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

  • Freitas, Fábio [1] ; Ribeiro, Jaime [1] ; Brandao, Catarina [2] ; Reis, Luís Paulo [3] ; de Souza, Francisle Neri [4] ; Costa, António Pedro [1]
    1. [1] Universidade de Aveiro

      Universidade de Aveiro

      Vera Cruz, Portugal

    2. [2] Universidade Do Porto

      Universidade Do Porto

      Santo Ildefonso, Portugal

    3. [3] Universidade do Minho

      Universidade do Minho

      Braga (São José de São Lázaro), Portugal

    4. [4] Centro Universitário Adventista de São Paulo

      Centro Universitário Adventista de São Paulo

      Brasil

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  • Localización: Digital Education Review, ISSN-e 2013-9144, Nº. 32, 2017, págs. 97-117
  • Idioma: inglés
  • Enlaces
  • Resumen

    • 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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