Georgia D. Solomou

An Ontology Model for Building, Classifying and Using Learning Outcomes

Learning outcomes are statements that should accompany any type of educational material intended for lifelong learning. These statements deliver important information, which works as an indicator for students in the process of learning. However, in order for this information to be further utilizable within the context of intelligent e-learning applications, a more fine-grained definition and structure should be adopted. Having these in mind, we initially assign a strict and rather technical definition for the notion of learning outcomes, which is fully aligned, though, with their educational purpose. We then propose an ontological model for their representation and classification, which fully adheres to this definition. Our ultimate goal is to provide the mean for exploiting all aspects of knowledge implied by such statements within intelligent applications. To bear out this possibility, we apply our model to a selected piece of educational material provided by the Hellenic Open University.

Modeling the characteristics of a learning object for use within e-learning applications

Educational content plays a significant role in the process of delivering knowledge, thats why it needs to be designed carefully, following designated principles. Learning Objects (LOs) constitute a novel approach in the educational contents organization, bearing features that if effectively used could lead to enhanced e-learning services. What is missing from literature, though, is a common agreement about the LOs attributes and structure. For this reason, we initially try to specify the main characteristics of a LO and determine its functionality, especially in the context of distance education. Having realized its fundamental role in the instructional design process, we make explicit its correlation with educational objectives and other aspects of learning. Finally, in an attempt to capture all LOs characteristics and make them utilizable by e-learning applications, we propose a metadata schema, reflecting all features of a LO, as described in this work.

Modeling the knowledge domain of the java programming language as an ontology

Java is a very popular programming language and many study programs in Informatics worldwide include courses particularly designed for its learning. It is considered as the best paradigm for introducing students with object-oriented programming and concepts. Considering Javas popularity, we initially make an attempt to model this language by using a quite expressing and rich knowledge representation structure, like is ontology. Our aim is to capture the semantics of Java concepts in a way that would render them utilizable by intelligent e-learning applications. Because the construction of an ontology is not an easy task, we follow very specific steps when building the Java ontology. We then take advantage of an already implemented model describing the structure of learning outcomes and combine it with our ontology, with a view to offer a more effective way in organizing the course of Java in the Hellenic Open University.

An Ontology-Based Approach in Learning Programming Languages

The learning of programming languages requires the student to develop systematic representations of the lexical constructs and semantic principles of the languages. At the same time, tutors are expected to develop the appropriate training content. In this paper we propose the combination of two techniques to make the above tasks more efficient. Firstly, we propose to use Learning Objects (LOs), for the organization of educational content. At the same time, ontologies are rich knowledge representation structures that can be utilized for modeling the domain concepts. We then present how we can take advantage of the ontological models for two very popular programming languages - that is Java and C - in an attempt to provide tutors with the possibility to better organize their educational material.

Federated semantic search using terminological thesauri for learning object discovery

Purpose The purpose of this paper is to propose a framework and system to address the inability to discover new and authentic learning material and the lack of a single access point for search and browsing of remote learning object repositories (LORs). Design/methodology/approach The authors develop a framework for keyword-based query expansion using SKOS domain terminologies and implement a federated search mechanism integrating various disparate LORs within a learning management system (LMS). Findings The authors show that the expanded query achieves improved information gain and it is applied for federated information access, by simultaneously searching within a number of repositories. Results can be seamlessly aggregated back within the LMS and the course context. Practical implications It is possible to retrieve additional learning objects (LOs) and achieve a corresponding increase in recall, while maintaining precision. SKOS expansion behaves well in a scholarly setting, which, combined with federated search, can contribute toward LOs’ discovery at a balanced cost. The system can be easily integrated with other platforms as well, building on open standards and RESTful communication. Originality/value To the authors’ knowledge, this is the first time SKOS-based query expansion is applied in a federated setting, and for the discovery and alignment of learning objects residing within LORs. The results show that this approach can achieve considerable information gain and that it is possible to strike a balance between search effectiveness, query drift and performance.

Herding Linked Data: Semantic Search and Navigation Among Scholarly Datasets

Linked Data seem to play a seminal role in the establishment of the Semantic Web as the next-generation Web. This is even more important for digital object collections and educational institutions that aim not only at promoting and disseminating their content but also at aiding its discoverability and contextualization. In this paper we show how repository metadata can be exposed as Linked Data, thus enhancing their machine understandability and contributing to the LOD cloud. We use a popular digital repository system, namely DSpace, as our deployment platform. Without requiring additional annotations that would harden the curation task, educational resources are semantically enhanced by reusing and transforming existing metadata values. Our effort comes complete with an updated UI that allows for reasoning-based search and navigation between linked resources within and outside the scope of the digital repository. Therefore ontological descriptions of resources can now be accessed from within the repository’s core context, linked from outside datasets, link to external datasets and get discovered by semantic search.