Fuzzy set analysis as a means to understand users of 21st-century learning systems: The case of mobile learning and reflections on learning analytics research

• Published in: Computers in human behavior Vol. 92; pp. 646 - 659
• Main Authors: Pappas, Ilias O., Giannakos, Michail N., Sampson, Demetrios G.
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.03.2019; Elsevier Science Ltd
• Subjects: Adoption; Complexity theory; Configuration; Configurations; Distance learning; Fuzzy sets; Fuzzy-set qualitative comparative analysis (fsQCA); Learning systems; Mobile learning; Qualitative analysis; Regression analysis; User groups; Learning systems; Fuzzy-set qualitative comparative analysis (fsQCA); Mobile learning; Configuration; Adoption
• ISSN: 0747-5632; 1873-7692
• DOI: 10.1016/j.chb.2017.10.010

Mobile technologies and their applications have the potential to benefit various learning contexts. Users’ perceptions of mobile learning (m-learning) technologies are of great importance and precede the successful integration of these technologies in education. M-learning adoption has been investigated in the literature with reference to various factors and learning analytics, but largely without considering the role of different configurations (i.e., specific combinations of variables), and how these configurations might affect the adoption of various user groups. For instance, users with different backgrounds, experiences, learning styles, and so on might not be represented by the one-model-fits-all produced from the common regression approaches. In this study, we briefly review factors that have been proven important in the context of mobile learning adoption, and build on complexity theory and configuration theory in order to explore the causal patterns of factors that stimulate the use of mobile learning. To test its propositions, the study employs fuzzy-set qualitative comparative analysis (fsQCA) on a data sample from 180 experienced m-learning users. Findings indicate eight configurations of cognitive and affective characteristics, and social and individual factors, that explain m-learning adoption. This research study contributes to the literature by (1) offering new insights on how predictors of m-learning adoption interrelate; (2) extending existing knowledge on how cognitive and affective characteristics, and social and individual factors, combine to lead to high m-learning adoption; and (3) presenting a step-by-step methodological approach for how to apply fsQCA in the area of learning systems and learning analytics. •How technology acceptance research can capture complex multidimensional phenomena.•How predictors of m-learning adoption interrelate to form complex configurations.•Eight different solutions can explain high m-learning adoption.•A methodological approach on how to apply fsQCA in learning systems and analytics.