Learn by yourself: The Self-Learning Tools for Qualitative Analysis Software Packages

Abstract

Computer Assisted Qualitative Data Analysis Software (CAQDAS) are tools that help researchers to develop qualitative research projects. These software packages help the users with tasks such as transcription analysis, coding and text interpretation, writing and annotation, content search and analysis, recursive abstraction, grounded theory methodology, discourse analysis, data mapping, and several other types of analysis. This paper focus the new paradigm of self-learning, that presents itself increasingly as a competence to support learning in a proactive way. It further analyses education and CAQDAS with emphasis on the use of CAQDAS in educational research and the self-learning of CAQDAS. The study conducted had two main goals: (1) analyse the self-learning tools of CAQDAS and (2) identify CAQDAS's users learning profile. Six software packages were selected: NVivo, Atlas.ti, Dedoose, webQDA, MAXQDA, and QDA Miner. They were reviewed, taking into account their transversality, language, (self-learning) tools, among other criteria. The results show that there is a considerable demand for information from users regarding the execution of processes in CAQDAS, and that the packages analysed do not guide users towards the self-learning tools that best fit their learning style.