Draft Standard for Learning Object Metadata
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Use and Abuse of Reusable Learning Objects
The term Learning Object, first popularized by Wayne Hodgins in 1994 when he named the CedMA working group "Learning Architectures, APIs and Learning Objects", has become the Holy Grail of content creation and aggregation in the computer-mediated learning field. The terms Learning Objects (LOs) and Reusable Learning Objects are frequently employed in uncritical ways, thereby reducing them to mere slogans. The serious lack of conceptual clarity and reflection is evident in the multitude of definitions and uses of LOs. The objectives of this paper are to assess current definitions of the term Learning Object, to articulate the foundational principles for developing a concept of LOs, and to provide a methodology and broad set of guidelines for creating LOs.
LOCUS: learning object classes with unsupervised segmentation
We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (learning object classes with unsupervised segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. A key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. By iteratively updating the belief in the object's position, size, segmentation and pose, LOCUS avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. We show that LOCUS successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. Finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.
Learning object design and sequencing theory
LEARNING OBJECT DESIGN AND SEQUENCING THEORY David Wiley Department of Instructional Psychology and Technology Doctor of Philosophy Given the likelihood of the broad deployment of learning objects-based technology, and the dangers of employing it in an instructionally unprincipled manner, the need for an instructional design theory providing explicit support for the instructional design and use of learning objects is clear. “Theory” here follows Reigeluth’s (1999) definition of design theories as “describ[ing] methods of instruction and the situations in which those methods should be used.” This study reviews, synthesizes, and combines four existing instructional design theories, namely Elaboration Theory (Reigeluth, 1999), Work Model Synthesis (Gibbons, et al., 1995), Domain Theory (Bunderson, Newby, & Wiley, 2000), and the Four-Component Instructional Design model (van Merriënboer, 1997) with new work, the result being a new instructional design theory, Learning Object Design and Sequencing Theory (LODAS). LODAS provides guidelines for the analysis and synthesis of an undifferentiated content area (e.g., English), the application of which produces specifications for the scope and sequence of learning objects. The theory also provides a taxonomy of five learning object types and provides design guidance for the different types of learning objects. Currently, any person or organization that wants to employ learning objects in their instructional design is required to create their own taxonomy of learning objects. The author considers this to be a major cause of the current poverty of practical applications of learning objects. However, taking the taxonomy and learning object design guidelines presented in LODAS, an instructional designer may be able to connect these to the instructional design theory of their choice via the creation of “prescriptive linking material,” a considerably simpler exercise than the creation of a new taxonomy. As the theory is tested, this development has the potential to speed the practical adoption of the learning object approach, allow the simplified application of any instructional design theory to the learning object approach, and provide a common ground for future research in the instructional technology called “learning objects.” ACKNOWLEDGMENTS My thanks go first and foremost to my wife, Elaine, and children, David Enoch and Megumi, who have supported and buoyed me up with love, patience, and understanding throughout my tenure as a graduate student. I also extend my gratitude to my committee co-chairs, Laurie Nelson, who introduced me to the field of instructional technology, specifically instructional design theory, and taught me to be passionate about it, and Vic Bunderson, who invited me to participate in the development of Domain Theory and first suggested validity argument as a means to test instructional design theories. My gratitude also goes to the entire committee, who read and reread quickly in order to help me finalize the study in a timely manner. Finally, special thanks go to my external reviewers, Charles Reigeluth and Brandon Muramatsu, for their tireless reading and commenting. This dissertation was partially funded by the Edumetrics Institute.
Learning object identification rules for information integration
When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects, as well as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.
An Exemplar Model for Learning Object Classes
We introduce an exemplar model that can learn and generate a region of interest around class instances in a training set, given only a set of images containing the visual class. The model is scale and translation invariant. In the training phase, image regions that optimize an objective function are automatically located in the training images, without requiring any user annotation such as bounding boxes. The objective function measures visual similarity between training image pairs, using the spatial distribution of both appearance patches and edges. The optimization is initialized using discriminative features. The model enables the detection (localization) of multiple instances of the object class in test images, and can be used as a precursor to training other visual models that require bounding box annotation. The detection performance of the model is assessed on the PASCAL Visual Object Classes Challenge 2006 test set. For a number of object classes the performance far exceeds the current state of the art of fully supervised methods.
Learning Objects: Resources For Distance Education Worldwide
This article discusses the topic of learning objects in three parts. First, it identifies a need for learning objects and describes their essential components based on this need. Second, drawing on concepts from recent developments in computer science, it describes learning objects from a theoretical perspective. Finally, it describes learning objects in practice, first as they are created or generated by content authors, and second, as they are displayed or used by students and other client groups.
Design principles for authoring dynamic, reusable learning objects
The aim of this paper is to delineate a coherent framework for the authoring of re-purposable learning objects. The approach is orthogonal to the considerable work into learning object metadata and packaging conducted by bodies such as IMS, ADL and the IEEE. The 'learning objects' and standardisation work has been driven largely by adding packaging and metadata to pre-constructed learning artefacts. This work is very valuable. The argument of this paper, however, is that these developments must be supplemented by significant changes in the creation of learning objects. The principal aim of this paper is to delineate authoring principles for reuse and repurposing. The principles are based on a synthesis of ideas from pedagogy and software engineering. These principles are outlined and illustrated from a case study in the area of learning to program in Java.
Learning objects
The purpose of this paper is to introduce a concept known commonly as the "learning object", articulate the foundational principles for developing a concept of LOs, and to provide guidelines for creating LOs. The paper proposes a view of the content packaging of learning objects grounding on the IMS Content Packaging and the SCORM (Sharable Content Object Reference Model).
Learning Object Evaluation: Computer-Mediated Collaboration And Inter-Rater Reliability
Learning objects offer increased ability to share learning resources so that system-wide production costs can be reduced. But how can users select from a set of similar learning objects in a repository and be assured of quality? This article reviews recent developments in the establishment of learning object repositories and metadata standards, and presents a formative reliability analysis of an online, collaborative method for evaluating quality of learning objects. The method uses a 10-item Learning Object Review Instrument (LORI) within a Convergent Participation evaluation model that brings together instructional designers, media developers, and instructors. The inter-rater reliability analysis of 12 raters evaluating eight learning objects identified specific items in LORI that require further development. Overall, the collaborative process substantially increased the reliability and validity of aggregate learning object ratings. The study concludes with specific recommendations including changes to LORI items, a rater training process, and requirements for selecting an evaluation team.
Reusable learning objects: a survey of LOM-based repositories
In this paper, we survey the field of learning object repositories. Learning objects are typically relatively small content components that are meant to be reusable in different contexts. Associated to these learning objects are metadata, so that they can be managed, searched, etc. As the international standardization in this area is making important progress, the number of these repositories is growing rapidly, and the whole field of learning objects is rapidly maturing as a research area in its own right.
A Framework for Evaluating the Quality of Multimedia Learning Resources
This article presents the structure and theoretical foundations of the Learning Object Review Instrument (LORI), an evaluation aid available through the E-Learning Research and Assessment Network at http://www.elera.net. A primary goal of LORI is to balance assessment validity with efficiency of the evaluation process. The instrument enables learning object users to create reviews consisting of ratings and comments on nine dimensions of quality: content quality, learning goal alignment, feedback and adaptation, motivation, presentation design, interaction usability, accessibility, reusability, and standards compliance. The article presents research and practices relevant to these dimensions and describes how each dimension can be interpreted to evaluate multimedia learning resources.
Handbook of Research on Learning Design and Learning Objects: Issues, Applications and Technologies
Designing effective learning experiences is a significant challenge for educators. While there is a wide range of expert advice available for technology supported teaching and learning, translating theories and good practice principles into practice can be a daunting task. The Handbook of Research on Learning Design and Learning Objects: Issues, Applications and Technologies provides an overview of current research and development activity in the area of learning designs in terms of teaching perspective and technological advances. This essential reference brings together xxx studies that encompass the latest research of leaders in the field to provide an up-to-date and complete picture of the subject.
Safety in numbers: Learning categories from few examples with multi model knowledge transfer
Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be useful to reproduce the human capability of recognizing objects even from only one single view. This paper presents an SVM-based model adaptation algorithm able to select and weight appropriately prior knowledge coming from different categories. The method relies on the solution of a convex optimization problem which ensures to have the minimal leave-one-out error on the training set. Experiments on a subset of the Caltech-256 database show that the proposed method produces better results than both choosing one single prior model, and transferring from all previous experience in a flat uninformative way.
Using Podcasts as Audio Learning Objects
Podcasting is an audio content syndication through RSS feeds in the audioblogs. As a new application of audioblogging, podcasting uses the enclosures in RSS feeds for syndication and distribution of audio content to mobile music players on the Web. Despite the advantages of podcasting, there is a need for research that focus on the use of podcasts as learning objects. Incorporating podcasts into e-learning systems require some design and translation work to achieve the pedagogical needs. This paper presents an introductory investigation on approaches to tailor and use audio podcasts as learning objects in learning management systems and learning object repositories.
The trouble with learning objects
Object-oriented instructional design (OOID) offers the promise of universal access to online instructional materials, increased productivity among trainers and educators, and solutions for individualizing learning. However, it is unclear whether it can fulfill these promises to the degree many envision. As with every new instructional technology, it is easy to become overoptimistic about learning objects, but problems of education are always more complex than technology alone can solve. In this article, I take a critical look at the proposed benefits of learning objects described in the published literature, particularly scalability andadaptability. I also look at both the difficulties in defining the term learningobject and the limitations of metaphors used to describe the concept, and concludes with propositions for learning object usage.
A LOM Research Agenda
This paper presents a research agenda on Learning Objects. The main intent is to elaborate on what the authors consider important issues for research on learning objects and their use in education and training. The paper focuses somewhat on metadata related issues, but does not restrict itself to only those aspects that have a direct relationship with metadata.
Identifier management and resolution: conforming the IEEE standard for learning object metadata
Uniform Resource Identifiers are an integral part of the current Architecture of the World Wide Web. This work analyzes the implications and possibilities of using Universal Resource Names as unique and persistent identifiers in systems for management of decentralized content and federated collections. Particularly, discussion focuses on applying such identifiers on the context of a learning object repository that the authors are developing at Universidad Nacional del Litoral, according to the IEEE 1484.12.1 standard for Learning Object Metadata. It is explained why Uniform Resource Locators are inadequate, and why Universal Resource Names are preferable. A standardized resolution service over Hypertext Transfer Protocol is recommended for locating resources, and usage of Uniform Resource Characteristics for accessing Learning Object Metadata is proposed. Finally, a content-negotiation mechanism for selecting the best representation among several format or language variants is outlined. The proposed naming schema provides a doubleindirection mechanism, comparable to the Human- Friendly Names approach proposed by Ballintijn, van Steen, and Tanenbaum for improving scalability and usability in naming replicated resources.
Learning Object Representations for Visual Object Class Recognition
This talk discussed our object-class recognition method that won the classification contest of the Pascal VOC Challenge 2007. We submitted two recognition methods sharing the same underlying image representations defined by a choice of image sampler, local descriptor and global spatial grid. The submitted methods also share the classifier, which is a one-against-rest non-linear Support Vector Machine with chi-square kernel. The methods differ in the way they combine multiple representations (channels). The first method is based on the approach of Zhang et al., where the final similarity measure is the sum of per-channel similarities. The second method employs a genetic algorithm, which is used to determine (on per-class basis) the parameters of the generalized RBF kernel incorporating all the channels, i.e., to estimate the importance of each sampling/description/spatial method for the recognition and to optimize the required level of generalization. Both methods showed superior performance compared to other state-of-the-art submissions.
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