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

Authors

  • Fábio Freitas University of Aveiro
  • Jaime Ribeiro University of Aveiro Polytechnic of Leiria
  • Catarina Brandao University of Porto
  • Luís Paulo Reis University of Minho
  • Francisle Neri de Souza Centro Universitário Adventista de Sao Paulo
  • António Pedro Costa Ludomedia and Research Centre in Didactics and Technology in the Training of Trainers (CIDTFF), University of Aveiro, Portugal

DOI:

https://doi.org/10.1344/der.2017.32.97-117

Keywords:

Computer Assisted Qualitative Data Analysis Software, CAQDAS, Self-learning, Education and CAQDAS.

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, among several other types of analysis. This paper analyses 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: analyse the self-learning tools of CAQDAS and identify CAQDAS's users profile regarding learning. Six software packages were selected: NVivo, Atlas.ti, Dedoose, webQDA, MAXQDA, and QDA Miner. They were then reviewed, taking into account their transversality, language, (self-learning) tools, among several other factors. 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 the user towards the self-learning tools that best fit their learning style.

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Published

2018-01-10

Issue

Number 32, December 2017

Section

Peer Review Articles

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