Ensemble: an innovative approach to practice computer programming

Currently, the teaching-learning process in domains, such as computer programming, is characterized by an extensive curricula and a high enrolment of students. This poses a great workload for faculty and teaching assistants responsible for the creation, delivery, and assessment of student exercises. The main goal of this chapter is to foster practice-based learning in complex domains. This objective is attained with an e-learning framework—called Ensemble—as a conceptual tool to organize and facilitate technical interoperability among services. The Ensemble framework is used on a specific domain: computer programming. Content issues are tacked with a standard format to describe programming exercises as learning objects. Communication is achieved with the extension of existing specifications for the interoperation with several systems typically found in an e-learning environment. In order to evaluate the acceptability of the proposed solution, an Ensemble instance was validated on a classroom experiment with encouraging results.

Banc electrònic d'objectes i recursos d'aprenentatge dels estudis de comunicació audiovisual

OdACAV és un banc electrònic d’objectes d’aprenentatge (OdA) que te com a finalitat principal servir als docents de les assignatures troncals dels Estudis de Comunicació Audiovisual (CAV) de la UB (obert també a altres universitats catalanes) implicats o que es vulguin implicar en la innovació docent i pretén facilitar i potenciar la documentació per a la investigació i la recerca a l’entorn del paper innovador dels OdA digitals; així com la patrimonialització dels mateixos. Què és i que no és un OdA de CAV? És tot allò que serveix en un procés d'ensinistrament, d'aprenentatge, de formació - que en els cas que ens ocupa, es tradueix en una col·lecció d'imatges fixes, en un hipertext, en una hipermèdia, un vídeo, etc.-, i la missió dels quals és suscitar l’interès i l'aprofitament en el transvasament dels continguts de les assignatures dels estudis actuals (de la Llicenciatura) i futurs (del Grau) de Comunicació Audiovisual. El projecte sorgeix com a necessitat orgànica de la mateixa naturalesa de l'ensenyament de Comunicació Audiovisual, on en el pla dels continguts els exemples, els referents, els models no són encara prou desenvolupats i costa molt disposar d'OdAs suficientment competents. Les fonts documentals del cinema, de la televisió i dels mitjans en general són la base sobre la que s’han bastit els OdA de la base de dades del web http://www.lmi.ub.es/repositori/ amb l’esperança que resultin adequats a la innovació en els Estudis de Comunicació Audiovisual. És, doncs, un repositori de condició cooperativa, dinàmic i flexible; amb esperit blog/wiki, els objectius del qual són: la creació d'un sistema de dipositació dels OdA; l'establiment d'un sistema de recuperació dels mateixos; la implantació de fluxos d'entrada i sortida; la consolidació d'un observatori d'investigació i de recerca sobre la innovació dels OdA en els entorns i els sistemes educatius actuals; l’articulació de possibles accions patrimonials al voltant de la creació i preservació d’aquests.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Analyzing hidden semantics in social bookmarking of open educational resources

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describingthem. This is especially interesting in the field of open educational resources, asdelicious is a simple way to bridge the institutional point of view (i.e. learningobject repositories) with the individual one (i.e. personal collections), thuspromoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used toimprove descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags createclusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

A catalogue of Primitive Scenario-Types : The First Step to the Automation of Learning Scenarios

Peer-reviewed

Handshake ecosystem for Educational Contents between Institutional Repository and OER based Repository

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

The Effectiveness of Learning Portfolios: A Study of Quality Assurance Programs of Selected Health Regulatory Colleges in Ontario

Health regulatory colleges promote quality practice and continued competence through Quality Assurance (QA) programs. For many colleges, a QA program includes the use of portfolios that incorporate self-directed learning. The purpose of this study was to determine some of the issues surrounding the effectiveness of QA portfolio programs. The literature review revealed that portfolios are valuable tools, but gaps in knowledge include a comparative analysis of QA programs and the perspective of regulatory college administrators. Data were collected through interviews with 6 administrators and a review of 14 portfolio models described on college websites. The results from the two data sources were applied to Robert Stake's responsive evaluation framework to identify issues related to the portfolio's effectiveness (Stake, 1967). The learning components of portfolios were analyzed through the humanist and constructivist lenses. All 14 portfolio models were found to have 3 main components: self-diagnosis, learning plan and activities, and self-evaluation. However, differences were uncovered in learners' autonomy in selecting learning activities, methods of portfolio evaluation, and the relationship between the portfolio and other QA components. The results revealed a dual philosophy of learning in portfolio models and an apparent contradiction between the needs of the individual learner and the organization. Paths for future research include the tenuous relationship between competence and learning, and the impact of technical approaches on selfdirected learning initiatives. A key recommendation is to acknowledge the unique identity of each profession so that health regulatory colleges can address legislative demands and learner needs.

Can you tell if they're learning?

The proliferation of Web-based learning objects makes finding and evaluating online resources problematic. While established Learning Analytics methods use Web interaction to evaluate learner engagement, there is uncertainty regarding the appropriateness of these measures. In this paper we propose a method for evaluating pedagogical activity in Web-based comments using a pedagogical framework, and present a preliminary study that assigns a Pedagogical Value (PV) to comments. This has value as it categorises discussion in terms of pedagogical activity rather than Web interaction. Results show that PV is distinct from typical interactional measures; there are negative or insignificant correlations with established Learning Analytics methods, but strong correlations with relevant linguistic indicators of learning, suggesting that the use of pedagogical frameworks may produce more accurate indicators than interaction analysis, and that linguistic rather than interaction analysis has the potential to automatically identify learning behaviour.

Interoperability for eLearning Service Management and Provision

Learning Objects offer flexibility and adaptability for users to request personalised information for learning. There are standards to guide the development of learning objects. However, individual developers may customise these standards for serving different purposes when defining, describing, managing and providing learning objects, which are normally stored in heterogeneous repositories. Barriers to interoperability hinder sharing of learning services and subsequently affect quality of instructional design as learners expect to be able to receive their personalised learning content. All these impose difficulties to the users in getting the right information from the right sources. This paper investigates the interoperability issues in eLearning services management and provision and presents an approach to resolve interoperability at three levels.

An instructional design model for constructivist learning

The emergent requirements for effective e-learning calls for a paradigm shift for instructional design. Constructivist theory and semiotics offer a sound underpinning to enable such revolutionary change by employing the concepts of Learning Objects. E-learning guidelines adopted by the industry have led successfully to the development of training materials. Inadequacy and deficiency of those methods for Higher Education have been identified in this paper. Based on the best practice in industry and our empirical research, we present an instructional design model with practical templates for constructivist learning.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Integrating constructive feedback in personalised e-learning

When using e-learning material some students progress readily, others have difficulties. In a traditional classroom the teacher would identify those with difficulties and direct them to additional resources. This support is not easily available within e-learning. A new approach to providing constructive feedback is developed that will enable an e-learning system to identify areas of weakness and provide guidance on further study. The approach is based on the tagging of learning material with appropriate keywords that indicate the contents. Thus if a student performs poorly on an assessment on topic X, there is a need to suggest further study of X and participation in activities related to X such as forums. As well as supporting the learner this type of constructive feedback can also inform other stakeholders. For example a tutor can monitor the progress of a cohort; an instructional designer can monitor the quality of learning objects in facilitating the appropriate knowledge across many learners.

Um hipermídia sobre fases da lua para o ensino de astronomia a distância

The interdisciplinary nature of Astronomy makes it a field of great potential to explore various scientific concepts. However, studies show a great lack of understanding of fundamental subjects, including models that explain phenomena that mark everyday life, like the phases of the moon. Particularly in the context of distance education, learning of such models can be favored by the use of technologies of information and communication. Among other possibilities, we highlight the importance of digital materials that motivate and expand the forms of representation available about phenomena and models. It is also important, however, that these materials promote the explicitation of student's conceptions, as well as interaction with the most central aspects of the astronomical model for the phenomenon. In this dissertation we present a hypermedia module aimed at learning about the phases of the moon, drawn from an investigation on the difficulties with the subject during an Astronomy course for teaching training at undergraduate level at UFRN. The tests of three semesters of course were analyzed, taking into account also the alternative conceptions reported in the literature in astronomy education. The product makes use of small texts, questions, images and interactive animations. Emphasizes questions about the illumination of the Moon and other bodies, and their relationship to the sun, the perception from different angles of objects illuminated by a single source, the cause of the alternation between day and night, the identification of Moon's orbit around the Earth and the occurrence of the phases as a result of the position of observing it, and the perception of time involved in the phenomenon. The module incorporated considerations obtained from interviews with students in two poles where its given presential support for students of the course, and subjects from different pedagogical contexts. The final form of the material was used in a real situation of learning, as supplementary material for the final test of the discipline. The material was analyzed by 7 students and 4 tutors, among 56 users, in the period in question. Most students considered that the so called "Lunar Module" made a difference in their learning, the animations were considered the most prominent aspect, the images were indicated as stimulating and enlightening, and the text informative and enjoyable. The analysis of learning of these students, observing their responses to issues raised at the last evaluation, suggested gains in key aspects relating to the understanding of the phases, but also indicates more persistent difficulties. The work leads us to conclude that it is important to seek contributions for the training of science teachers making use of new technologies, with attention to the treatment of computer as a complementary resource. The interviews that preceded the use of the module, and the way student has sought the module if with questions and/or previous conflicts - established great difference in the effective contribution of the material, indicating that it should be used with the mediation of teacher or tutor, or via strategies that cause interactions between students. It is desirable that these interactions are associated with the recovery of memories of the subjects about previous observations and models, as well as the stimulus to new observations of phenomena