OntoAIMS: ontological approach to courseware authoring

In this paper we discuss how current ontology concepts can be beneficial for more flexible and semantic rich description of the authoring process and for the provision of authoring support of Intelligent Educational Systems (IES) with respect to the three main authoring modules: domain editing, course composition and resource management. We take a semantic perspective on the knowledge representation within such systems and explore the interoperability between the various ontological structures for domain, instructional and resource modeling and the modeling of the entire authoring process. We build upon our research on Authoring Task Ontology and exemplify it within OntoAIMS system. We present authoring scenarios and show their mapping with authoring task ontology. Further we discuss the OntoAIMS framework for management of electronic learning objects (resources) and their usage in the automatic generation of course templates for the authors. Finally, we describe our architecture, based on the ontological specification of the authoring process

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain\ud knowledge acquisition from textual documents for knowledge-based systems. First, the\ud Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’\ud content through natural language processing and machine learning technologies. Second,\ud it employs a new content model, the Knowledge Puzzle Content Model, which aims to model\ud learning material from annotated content. Annotations are performed semi-automatically based\ud on IBM’s Unstructured Information Management Architecture and are stored in an Organizational\ud memory (OM) as knowledge fragments. The organizational memory is used as a knowledge\ud base for a training environment (an Intelligent Tutoring System or an e-Learning environment).\ud The main objective of these annotations is to enable the automatic aggregation of Learning\ud Knowledge Objects (LKOs) guided by instructional strategies, which are provided through\ud SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects\ud from these LKOs

Identification of Design Principles

This report identifies those design principles for a (possibly new) query and transformation language for the Web supporting inference that are considered essential. Based upon these design principles an initial strawman is selected. Scenarios for querying the Semantic Web illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of the query language to be designed and implemented by the REWERSE working group I4

ATP and Presentation Service for Mizar Formalizations

This paper describes the Automated Reasoning for Mizar (MizAR) service, which integrates several automated reasoning, artificial intelligence, and presentation tools with Mizar and its authoring environment. The service provides ATP assistance to Mizar authors in finding and explaining proofs, and offers generation of Mizar problems as challenges to ATP systems. The service is based on a sound translation from the Mizar language to that of first-order ATP systems, and relies on the recent progress in application of ATP systems in large theories containing tens of thousands of available facts. We present the main features of MizAR services, followed by an account of initial experiments in finding proofs with the ATP assistance. Our initial experience indicates that the tool offers substantial help in exploring the Mizar library and in preparing new Mizar articles

Design Support for non-expert authors in the creation of units of learning - a first exploration

[unpublished]Diverse approaches have been proposed to model educational resources using design rules into IMS Learning Design units of learning. Although varied, these approaches have found limited practical application by teachers, in today’s e-learning. The tools remains tied to the specification with little to no design support for non-experts in the specification. As a result, today’s IMS LD tools cater to LD experts and serve as reference implementations of the specification rather than supporting the non-experts’ engagement in the design process. Consequently, non-experts in the specification cannot undertake the creation of units of learning and remain outside the fold of the IMS LD community. This paper presents features and characteristics of an IMS LD authoring environment to actualize the active participation of non-expert authors in the design of instruction using IMS LD, by addressing the paucity of support afforded to this group with the application of learning design rules to capture the their knowledge. The paper presents an alternate classification of the approaches used in IMS LD authoring tools to support the engagement of non-experts, and based on the salient features of the approaches proposed, the paper reviews the state-of-the-art in IMS LD tools, exemplifying the paucity of IMS LD tools for non-expert authors.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

The Virtual Storyteller: story generation by simulation

The Virtual Storyteller is a multi-agent framework that generates stories based on a concept called emergent narrative. In this paper, we describe the motivation and approach of the Virtual Storyteller, and give an overview of the computational processes involved in the story generation process. We also discuss some of the challenges posed by our chosen approach

A structured model metametadata technique to enhance semantic searching in metadata repository

This paper discusses on a novel technique for semantic searching and retrieval of information about learning materials. A novel structured metametadata model has been created to provide the foundation for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of metametadata (based on the metametadata taxonomy) supported by the ontologies have contributed towards a novel semantic searching mechanism. This research has presented a metametadata model for identifying semantics and describing learning objects in finer-grain detail that allows for intelligent and smart retrieval by automated search and retrieval software

Using ontology in query answering systems: Scenarios, requirements and challenges

Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers. L&C’s company strategy in this area is to design in a step-by-step fashion the essential components of such a system, each component being designed to solve some one part of the total problem and at the same time reflect well-defined needs on the prat of our customers. We compare our strategy with the research roadmap proposed by the Question Answering Committee of the National Institute of Standards and Technology (NIST), paying special attention to the role of ontology