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Final Thesis: A Teaching Platform For QDA

Abstract: Qualitative Data Analysis (QDA) requires a set of competences which can best be aquired through direct experience. Transferring these competences through practical exercises to students in a classroom setting is a challenging task, due to the difficulties which arise with scaling high-touch teaching methods to support large numbers of students. QDAcity is a cloud based web application which supports collaborative QDA, and has been successfully employed in a teaching environment with 40-60 students, however without native support for courses and exercises. This thesis builds upon three previously defined use cases for QDAcity which address teaching QDA in the classroom. The requirements of the thesis are derived from these use cases, with the purpose of extending QDAcity’s feature set with features common to teaching platforms. After the implementation of these requirements, QDAcity will enable instructors to offer courses and exercises to students on the cloud platform and evaluate their solutions using different evaluation methods.

Keywords: QDA, Teaching, QDAcity

PDFs: Master Thesis, Work Description

Reference: Nazeeh Ammari. A Teaching Platform For QDA. Master Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg: 2018.

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