Dokumet ODA is a software for supporting teaching and research with qualitative research methods in the humanities and social sciences (QDA= Qualitative Data Analysis). The software was specially programmed for the "Documentary Method of Interpretation". This methodology, originally developed by Prof. Ralf Bohnsack at the Department of Qualitative Educational Research at the Free University of Berlin, is established in a wide variety of disciplines through relevant textbooks and diverse research activities, is currently taught at more than 40 chairs in Germany and abroad, and is in high demand in further education contexts.
Up to now, paper and pencil or normal word processing and spreadsheet software have essentially been used for evaluation. Especially in larger research projects, however, this approach quickly reaches its limits: Researchers lose the overview and therefore have to focus on a smaller selection of cases. The development of research and teaching software for the Documentary Method was a logical and overdue step in this respect, since software available on the market is only suitable to a very limited extent for working with the quite demanding methodology of the Documentary Method. With DokuMet QDA, on the other hand, different types of empirical material can be analysed in a lege artis structured way without losing the overview. The programme guides the interpreter through systematic and easy-to-learn summarisation steps up to the formation of types, typologies and typologies. The evaluation steps are easy for novices to follow, which is why the software can be used especially in the teaching of social science methods.
QDA programmes already on the market, such as MaxQDA, Atlas ti or NVivo, are more or less explicitly oriented towards the method of qualitative content analysis, even if they emphasise their "methodological openness": The empirical material is provided with keywords ("coded") according to a "coding scheme" in order to then compare, e.g. in an interview study, text passages from different interviews that have been coded in the same way (for example, all passages that have been coded with the code "violence in the family"). This is not valid from the point of view of the methodology of the documentary method, as it involves a decontextualisation of the material, because in an interview, most text passages are preceded by other sequences that only contribute to an adequate understanding of this one passage.
In DokuMet QDA, on the other hand, the software is programmed in such a way that the material can be reconstructed in its sequential structure and the following questions can be answered: How, i.e. in what context, does someone come to talk about "violence in the family"? How does a corresponding context of meaning build up during the narrative process, which has little in common with the fragments of meaning generated with conventional QDA programmes? DokuMet QDA enables the reconstruction of such contexts of meaning against the background of a systematic comparative horizon formation with contexts of meaning of other cases. In this way, DokuMet QDA leads the user via methodically controlled condensation steps to typifiable results that are on the one hand anchored in cases and on the other hand point far beyond them in the sense of an analytical abstraction.
In the field of language processing by means of artificial intelligence (AI), Natural Language Processing (NLP), great progress has been made recently. The focus here is on so-called General Pretrained Transformer (GPT) models. These are language models that are pre-trained with machine learning on the basis of extremely large amounts of data (e.g. the entire Wikipedia). These models are already capable of answering arbitrary queries from users without special training and are able to carry out very complex tasks independently.
The DTEC-funded KISOFT project at the University of Munich is investigating the extent to which such models are suitable for supporting interpretation with the documentary method. This support can be provided, for example, by the AI 'pre-interpreting' passages from an interview or a group discussion. For this purpose, the CI in the project is taught by means of so-called finetuning on the basis of a larger number of human interpretations how to interpret according to the principles of the Documentary Method (e.g. the separation of formulating and reflecting interpretation). It is planned and already very advanced to implement an AI query in DokuMet QDA, by means of which the interpreters can obtain suggestions for the analysis of their materials during their interpretation work, in a way analogous to a research workshop, except that here the suggestions do not come from colleagues but from the AI.
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