Model-driven transformation and validation of adaptive educational hypermedia using CAVIAr

Authoring of Adaptive Educational Hypermedia is a complex activity requiring the combination of a range of design and validation techniques.We demonstrate how Adaptive Educational Hypermedia can be transformed into CAVIAr courseware validation models allowing for its validation. The model-based representation and analysis of different concerns and model-based mappings and transformations are key contributors to this integrated solution. We illustrate the benefits of Model Driven Engineering methodologies that allow for interoperability between CAVIAr and a well known Adaptive Educational Hypermedia framework. By allowing for the validation of Adaptive Educational Hypermedia, the course creator limits the risk of pedagogical problems in migrating to Adaptive Educational Hypermedia from static courseware

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

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

An information architecture for validating courseware

Courseware validation should locate Learning Objects inconsistent with the courseware instructional design being used. In order for validation to take place it is necessary to identify the implicit and explicit information needed for validation. In this paper, we identify this information and formally define an information architecture to model courseware validation information explicitly. This promotes tool-support for courseware validation and its interoperability with the courseware specifications

Managing quality constraints in technology-managed learning content processes

Technology-enhanced learning content processes consist of individual activities related to the creation, composition, consumption and analysis of content facilitated through services. These service processes are often enacted across different boundaries such as organisations, countries or even languages. Specifically, looking at the quality of learning content and other artefacts and the governance of respective processed through services in this context is important to control quality requirements. We assume a partially automated workflow process for the content lifecycle. We suggest a rule-based constraints monitoring of learning content processes. A learning domain ontology shall capture the key data/content types, activities and constraints, which forms the basis of a rule-based policy monitoring solution that takes content provenance data into account

E-Learning and Intelligent Planning: Improving Content Personalization

Combining learning objects is a challenging topic because of its direct application to curriculum generation, tailored to the students' profiles and preferences. Intelligent planning allows us to adapt learning routes (i.e. sequences of learning objects), thus highly improving the personalization of contents, the pedagogical requirements and specific necessities of each student. This paper presents a general and effective approach to extract metadata information from the e-learning contents, a form of reusable learning objects, to generate a planning domain in a simple, automated way. Such a domain is used by an intelligent planner that provides an integrated recommendation system, which adapts, stores and reuses the best learning routes according to the students' profiles and course objectives. If any inconsistency happens during the route execution, e.g. the student fails to pass an assessment test which prevents him/her from continuing the natural course of the route, the systeGarrido, A.; Morales, L. (2014). E-Learning and Intelligent Planning: Improving Content Personalization. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje. 9(1):1-7. doi:10.1109/RITA.2014.2301886S179