Model-driven description and validation of composite learning content

Authoring of learning content for courseware systems is a complex activity requiring the combination of a range of design and validation techniques. We introduce the CAVIAr courseware models allowing for learning content description and validation. Model-based representation and analysis of different concerns such as the subject domain, learning context, resources and instructional design used are key contributors to this integrated solution. Personalised learning is particularly difficult to design as dynamic configurations cannot easily be predicted and tested. A tool-supported technique based on CAVIAr can alleviate this complexity through the validation of a set of pedagogical and non-pedagogical requirements. Courseware validation checks intra- and inter-content relationships and the compliance with requirements and educational theories

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

Knowledge lattice approach for web courseware authoring

In this paper, we present the application of knowledge lattice approach in a Web-based courseware authoringsupport system.Knowledge lattice is widely known as ontology in Semantic Web environment.Onlotogy is a formal and declarative representation for knowledge. Knowledge is represented in the form of chunks in several layers. This approach is being used to develop Instructional Management Support System (IMSS).IMSS aims to support students, teachers, courseware developers, administrators, and parents. For example, it supports teachers by managing tasks such as monitoring progress and presenting subject content in a Web-based learning environment. Knowledge lattice is applied as the main interface to navigate the system modules. The lattice is used to represent the subject domain by linking the basic knowledge chunks to more advanced knowledge chunks.To further explain this approach, we define a set of functions related to each knowledge chunks

An information architecture for courseware validation

A lack of pedagogy in courseware can lead to learner rejec- tion. It is therefore vital that pedagogy is a central concern of courseware construction. Courseware validation allows the course creator to specify pedagogical rules and principles which courseware must conform to. In this paper we investigate the information needed for courseware valida- tion and propose an information architecture to be used as a basis for validation

Adaptive courseware design based on learner character

During last decade, a great advance has been done in both theoretical research and software construction of Adaptive Hypermedia Systems (AHS). The article discusses the practical approach taken for authoring and instructional design of adaptive courseware based on learner character, namely learner goals and preferences, learner style, and learner performance and satisfaction level. This approach is adopted at pilot test of ADOPTA - adaptive technology-enhanced platform for edutainment. The authoring process relies strongly on an enhanced learning object metadata support, where learning styles are used for adaptive navigation within the narrative storyboard graph. On other side, both learner knowledge and satisfaction level determine adaptive content selection

Software Construction of an Authoring Tool for Adaptive E-learning Platform

In last decade, more and more platforms for elearning content delivery provide adaptability towards learners goals, styles and performance. Usually, such platforms rely on own authoring tool or use external one in order to create learning materials. Usually, these tools follow modern e-learning standards but are rather complicated to be used and miss interoperability features. In this paper, we present software construction of an authoring tool, which is a part of a platform for building edutainment (education plus entertainment) services – ADOPTA (ADaptive technOlogy-enhanced Platform for eduTAinment). This authoring tool is designed by using Java EE 5 platform and provides inheritance mechanisms for learning object metadata descriptions, metadata for semantic ontology graphs, and good integration with instructor tool for creation of adaptive courseware

Towards automatic construction of adaptable courseware storyboards

In last twenty years, researchers have conducted intensive research in the area of principal models, software architectures and practical system development of adaptive e-learning platforms. Brains are fascinated by great opportunities for radical improvement of the teaching process by means of applying adaptability at different levels. There are two general issues of adaptive e-learning – enabling different educational content delivered to different individuals or groups and, as well, differently formed sequencing and presentation of that content delivery. This paper presents two approaches for creating and delivering training courses adaptable to learners with different learning styles. The first one is implemented within a platform for building edutainment (education plus entertainment) services called ADOPTA (ADaptive technOlogy-enhanced Platform for eduTAinment). By means of ADOPTA, e-learning courses can be created manually by an instructor as directed storyboard graphs. Another feasible approach is to generate them automatically on-the-fly by the adaptive engine. The article discusses advantages and drawbacks of these two approaches for adaptive e-learning course constructio

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)