Is the unfolding of the group discussion off-pattern? Improving coordination support in educational forums using mobile devices

A forum is a valuable tool to foster reflection in an in-depth discussion; however, it forces the course mediator to continually pay close attention in order to coordinate learners` activities. Moreover, monitoring a forum is time consuming given that it is impossible to know in advance when new messages are going to be posted. Additionally, a forum may be inactive for a long period and suddenly receive a burst of messages forcing forum mediators to frequently log on in order to know how the discussion is unfolding to intervene whenever it is necessary. Mediators also need to deal with a large amount of messages to identify off-pattern situations. This work presents a piece of action research that investigates how to improve coordination support in a forum using mobile devices for mitigating mediator`s difficulties in following the status of a forum. Based on summarized information extracted from message meta-data, mediators consult visual information summaries on PDAs and receive textual notifications in their mobile phone. This investigation revealed that mediators used the mobile-based coordination support to keep informed on what is taking place within the forum without the need to log on their desktop computer. (C) 2009 Elsevier Ltd. All rights reserved.

Pedagogical Aspects of Using Mobile Devices in the Biology Classroom

The ubiquity and power of personal digital devices make them attractive tools for STEM instructors who would like to stimulate active learning. These devices offer both abundant pedagogical opportunities and worrisome challenges. We will discuss our two years of experience in using mobile devices to teach biology in a community college setting, as well as our observations on the best ways to organize digital-based activities to facilitate student active learning.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Desenvolvimento de uma aplicação para suporte ao t-learning

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

On the use of a low-cost thermal sensor to improve kinect people detection in a mobile robot

[EN]Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform.

ParTec and KarnUMa-Digital tools as a help for freshman learning

[EN]Freshman students always present lower success rates than other levels of students. Digital systems is a course usually taught at first year studentsand its success rate is not very high. In this work we introduce three digital tools to improve freshman learning designed for easy use and one of them is a tool for mobile terminals that can be used as a game. The first tool is ParTec and is used to implement and test the partition technique. This technique is used to eliminate redundant states in finite state machines. This is a repetitive task that students do not like to perform. The second tool is called KarnUMa and is used for simplifying logic functions through Karnaugh Maps. Simplifying logical functions is a core task for this course and although students usually perform this task better than other tasks, it can still be improved. The third tool is a version of KarnUMa, designed for mobile devices. All the tools are available online for download and have been a helpful tool for students.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

E-Learning: Bildung 2.0?

Einzelne Projekte bildeten den Anfang für die E-Learning-Integration im Hochschulbereich. Heute, nach dem Ende der großen E-Learning-Förderprojekte, haben sich an vielen Hochschulen feste E-Learning-Einrichtungen etabliert. Learning Management Systeme (LMS) sind flächendeckend Realität. Die Pädagogische Hochschule Ludwigsburg war in der Lage, E-Learning auch strukturell fest in der Hochschulorganisation zu verankern – ein ‚luxuriöser‘ und beruhigend zukunftsfähiger, nachhaltiger Zustand. Didaktische Konzepte sind erprobt, der Einsatz von E-Learning in den Hochschulveranstaltungen vielzählig in allen Fachgebieten etabliert; die technische Realisation stellt kein Problem mehr dar. Das ‚klassische E-Learning‘ sozusagen haben wir hinter uns – was bringt die mobile Zukunft? Genau jetzt ist der richtige Zeitpunkt festzuhalten, welche Umsetzungen und Anwendungen es für E-Learning an der Pädagogischen Hochschule Ludwigsburg gibt – und dies sicher beispielhaft für viele Hochschulen. Welche Projekte bewegen die Hochschule auf diesem Feld, welche Partner wurden gefunden und welche Antworten auf die Grundfragen des E-Learning? UND: Wie soll es weiter gehen auf dem elektronischen Weg der individualisierten Lernumgebungen; welchen Anforderungen stellen wir uns?

Rahmenwerk für zielgruppenorientiertes Blended E-Learning im MINT-Bereich im Kontext des Lebenslangen Lernens

Das heutige Leben der Menschen ist vom Internet durchdrungen, kaum etwas ist nicht „vernetzt“ oder „elektronisch verfügbar“. Die Welt befindet sich im Wandel, die „Informationsgesellschaft“ konsumiert in Echtzeit Informationen auf mobilen Endgeräten, unabhängig von Zeit und Ort. Dies gilt teilweise auch für den Aus- und Weiterbildungssektor: Unter „E-Learning“ versteht man die elektronische Unterstützung des Lernens. Gelernt wird „online“; Inhalte sind digital verfügbar. Zudem hat sich die Lebenssituation der sogenannten „Digital Natives“, der jungen Individuen in der Informationsgesellschaft, verändert. Sie fordern zeitlich und räumlich flexible Ausbildungssysteme, erwarten von Bildungsinstitutionen umfassende digitale Verfügbarkeit von Informationen und möchten ihr Leben nicht mehr Lehr- und Zeitplänen unterordnen – das Lernen soll zum eigenen Leben passen, lebensbegleitend stattfinden. Neue „Lernszenarien“, z.B. für alleinerziehende Teilzeitstudierende oder Berufstätige, sollen problemlos möglich werden. Dies soll ein von der europäischen Union erarbeitetes Paradigma leisten, das unter dem Terminus „Lebenslanges Lernen“ zusammengefasst ist. Sowohl E-Learning, als auch Lebenslanges Lernen gewinnen an Bedeutung, denn die (deutsche) Wirtschaft thematisiert den „Fachkräftemangel“. Die Nachfrage nach speziell ausgebildeten Ingenieuren im MINT-Bereich soll schnellstmöglich befriedigt, die „Mitarbeiterlücke“ geschlossen werden, um so weiterhin das Wachstum und den Wohlstand zu sichern. Spezielle E-Learning-Lösungen für den MINT-Bereich haben das Potential, eine schnelle sowie flexible Aus- und Weiterbildung für Ingenieure zu bieten, in der Fachwissen bezogen auf konkrete Anforderungen der Industrie vermittelt wird. Momentan gibt es solche Systeme allerdings noch nicht. Wie sehen die Anforderungen im MINT-Bereich an eine solche E-Learning-Anwendung aus? Sie muss neben neuen Technologien vor allem den funktionalen Anforderungen des MINTBereichs, den verschiedenen Zielgruppen (wie z.B. Bildungsinstitutionen, Lerner oder „Digital Natives“, Industrie) und dem Paradigma des Lebenslangen Lernens gerecht werden, d.h. technische und konzeptuelle Anforderungen zusammenführen. Vor diesem Hintergrund legt die vorliegende Arbeit ein Rahmenwerk für die Erstellung einer solchen Lösung vor. Die praktischen Ergebnisse beruhen auf dem Blended E-Learning-System des Projekts „Technische Informatik Online“ (VHN-TIO).

Learning&Information Technologies Cartography

Nowadays, many researches focus their efforts in studies and applications on the Learning area. However, there is a lack of a reference system that permits to know the positioning and the existing links between Learning and Information Technologies. This paper proposes a Cartography where explains the relationships between the elements that compose the Learning Theories and Information Technologies, considering the own features of the learner and the Information Technologies Properties. This intersection will allow us to know what Information Technologies Properties promote Learning Futures.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Detecting unknown attacks in wireless sensor networks that contain mobile nodes

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Opportunities and challenges in the mobile media ecosystem

Increasing availability (andaffordability) of mobile broadband - In 2015 half of the subscriber base will be in 3G/4G, and 80% in 2020 (27% in 2011) - 7.6 billion mobile users by 2020 (5.4 billion in 2011). Mobile subscribers per 100 inhabitants:99%. Increasing availability (and affordability) of smartphones - In 2020 81% of phones sold globally will be smartphones (2.5 billion) from 26% in 2011 (400 million) - 595 million tablets in 2020 (70 million in 2011)