A PATTERN LANGUAGE FOR SCIENTIFIC TEXTS – SUPPORT FOR THE ACADEMIC EDUCATION PROCESS
Scientific writing in higher education is not only the prerequisite for participation in scientific discourse, but also forms the basis for the development of one's own thinking. Despite the sometimes extensive writing tasks in the various study programmes, the results are often unsatisfactory. The reasons for this could be many but one of the most pressing ones is the difficulties learners face when grasping the complex structure of a scientific paper. Contributing to this might be not only the many components a scientific paper is composed of, but also their often intricated relationships and dependencies.
The central goal of the work presented in this paper is to develop a set of proven structural parts of scientific texts that can be assembled into a model language. Patterns are already used in different contexts to represent typical solutions. Examples can be found in software development and in the optimization of processes. A pattern language can then support the writing process in its different phases by linking the patterns (e.g. from the first creation of the main parts of a report to the detailed argumentation). We suggest mechanisms aimed at both supporting and complementing the creation of a scientific paper. In this regard, we focus on the importance of an early, concrete planning of how selected parts of the desired product, i.e. the intended scientific paper, should be considered.
Three major steps are necessary to achieve that goal: First, the necessary language elements for the description of texts needs to be identified. Typical text patterns can then be formulated with the help of these elements. In order to support the entire writing process, these patterns must finally be linked in a suitable way that allows for the planning and implementation of the text creation in more and more concrete proportions. The initial data for determining the language elements as well as the text patterns are provided by the authors' previous experience in the supervision of approx. 2000 final theses, as well as by a thorough analysis of best practices from the literature on scientific writing. The examination of several examples of students’ submitted work reveals especially faulty structures. They are formulated as anti-patterns which show common mistakes that should be avoided.
This work marks a starting point in the development of a model language, which is open for the integration of further experiences in the form of new patterns. It also raises the question of how text patterns can be used appropriately in an initial learning process of scientific writing and working. Reverse engineering of existing texts could also lead to a better understanding and thus to learning success.