Learning Analytics for Adaptive Systems supporting Self-regulated Social Learning

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Opportunities to facilitate learning, in all forms from elementary schools to post-secondary, workplace, and life-long learning and at different levels, on the Internet and with social media are widely recognized today. However, information offered on the Internet and learning activities in social media, even via formal academic courses, are mainly not based on empirically-proven recommendations from learning sciences. Often, learners are left "on their own" to figure out which study tactics best work for them. This is a serious threat given that most learners have weak skills for self-regulated learning, as reported in learning science research. This can have severe effects on their learning success with numerous societal consequences such as high dropout rates and workforce unprepared for career transitions. Adaptive learning systems are seen as a promising approach to enhancing (self-regulated) learning through real-time adaptations based on observed progress and traits of learners. In contemporary systems, adaptations are focused on one specific group of students to assist (e.g., those at risk) typically by measuring the distance between a learner's progress and the traits expected that learners should have "on average." To unlock the full potential of social adaptive learning systems, profound methods and techniques for harnessing the wealth of trace data logged by learning systems should be developed in order to optimize learning for diverse learner subpopulations. Therefore, the objectives of this research are to (i) propose methods for analysis of self-regulated learning in social context by mining quantitative (e.g., page visit counts) and qualitative (e.g., unstructured text) crowd-sourced data about learning in action; (ii) propose methods for designing adaptive learning systems that motivate deeper learning (e.g., dialogue, peer-discussion, or self-testing) by considering learners' individual differences (e.g., motivation and goal-orientation). These methods will be used to develop new and extend existing (open source and commercial) adaptive systems supporting self-regulated social learning; and (iii) validate the effectiveness of the proposed methods in empirical studies with learners.