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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.

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Throughout the years competence-based management approaches have proved to be a critical tool in human resource management, vocational training and performance management. As a result, competence-based approaches are often adopted as the key paradigm in both formal or informal education and training programs. Despite this fact, the Technologyenhanced Learning (TeL) research community has only recently considered undertaking research towards technology-enhanced competencebased learning and training. To this end, there exist a number of open issues such as: how can we model competences; how can we assess competences; how can we develop training resources and training activities that target specific competences. The scope of this chapter is to contribute to this field by addressing the issue of competence modeling in technologyenhanced competence-based training, that is, how can we model and represent competence-related information in a system meaningful way.

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