(Advanced) Data Analysis for Linguists

Type of event: Seminars
Institution: Faculty of Humanities
Funding period: 01.04.2022 to 31.03.2023
Short title: DAforLing

Extract from the funding application: "The ability to analyze and evaluate large amounts of data is increasingly becoming a core competence in many industries and scientific fields, as it offers the opportunity to gain well-founded insights and develop effective strategies. This also applies to the discipline of linguistics, which is dedicated to the study of language. Large amounts of data are made available and analyzed, particularly in natural language databases ('corpora'), which can contain several billion words (one A4 page contains 350 to 400 words). Compared to the increasing importance of data literacy in many industries and, in particular, research practice in linguistics, it is clear that current teaching practice in this discipline lags behind the latest developments in big data research."

The Project DaforLing

In order to prepare students for the demands of the modern job market, there is a significant need for data literacy. This need was the trigger for the development of the course concept, which aims to improve the skills of English linguistics students in data evaluation and application as well as the critical interpretation of data. In this context, opportunities were also sought to integrate this topic into the Master's degree program in English Linguistics "English as a World Language (ENGAGE)" and related courses in linguistics in the long term and to give students the opportunity to deepen their skills in data evaluation and application in smaller research projects.

Review and results

In the introductory course "Data Analysis for Linguists", students received a basic introduction to R and RStudio in order to be able to use these tools effectively for their data analyses. They also learned basic concepts of data analysis, such as data types, variables and data preparation. Students also learned methods for describing quantitative data, such as measures of central tendency and dispersion as well as graphical representations of distributions. In addition, knowledge of regression analysis was taught in order to analyze linear relationships between variables. In the in-depth course "Advanced Data Analysis for Linguists", students were taught further advanced methods of data analysis.

The skills and knowledge acquired are applicable in many areas of linguistics and beyond and thus represent an important contribution to the training of professional linguists. However, the project has also revealed unexpected challenges. Although it succeeded in engaging many students who already had an interest in data literacy, it turned out that many English studies students without an existing interest in this topic were reluctant to engage with data-intensive methods. For this reason, our follow-up project "Analyzing Controversial Discourses" within the DDLitLab focuses on developing new didactic approaches that have a broader thematic reference.

Tips from lecturers for lecturers

The increasing importance of data-intensive methods in research and business underlines the significance of the basic idea of the course. Data literacy and the critical interpretation of data continue to be central aspects. These core competencies should also be the focus of future courses. Experience has shown that students particularly benefit from interactive teaching methods and practical exercises and that learning is promoted through application in real research projects. Through self-analysis as a teacher, teaching strategies can be reconsidered and optimized in order to achieve better communication of content and higher student motivation. Many subjects today rely on data-intensive methods, whether in the natural sciences, the humanities or the social sciences. Therefore, the basics of data analysis taught in our teaching project are potentially relevant in other courses or degree programs.