the data types of the arguments of each operation. These data types consist mainly on learning objects and their identifications, but include also usage reports and queries using XQuery.

Integrating the LMS in service oriented e-learning systems

Learning management systems are routinely used for presenting, solving and grading exercises with large classes. However, teachers are constrained to use questions with pre-defined answers, such as multiple-choice, to automatically correct the exercises of their students. Complex exercises cannot be evaluated automatically by the LMS and require the coordination of a set of heterogeneous systems. For instance, programming exercises require a specialized exercise resolution environment and automatic evaluation features, each provided by a different type of system. In this paper, the authors discuss an approach for the coordination of a network of eLearning systems supporting the resolution of exercises. The proposed approach is based on a pivot component embedded in the LMS and has two main roles: 1) provide an exercise resolution environment, and 2) coordinate communication between the LMS and other systems, exposing their functions as web services. The integration of the pivot component in the LMS relies on Learning Tools Interoperability (LTI). This paper presents an architecture to coordinate a network of eLearning systems and validate the proposed approach by creating such a network integrated with LMS from two different vendors.