Semantic Tagging for Managing Web-based Learning Resources: Models, Methods and a Plattform for Supporting Resource-based Learning
Supervisor(s) and Committee member(s): Examiners: Ralf Steinmetz, Ulrik Schroeder
The knowledge explosion, changing circumstances due to new forms of work and many technical developments determine that the knowledge acquired in education is not sufficient throughout life. Therefore, self-directed learning in the workplace is becoming increasingly important. This is a form of learning where a current information need is met by the self-directed interaction with a wide range of digital resources. Therefore, this learning is called Resource-based Learning. Increasingly, the importance of the Web as an information source grows because it provides many resources that can be used for learning purposes.
However, self-directed Resource-based Learning also poses many challenges to learners. First, digital resources on the Web are usually not didactically prepared and therefore are not intended to be used as learning materials. In addition, the relevant information is often distributed across many different websites. Further, there is already a very large but still rapidly increasing amount of information available on the Web, which can lead to information overload. In the scenario of self-directed learning considered here, there is no teacher who structures the learning process. Therefore, learners have to independently determine their information needs and plan their proceeding. They have to identify, annotate and organize relevant resources for future use. This makes an appropriate management of resources necessary. However, the majority of learners is unsatisfied with the currently available possibilities for the organization of Web resources.
The goal of this thesis is therefore the design and development of a tool to support learners in Resource-based Learning. In particular, the management of resources should be supported and hence challenges mentioned above are addressed.
Self-directed Resource-based Learning requires a personal information and knowledge management by the learners. In literature, several models for managing information and knowledge in organizational and personal scopes exist. For self-directed Resource-based Learning such a model is missing so far. Therefore, a model for Resource-based Learning is developed based on the existing models and on a questionnaire survey conducted in the context of this thesis. This model encompasses several process steps that should be supported by the tool.
The management of resources necessitates the learners to appropriately store the resources, such as based on topic of interest or task to be executed. Tagging is a simple and accepted way to manage any resource on the Web, but its power of expression is restricted. Other forms of resource management can be found in the area of formal knowledge organization (e.g. modeling of a semantic network), however, expert knowledge is usually required to build a semantic network. As a basis for the tool that is developed in the context of this work, therefore, a combination of both forms is proposed, i.e. a semantic network that is created and expanded by the learners using tagging. Core components of this network are resources and tags. Additionally, a learner is able to assign a type to each tag. Therefore, the information whether the tag is e.g. a topic or task can be stored. As part of this thesis, the types of tags that are necessary for the scenario of Resource-based Learning have been analyzed and evaluated.
Furthermore, an algorithm for automatic detection of these tag types is presented, as such an algorithm can reduce the manual maintenance effort for the management of resources. The evaluation of various corpora shows that the knowledge-based algorithm can classify a tag already during the tagging process with an accuracy which is sufficient for the scenario.
Based on the developed model of Resource-based Learning and its requirements for the management of resources, different tools and systems are analyzed with regard to their support of Resource-based Learning. None of the related tools fulfill the requirements appropriately.
Therefore, on the basis of the model’s process steps and the derived functional requirements a concept for a supporting tool is developed. Based on the technical requirements, a system is designed, consisting of a browser add-on, a backend for the management of the knowledge networks and a web-based frontend. The tool is implemented and evaluated in user studies eventually.
The user studies conducted in this work show that the extended form of tagging, based on tag types, is well accepted and allows for appropriate management of resources. Furthermore, the studies show that the implemented tool addresses the challenges of self-directed Resource-based Learning adequately. The present work thus creates a basis for optimizing the approach to self-directed interaction with resources in order to meet an information need.