The use of learning management systems: A Longitudinal Case Study

In this article the use of Learning Management Systems (LMS) at the School of Engineering, University of Borås, in the year 2004 and the academic year 2009-2010 is investigated. The tools in the LMS were classified into four groups (tools for distribution, tools for communication, tools for interaction and tools for course administration) and the pattern of use was analyzed. The preliminary interpretation of the results was discussed with a group of teachers from the School of Engineering with long experience of using LMS. High expectations about LMS as a tool to facilitate flexible education, student centered methods and the creation of an effective learning environment is abundant in the literature. This study, however, shows that in most of the surveyed courses the available LMS is predominantly used to distribute documents to students. The authors argue that a more elaborate use of LMS and a transformation of pedagogical practices towards social constructivist, learner centered procedures should be treated as an integrated process of professional development.

Digital Systems for Open Access to Formal and Informal Learning

Today, Digital Systems and Services for Technology Supported Learning and Education are recognized as the key drivers to transform the way that individuals, groups and organizations “learn” and the way to “assess learning” in 21st Century. These transformations influence: Objectives - moving from acquiring new “knowledge” to developing new and relevant “competences”; Methods – moving from “classroom” based teaching to “context-aware” personalized learning; and Assessment – moving from “life-long” degrees and certifications to “on-demand” and “in-context” accreditation of qualifications. Within this context, promoting Open Access to Formal and Informal Learning, is currently a key issue in the public discourse and the global dialogue on Education, including Massive Open Online Courses (MOOCs) and Flipped School Classrooms. This volume on Digital Systems for Open Access to Formal and Informal Learning contributes to the international dialogue between researchers, technologists, practitioners and policy makers in Technology Supported Education and Learning. It addresses emerging issues related with both theory and practice, as well as, methods and technologies that can support Open Access to Formal and Informal Learning. In the twenty chapters contributed by international experts who are actively shaping the future of Educational Technology around the world, topics such as: - The evolution of University Open Courses in Transforming Learning - Supporting Open Access to Teaching and Learning of People with Disabilities - Assessing Student Learning in Online Courses - Digital Game-based Learning for School Education - Open Access to Virtual and Remote Labs for STEM Education - Teachers’ and Schools’ ICT Competence Profiling - Web-Based Education and Innovative Leadership in a K-12 International School Setting are presented. An in-depth blueprint of the promise, potential, and imminent future of the field, Digital Systems for Open Access to Formal and Informal Learning is necessary reading for researchers and practitioners, as well as, undergraduate and postgraduate students, in educational technology.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

An adaptive Newton-method based on a dynamical systems approach

The traditional Newton method for solving nonlinear operator equations in Banach spaces is discussed within the context of the continuous Newton method. This setting makes it possible to interpret the Newton method as a discrete dynamical system and thereby to cast it in the framework of an adaptive step size control procedure. In so doing, our goal is to reduce the chaotic behavior of the original method without losing its quadratic convergence property close to the roots. The performance of the modified scheme is illustrated with various examples from algebraic and differential equations.

A Fast Reconfigurable 2D HW Core Architecture on FPGAs for Evolvable Self- Adaptive Systems

Modern FPGAs with Dynamic and Partial Reconfiguration (DPR) feature allow the implementation of complex, yet flexible, hardware systems. Combining this flexibility with evolvable hardware techniques, real adaptive systems, able to reconfigure themselves according to environmental changes, can be envisaged. In this paper, a highly regular and modular architecture combined with a fast reconfiguration mechanism is proposed, allowing the introduction of dynamic and partial reconfiguration in the evolvable hardware loop. Results and use case show that, following this approach, evolvable processing IP Cores can be built, providing intensive data processing capabilities, improving data and delay overheads with respect to previous proposals. Results also show that, in the worst case (maximum mutation rate), average reconfiguration time is 5 times lower than evaluation time.

Reduced order adaptive models for systems of PDEs using POD

In this work we propose a method to accelerate time dependent numerical solvers of systems of PDEs that require a high cost in computational time and memory. The method is based on the combined use of such numerical solver with a proper orthogonal decomposition, from which we identify modes, a Galerkin projection (that provides a reduced system of equations) and the integration of the reduced system, studying the evolution of the modal amplitudes. We integrate the reduced model until our a priori error estimator indicates that our approximation in not accurate. At this point we use again our original numerical code in a short time interval to adapt the POD manifold and continue then with the integration of the reduced model. Application will be made to two model problems: the Ginzburg-Landau equation in transient chaos conditions and the two-dimensional pulsating cavity problem, which describes the motion of liquid in a box whose upper wall is moving back and forth in a quasi-periodic fashion. Finally, we will discuss a way of improving the performance of the method using experimental data or information from numerical simulations

An analysis of the moderating effects of age in the acceptance of learning management systems

El presente estudio analiza las intenciones de los usuarios acerca del uso de sistemas de tele-enseñanza LMS (Learning Management Systems, basándose en un modelo que integra el Modelo de Aceptación Tecnológica (TAM, Technology Acceptance Model, la Teoría del Comportamiento Percibido (TPB, Theory of Planned Behavior) y la Teoría Unificada de la Aceptación y Uso de la Tecnología (UTAUT, Unified Theory of Acceptance and Use of Technology), tomando la edad como variable moderadora. Así, este artículo estudia la influencia de la intención conductual, la actitud hacia el uso, la facilidad de uso percibida, la utilidad percibida, la norma subjetiva y la influencia social en la intención de utilizar sistemas e-learning LMS. Como antecedentes de estos factores de influencia se plantean las características del sistema y del usuario. El resultado de la revisión teórica es un modelo unificado que ha sido validado con datos recogidos de 94 estudiantes a través de un cuestionario en línea. Estos datos han sido analizados utilizando la técnica de mínimos cuadrados parciales, y los principales resultados confirman la relevancia predictiva del modelo para usuarios de entre 26 y 35 años y de entre 36 y 45 años.

Adaptive neuro-fuzzy inference systems (ANFIS) for supervised classification of competence in MIS based on psychomotor skills

INTRODUCTION: Objective assessment of motor skills has become an important challenge in minimally invasive surgery (MIS) training.Currently, there is no gold standard defining and determining the residents' surgical competence.To aid in the decision process, we analyze the validity of a supervised classifier to determine the degree of MIS competence based on assessment of psychomotor skills METHODOLOGY: The ANFIS is trained to classify performance in a box trainer peg transfer task performed by two groups (expert/non expert). There were 42 participants included in the study: the non-expert group consisted of 16 medical students and 8 residents (< 10 MIS procedures performed), whereas the expert group consisted of 14 residents (> 10 MIS procedures performed) and 4 experienced surgeons. Instrument movements were captured by means of the Endoscopic Video Analysis (EVA) tracking system. Nine motion analysis parameters (MAPs) were analyzed, including time, path length, depth, average speed, average acceleration, economy of area, economy of volume, idle time and motion smoothness. Data reduction was performed by means of principal component analysis, and then used to train the ANFIS net. Performance was measured by leave one out cross validation. RESULTS: The ANFIS presented an accuracy of 80.95%, where 13 experts and 21 non-experts were correctly classified. Total root mean square error was 0.88, while the area under the classifiers' ROC curve (AUC) was measured at 0.81. DISCUSSION: We have shown the usefulness of ANFIS for classification of MIS competence in a simple box trainer exercise. The main advantage of using ANFIS resides in its continuous output, which allows fine discrimination of surgical competence. There are, however, challenges that must be taken into account when considering use of ANFIS (e.g. training time, architecture modeling). Despite this, we have shown discriminative power of ANFIS for a low-difficulty box trainer task, regardless of the individual significances between MAPs. Future studies are required to confirm the findings, inclusion of new tasks, conditions and sample population.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Electronic systems major in the context of integrated language learning lab (ILLLab) proyect = Sistemas electrónicos en el contexto del proyecto Integrated Language Learning Lab (ILLLab)

Este proyecto colabora con la plataforma Integrated Language Learning Lab (ILLLab) aportando una serie de ejercicios de gramática inglesa para una nueva sección de la web basada íntegramente en la especialidad de Sistemas Electrónicos. Complementariamente se adentra en una nueva oportunidad basada en dispositivos móviles inteligentes creando una aplicación para el repaso de los conceptos estudiados en la titulación de Ingeniería Técnica de Telecomunicación en lengua inglesa.

Knowledge-based models for adaptive traffic management systems

This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems.

Active learning and generic competences in an operating systems course

The Bologna Declaration and the implementation of the European Higher Education Area are promoting the use of active learning methodologies. The aim of this study is to evaluate the effects obtained after applying active learning methodologies to the achievement of generic competences as well as to the academic performance. This study has been carried out at the Universidad Politécnica de Madrid, where these methodologies have been applied to the Operating Systems I subject of the degree in Technical Engineering in Computer Systems. The fundamental hypothesis tested was whether the implementation of active learning methodologies (cooperative learning and problem based learning) favours the achievement of certain generic competences (‘teamwork’ and ‘planning and time management’) and also whether this fact improved the academic performance of our students. The original approach of this work consists in using psychometric tests to measure the degree of acquired student’s generic competences instead of using opinion surveys, as usual. Results indicated that active learning methodologies improve the academic performance when compared to the traditional lecture/discussion method, according to the success rate obtained. These methods seem to have as well an effect on the teamwork competence (the perception of the behaviour of the other members in the group) but not on the perception of each students’ behaviour. Active learning does not produce any significant change in the generic competence ‘planning and time management'.

Cooperative Learning Model based on Multi-Agent Architecture for Embedded Intelligent Systems

Cooperative systems are suitable for many types of applications and nowadays these system are vastly used to improve a previously defined system or to coordinate multiple devices working together. This paper provides an alternative to improve the reliability of a previous intelligent identification system. The proposed approach implements a cooperative model based on multi-agent architecture. This new system is composed of several radar-based systems which identify a detected object and transmit its own partial result by implementing several agents and by using a wireless network to transfer data. The proposed topology is a centralized architecture where the coordinator device is in charge of providing the final identification result depending on the group behavior. In order to find the final outcome, three different mechanisms are introduced. The simplest one is based on majority voting whereas the others use two different weighting voting procedures, both providing the system with learning capabilities. Using an appropriate network configuration, the success rate can be improved from the initial 80% up to more than 90%.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.