Capturing Application-Domain Specific Patterns in a Web Application: the E-Learning Paradigm, Dimitra Dimitrakopoulou (Greece), Maria Rigou (Greece) , Spiros Sirmakessis (Greece), Athanasios Tsakalidis (Greece), Giannis Tzimas (Greece)
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Designing and maintaining Web applications is one of the major challenges that software industry has to face. Several modeling techniques have been proposed to support this process. In this work we present a methodology for identifying design patterns within an application modelled using WebML, a modelling language for designing data-intensive Web applications. We extend the set of design patterns supported by WebML and exemplify the application of the methodology using an e-learning scenario.

Concepts for Knowledge-Oriented Learning, Johannes Lischka (Austria), Judit Bajnai (Austria), Dimitris Karagiannis (Austria) Download Presentation

This paper introduces a Framework for classifying different learning scenarios. Furthermore, these scenarios have to be supported by a layered stack of models representing different views for respective users. In particular, this paper focuses on the role of instructors. With the help of the mentioned approach, instructors have the ability to plan, manage and structure a course independent of a platform or technology. In order to retrieve and use learning objects two different learning paradigms can be used: first, contemporary solutions with client-server architectures based on XML represent the scenario where instructors know most of the details about their courses; secondly, the service-oriented architecture and Semantic Web technologies reflect the scenario where instructors themselves take the student-role because they are - at the time of the course creation - not fully aware of all specifics about their course. Both scenarios are presented on a conceptual level and blend E-Learning with the discipline of Knowledge Management.

Using Semantic Web Mining Technologies for Personalized E-Learning Experiences, Penelope Markellou (Greece) Ioanna Mousourouli (Greece), Sirmakessis Spiros (Greece), Athanasios Tsakalidis (Greece) Download Presentation

The challenge of the Semantic Web Mining technologies in the e-Learning domain can relate to the provision of personalized experiences for the users. Particularly, these applications can take into consideration the individual needs and requirements of learners. In this paper, we propose a framework for personalised e-Learning based on aggregate usage profiles and a domain ontology. We have distinguished two stages in the whole process, one of offline tasks that includes data preparation, ontology creation and usage mining and one of online tasks that concerns the production of recommendations.

Visualizing Learning Networks for Instructor Support, Vasileios Tzoumakas (UK), Babis Theodoulidis (UK) Download Presentation

This paper introduces a framework for monitoring of the instruction process in on-line computer supported collaborative learning (CSCL) systems. The proposed framework is influenced by the project-based-learning pedagogical approach, which puts emphasis in two main directions; problem solving and communication. Based on this model, we investigate the requirements for effective instruction from the instructor’s point of view. We propose the design of an environment which aims at assisting the instructor in visualizing characteristics and patterns of individual and group interactions within the particular educational approach. The system is based on a layered architecture that uses log-data from the collaboration server, parses them in a relational database, processes them using knowledge extraction algorithms and graphically represents them using graph drawing algorithms. We discuss the data model which combines information pertaining to aspects of the instruction such as performance and group functioning. The visualization we propose is based on graph drawing techniques that use physical analogies, aiming at assisting the instructor in various aspects of computer supported instruction.

Overview & Classification of Web-based Education (Systems, Tools & Practices), Hadzilacos T. (Greece), Xenos M. (Greece)
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The paper presents a classification of web-based education systems, tools and practices, focusing on the learning process and in particular on what the learner does while learning. The classification is based on six basic functions during the learning process: information storing/ retrieving; written expression; communication and publication; experimentation including programming, simulation and virtual reality; administration; and what is today ‘advanced usage’. The paper also presents a brief overview of systems, tools and practices currently used in web-based education, including examples of actual usage characterized according to these learning process functions. Plotting learning functionality requirements vs web-systems functionality provided we can clearly see the needs and opportunities ahead.

Semantic Learning Interventions Using Web Services Technology, Poulia Adamopoulou (Greece), Dimitrios Kanellopoulos (Greece), Evangelos Sakkopoulos (Greece), Athanasios Tsakalidis (Greece) Download

In science learning, cognitive concepts that are distinguished for the complexity of their structure and operation can be understood by using successful metaphors. Conventional instruction is ineffective in dealing with misconceptions. We propose a semantic learning interventions management system that utilizes web services technology. The proposed system aims to be an integrated educational solution that offers interoperable, web service-based, cross-platform learning services. It extends the available educational logic and content by publishing and consuming educational web services. Additionally, the combination of Web Services and Semantic Web offers sophisticated capabilities including automated discovery, composition, invocation, and monitoring.

Using expert systems Technology for Student Evaluation in a Web Based Educational System, Ioannis Hatzilygeroudis (Greece), Panagiotis Chountis (Greece), Christos Giannoulis (Greece) and Constantinos Koutsojannis (Greece)
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In this paper, we present a web-based intelligent education system to help students and tutors in the context of an AI course. We concentrate on the knowledge management and student evaluation aspects of the system. Knowledge management mainly refers to test questions construction and management. Student evaluation refers to the evaluation of the knowledge level of a student with regards to taught concepts. A rule-based expert system helps in student evaluation. A number of statistics provided by the system give valuable information to the tutor.