Cybernetic Autonomy: an Analysis and Critique of Adaptive Learning Systems

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Though personalized learning has been a goal of educators since the days of Aristotle and private tutors, it is only relatively recently that technological and socio-cultural drivers have made personalized learning at scale possible. The development of digital technologies that allow for the analysis of large volumes of student data, combined with greater accessibility to large processing power on cloud-based servers, has led to the increasing feasibility of adaptive learning systems. Furthermore, socio-cultural drivers such as an ever-increasing rate of skill obsolescence and a greater demand for job retraining have led to commercial and corporate interest in adaptive learning. More individuals are also increasingly using digital technology to track or quantify many aspects of their lives, as seen in the cultural phenomenon of the so-called " quantified self " movement. As adaptive learning systems become more accessible to educational institutions, corporations, and individuals, there yet remain serious questions about the conceptual model that informs their design, and the implications that model has for the users of the system. Though the rhetoric of many adaptive learning companies attempts to situate their design philosophy in a tradition of liberal humanism, their conceptual models are in fact better understood as exemplars of a cybernetic design framework. The values of liberal humanism, including autonomy, agency, and freedom of choice are given only lip service in the design of many adaptive learning systems, and the principles of authority, command and control, and automation are, in reality, a much greater influence on their design. It is clear that, with the speculation about the possibilities for adaptive learning systems, coupled with the challenges and risks presented by their use, more research is needed on how they work, and on how they shape the students and teachers that use them. Many companies claim the mantle of " personalized " or adaptive, but few do it in quite the same way, and with quite the same effectiveness. In the rest of this thesis, I will explicate the various components 1 and functions of adaptive learning systems and develop a taxonomy for understanding the various methods used by such systems, in order to better understand how those methods affect the kinds of teaching and learning they make possible. First, in Chapter 1, I will provide a background by which to understand …