Final Thesis: Improving Scalability and UX of the QDAcity Transcription Service

Abstract: Qualitative research usually consists of multiple activities to extract information from unstructured data. The transcription process is one of the steps in qualitative research and is crucial for the results. With a large data set, the transcription process can be time-consuming and tedious. To enhance the efficiency, Qualitative Data Analysis (QDA) software provides tools for the user to organize and process the data as well as to optimize the workflow. QDAcity is such a cloud-based web application for QDA. It provides a transcription service to convert audio to text, a customized dashboard and collaborative research but it is nevertheless under constant development for improvement. For this thesis, a new transcription service was designed and implemented for QDAcity. The transcription editor was also enhanced with a new UI design, a speaker diarization feature, error handling and a guided tour to explain features. It was important, when adding those features, to ensure compatibility with the existing application. These new features were broken down into several steps. First, the transcription service of the QDAcity Java backend was migrated to a new transcription service in Node.js to improve the scalibility and performance. Second, a new feature was implemented: Speaker diarization. This was achieved by restructuring the data format from the cloud-based service and using a React transcription editor library to display the results on the frontend. Third, the UI elements were designed and adjusted to be more intuitive and modern to improve the user experience. After the implementation, the user can now use these new features in QDAcity.

Keywords: None

PDF: Master Thesis

Reference: Shu-Man Cheng. Improving Scalability and UX of the QDAcity Transcription Service. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2025.