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Her research interests include learning analytics to support classroom orchestration, teacher inquiry and institutional decision making.

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When organizations create new knowledge and work practices as a reaction to challenges they face, they often have difficulty to adopt these new practices "on the ground". One of the reasons is that in these cases, individual informal learning and collective knowledge creation are often insufficiently connected. In this paper, we investigate knowledge practices that explain how new knowledge generated in the process of creating and adapting new practices is applied in work situations. We conducted 30 semi-structured interviews in five networks of organizations focusing on knowledge sharing in the German construction industry. Through a qualitative content analysis, we first identified five patterns of situations where individual and collective knowledge interact to implement new work practices. We detail these patterns with four knowledge maturation practices that explain how individuals contribute to collective knowledge development, and three scaffolding practices that explain how individual learning processes are facilitated through help seeking and guiding. Four practices of knowledge appropriation explain how knowledge is adapted and validated in concrete work situations. We combine scaffolding, maturation and appropriation practices into a model of knowledge appropriation that extends workplace learning research by offering a distinctive perspective on the practices that shape the interaction between knowledge creation and individual learning.

A mis padres. AcknowledgementsThe opening lines of this document serve to close this dissertation. In them, I want to sincerely thank all those who have made this work possible, and without whom this dissertation could not have been accomplished. First, I would like to thank my supervisors Miguel and Eduardo, with whom I share the credit of this work, and who have supported me throughout all these years in which we have worked together very closely. They believed in me from the very rst moment I ran into them, and they spent much time and e ort to make this dissertation succeed.Yannis is also one of the great minds that has most in uenced me over these years, and so I owe him much of this work. He insisted in showing me the right way, and he never hesitated to give me on-the-y advice or answers to my questions, despite his busy schedule. Of course, I have to specially thank Asen, Guillermo and Adolfo, who have very closely followed my dissertation, and with whom I had deep and intense discussions.Regarding the development tasks, I have been very lucky to be supported by David and Javier; without their work, this dissertation would have needed much more time, and the de- This fellowship gave me economic stability for almost three years and allowed me to make several short research stays abroad.Despite this ideal working environment, I could never have nished this dissertation without the support of my family and friends. Among them, special words of thanks are to Martín, iii Eduardo, Natalia, Miguel Ángel, Dani, Quini and many others who stood by my side to a greater or lesser extend throughout these years. Here, I would also like to remember all my college friends which led me to the front door of this PhD after a ve-year degree in which we all shared great times and memories.My parents, Julián and Tere, deserve my best words. They raised me in the values of sacri ce and knowledge. They always backed me up in every choice, and helped me overcome the di culties, and what is more important, they still keep doing it day by day. They are the ones that encourage me to get the best out of myself.Finally, my last words of gratitude must be for Bea, because she su ered this dissertation as I did. She was always by my side and never gave up on me. She gave me the strength to face this challenge, being even able to follow me to the end of the world. This dissertation proposes a middleware integration architecture called GLUE! (Group Learning Uniform Environment) that enables the lightweight integration of multiple existing external tools in multiple existing VLEs, overcoming these limitations. GLUE! fosters this integration by imposing few restrictions on VLE and tool providers, as well as by expecting an attainable e ort from developers. Besides, GLUE! facilitates the instantiation and enactment of collaborative learning situations within VLEs, leveraging the VLEs distinctive features for the management of users and groups. By means of GLUE!, practitioners may use external tools as if they were VLE built-in tools, and w...