DIGITAL LIBRARIES AND LEARNING OBJECTS REPOSITORIES

Discusses the technological changes that affects learning organizations as well as the human, technical, legal and sustainable aspects regarding learning objects repositories creation, maintenance and use. It presents concepts of information objects and learning objects, the functional requirements needed to their storage at Learning Management Systems. The role of Metadata is reviewed concerning learning objects creation and retrieval, followed by considerations about learning object repositories models, community participation/collaborative strategies and potential derived metrics/indicators. As a result of this desktop research, it can be said that not only technical competencies are critical to any learning objects repository implementation, but it urges that an engaged community of interest be establish as a key to support a learning object repository project. On that matter, researchers are applying Activity Theory (Vygostky, Luria y Leontiev) in order to seek joint perceptions and actions involving learning objects repository users, curators and managers, perceived as critical assets to a successful proposal.

Decision Support Models for Composing and Navigating through e-Learning Objects

Libraries of learning objects may serve as basis for deriving course offerings that are customized to the needs of different learning communities or even individuals. Several ways of organizing this course composition process are discussed. Course composition needs a clear understanding of the dependencies between the learning objects. Therefore we discuss the metadata for object relationships proposed in different standardization projects and especially those suggested in the Dublin Core Metadata Initiative. Based on these metadata we construct adjacency matrices and graphs. We show how Gozinto-type computations can be used to determine direct and indirect prerequisites for certain learning objects. The metadata may also be used to define integer programming models which can be applied to support the instructor in formulating his specifications for selecting objects or which allow a computer agent to automatically select learning objects. Such decision models could also be helpful for a learner navigating through a library of learning objects. We also sketch a graph-based procedure for manual or automatic sequencing of the learning objects.

How can directories of open access repositories improve the reuse of learning objects in building engineering?

This paper studies the use of directories of open access repositories worldwide (DOARW) to search Spanish repositories containing learning objects in the field of building engineering (BE). Results show that DOARW are powerful tools, but deficiencies (indicated in this study) have to be solved in order to obtain more accurate searches, and to facilitate repository-finding for potential users who are seeking learning objects (LOs) for reuse. Aiming to contribute to the promotion of the reuse of Spanish LOs, this study exposes to the academic community all existing Spanish repositories with LOs, and in particular, the repositories that contain LOs in the field of BE. This paper also studies the critical mass of available content (LOs) in the field of BE in Spain. It has been found to be low.

Leveraging the repository infrastructure to curate institutional heritage materials, learning objects and data assets

Developing and maintaining a successful institutional repository for research publications requires a considerable investment by the institution. Most of the money is spent on developing the skill-sets of existing staff or hiring new staff with the necessary skills. The return on this investment can be magnified by using this valuable infrastructure to curate collections of other materials such as learning objects, student work, conference proceedings and institutional or local community heritage materials. When Queensland University of Technology (QUT) implemented its repository for research publications (QUT ePrints) over 11 years ago, it was one of the first institutional repositories to be established in Australia. Currently, the repository holds over 29,000 open access research publications and the cumulative total number of full-text downloads for these document now exceeds 16 million. The full-text deposit rate for recently-published peer reviewed papers (currently over 74%) shows how well the repository has been embraced by QUT researchers. The success of QUT ePrints has resulted in requests to accommodate a plethora of materials which are ‘out of scope’ for this repository. QUT Library saw this as an opportunity to use its repository infrastructure (software, technical know-how and policies) to develop and implement a metadata repository for its research datasets (QUT Research Data Finder), a repository for research-related software (QUT Software Finder) and to curate a number of digital collections of institutional and local community heritage materials (QUT Digital Collections). This poster describes the repositories and digital collections curated by QUT Library and outlines the value delivered to the institution, and the wider community, by these initiatives.

CrimsonHex: a service oriented repository of specialised learning objects

The corner stone of the interoperability of eLearning systems is the standard definition of learning objects. Nevertheless, for some domains this standard is insufficient to fully describe all the assets, especially when they are used as input for other eLearning services. On the other hand, a standard definition of learning objects in not enough to ensure interoperability among eLearning systems; they must also use a standard API to exchange learning objects. This paper presents the design and implementation of a service oriented repository of learning objects called crimsonHex. This repository is fully compliant with the existing interoperability standards and supports new definitions of learning objects for specialized domains. We illustrate this feature with the definition of programming problems as learning objects and its validation by the repository. This repository is also prepared to store usage data on learning objects to tailor the presentation order and adapt it to learner profiles.

Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Something’s coming, something good: Identifying TPACK competence in pre-service teachers’ analyses of learning objects

There is a song at the beginning of the musical, West Side Story, where the character Tony sings that “something’s coming, something good.” The song is an anthem of optimism, brimming with promise. This paper is about the long-held promise of information and communication technology (ICT) to transform teaching and learning, to modernise the learning environment of the classroom, and to create a new digital pedagogy. But much of our experience to date in the schooling sector tells more of resistance and reaction than revolution, of more of the same but with a computer in the corner and of ICT activities as unwelcome time-fillers/time-wasters. Recently, a group of pre-service teachers in a postgraduate primary education degree in an Australian university were introduced to learning objects in an ICT immersion program. Their analyses and related responses, as recorded in online journals, have here been interpreted in terms of TPACK (Technological Pedagogical and Content Knowledge). Against contemporary observation, these students generally displayed high levels of competence and highly positive dispositions of students to the integration of ICT in their future classrooms. In short, they displayed the same optimism and confidence as the fictional “Tony” in believing that something good was coming.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

162 p.