The social dimension: a neglected policy item in the Bologna Process

Since its beginning in 1999, the Bologna Process has influenced various aspects of higher education in its member countries, e.g., degree structures, mobility, lifelong learning, social dimension and quality assurance. The social dimension creates the focus of this research. The social dimension entered the Bologna Process agenda in 2001. Despite a decade of reforms, it somehow remained as a vague element and received low scholarly attention. This research addresses to this gap. Firstly, different meanings of the social dimension according to the major European policy actors are analysed. Unfolding the understandings of the actors revealed that the social dimension is mostly understood in terms reflecting the diversity of population on the student body accessing to, progressing in and completing higher education, with a special concern on the underrepresented groups. However, it is not possible to observe a similar commonality concerning the actual policy measures to achieve this goal. Divergence occurs with respect to the addressed underrepresented groups, i.e., all underrepresented groups or people without formal qualifications and mature learners, and the values and institutional interests traditionally promoted by these actors. Secondly, the dissertation discusses the reflection of this social dimension understanding at the national level by looking at cases of Finland, Germany and Turkey. The in-depth analyses show an awareness of the social dimension among most of the national Bologna Process actors and a common understanding of the social dimension goals. However, this understanding has not triggered action in any of the countries. The countries acted on areas which they defined problematic before the Bologna Process. Finally, based on these findings the dissertation discusses the social dimension as a policy item that managed to get into the Bologna Process agenda, but neither grew into an implementable policy, nor drop out of it. To this aim, it makes use of the multiple streams framework and explains the low agenda status social dimension with: i. the lack of a pressing problem definition: the lack of clearly defined indicators and a comprehensive monitoring system, ii. the lack of a viable solution alternative: the proposal of developing national strategies and action plans closed the way to develop generic guidelines for the social dimension to be translated into national policy processes, iii. low political perceptivity: the recent trends opt for increasing efficiency, excellence and exclusiveness discourses rather than ensuring equality and inclusiveness iv. high constraints: the social dimension by definition requires more public funding which is less appreciated and strategic constraints of the actors in allocating their resources v. the type of policy entrepreneur: the social dimension is promoted by an international stakeholder, the European Students’ Union, instead of the ministers responsible for higher education The social dimension remains a policy item in the Bologna Process which is noble enough to agree but not urgent enough to act on.

Exploration tools in formal concept analysis

The development of conceptual knowledge systems specifically requests knowledge acquisition tools within the framework of formal concept analysis. In this paper, the existing tools are presented, and furhter developments are discussed.

Conceptual structures represented by conceptual graphs and formal concept analysis

Conceptual Graphs and Formal Concept Analysis have in common basic concerns: the focus on conceptual structures, the use of diagrams for supporting communication, the orientation by Peirce's Pragmatism, and the aim of representing and processing knowledge. These concerns open rich possibilities of interplay and integration. We discuss the philosophical foundations of both disciplines, and analyze their specific qualities. Based on this analysis, we discuss some possible approaches of interplay and integration.

Intelligent structuring and reducing of association rules with formal concept analysis

Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present relatively small bases for association rules from which all rules can be deduced. We also provide algorithms for their calculation.

Efficient data mining based on formal concept analysis

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining. In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm TITANIC. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

Conceptual knowledge processing with formal concept analysis and ontologies

Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.

Ontology merging with formal concept analysis

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. In this short paper, we address two methods for merging ontologies based on Formal Concept Analysis: FCA-Merge and ONTEX. --- FCA-Merge is a method for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-Merge. The generated result is then explored and transformed into the merged ontology with human interaction. --- ONTEX is a method for systematically structuring the top-down level of ontologies. It is based on an interactive, top-down- knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.

Efficient mining of association rules based on formal concept analysis

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

Algorithmic Computation of Formal Fourier Series

KAAD (Katholischer Akademischer Ausländer-Dienst)