Semantic and structural analysis of web-based learning resources: supporting self-directed resource-based learning

In the knowledge-based society, the maintenance and acquisition of new knowledge are vital for each individual. Changed living and working conditions and the rapid development of technology cause the half-life of knowledge to decrease. Therefore, the knowledge that is acquired in educational institutions is no longer sufficient for an entire lifetime. Thus, self-directed learning at the workplace and in private life is becoming more and more important. At the same time, the Web has become a very important source for knowledge acquisition, as it provides a huge amount of resources containing information that can be utilized for learning purposes. This form of self-directed learning that often involves learning with web resources is commonly referred to as Resource-Based Learning. In particular, it is characterized by a high degree of freedom in choice of resources and execution of the learning process. When utilizing web resources as learning materials, learners face novel challenges: First, relevant information that covers the specific information need of a learner is often distributed over several web resources. This challenge can be addressed by providing adequate retrieval strategies where retrieval is not only restricted to a web search but also involves content that learners have already considered to be relevant. However, the so-called vocabulary gap - the fact that information can be expressed in completely different terminology, e. g. in technical terms or colloquial language - makes retrieval difficult. Further, in contrast to Learning Objects that are often used in educational institutions, web resources rarely include well-structured metadata. As Resource-Based Learning using web resources requires learners to handle and organize a large number of web resources efficiently, the availability of relevant metadata is vital. Eventually, in the majority of self-directed learning settings, the role of the teacher or tutor does not exist. These authorities usually set learning goals according to a curriculum, structure the learning process and assess the learning result. In self-directed learning, the learner has to take over these tasks which would otherwise have been accomplished by the teacher. This thesis examines this form of Resource-Based Learning and derives adequate mechanisms to support this kind of learning. The requirements of supporting Resource-Based Learning are deduced and, based on these requirements, the design and the implementation of a tool called ELWMS. KOM is presented. ELWMS. KOM is a tool that enables learners to organize their self-directed learning process and the contributing learning resources in a personal knowledge network by applying semantically typed tags. In particular, web resources are focused. Web resources are primarily not intended to be used for learning and thus, are rarely didactically adapted to learning scenarios. Further, they infrequently expose metadata that are relevant for learners. ELWMS. KOM is designed to attenuate these short-comings and the resulting challenges for learners by providing an appropriate level of support. The contributions of this thesis comprise of the derivation and implementation of paradigms and technologies that enable such a supporting functionality in ELWMS. KOM. Based on an examination of Learning Objects that are commonly used in learning scenarios in educational institutions, the peculiarities and differences to self-directed learning paradigms are analysed and design decisions for ELWMS. KOM are inferred. These design decisions represent a foundation for the supporting functionalities that are proposed in this thesis. Firstly, the technologies are presented that enable ELWMS. KOM to recommend tags and learning resources to the learner based on a semantic representation of their content. A user study based on ELWMS. KOM shows the need to support monolingual as well as cross-lingual approaches to recommend semantically related tags and resources. An analysis of the approach that has been chosen to determine semantic relatedness is presented. Based on this analysis, several strategies are compared that show potential to reduce the computational complexity of this approach without considerably reducing its quality. Additionally, several extensions to improve the quality this approach that incorporate supplementary semantic properties of a reference corpus are presented and evaluated. Furthermore, this thesis presents an approach to automatically segment web resources in order to support learners in the selection of relevant fragments of a web resource. This segmentation is based on a structural and visual analysis of web resources and yields a set of coherent segments. A user study confirms the quality of this approach. In addition, an approach is introduced that supports learners in the consistent creation of their tagging vocabulary in ELWMS. KOM for the semantic tag type Type. This approach automatically recognizes the web genre of a web resource and is language-independent. Novel features have been developed that allow a reliable classification of web genres. Several evaluations using different feature sets and corpora are presented. Finally, this thesis introduces the tag type Goal that supports learners to plan, execute and evaluate their overall learning process. This support feature has been derived from the theory of Self-Regulated Learning and has been implemented accordingly in ELWMS. KOM. The benefits are shown in two large-scale user studies that have been executed with ELWMS. KOM and the implemented goal setting mechanisms.