## 1 Introduction
However, much of the effort in educational and learning analytics has concentrated on analyzing utilization data from learning management systems and/or other enterprise-level systems within an institution to model learner activity [10]. By modeling learner activity in such systems, institutions and educators have been able to identify \"at risk\" students, such as students who may be absent or not active within a virtual class environment. In doing so, institutions have been able to put in place appropriate mitigation measures to help the students stay on track. In other words, learning analytics has been implemented as a warning trigger for institutions and educators. The research done from this perspective has shown positive impacts on achievement and student retention [13]. This application of learning analytics is a worthwhile but a limited first step. The transformational opportunity is in utilizing learning analytics as a driver and a support tool for learner-managed systems of learning. In this paper, we describe a learning analytics model that is grounded in the social cognitive theoretical perspective. At the core of the socio-cognitive psychological perspective is the idea that learning occurs in a social environment and that interactions between behaviors, the individual, and the environment influence each other [14]. The proposed model is a combination of activity data in relation to interaction with an online system as well as the output of what learners have been working on as part of the online system. This combination of activity data and learning artifacts can then be used to drive the design of a learner-managed dashboard enabling the mapping of data to both the social and individual aspects of the learning process. In addition, it could serve to support learners in following a reflective sensemaking approach for integrating the multiple perspectives that arise as part of the social discourse around ill-structured problems. This paper focuses on a learning analytics model that has the potential to support a reflective sensemaking approach in the context of ethical reasoning. A 2004 survey study examining student achievement in college in regards to the liberal education essential outcomes revealed that students had low engagement in outcomes for individual and social responsibility such as ethical reasoning and action [2]. When the survey was repeated in 2010, there was no statistically significant change [15]. People routinely face ethical, ill-structured problems without clear-cut answers in both their personal and professional lives. They tend to reason through these issues and justify the reasoning in different ways depending on their own personal philosophical orientation [16]. Developing deep learning within the context ill-structured ethical dilemmas requires a reflective analytical approach that challenges students to integrate differing perspectives into their own value-based ethical judgments [17]. Within the context of digital learning, the dialog interaction that is necessary for this deep-level processing is not one that is easily attained in the linearly designed online discussion-based environments, nor do such environments make it easy for instructors to support the generation of quality discourse. For this reason, we identified an online tool, Coherence, for testing that enabled learners to engage in online discourse through the creation of connections among differing conceptual perspectives. This online open-source tool generated concept and social networks based on learners' dialogic interaction. It therefore provided us the opportunity to begin exploring what has been elusive thus far in online discussion-based environments: learning with ill-structured problems by making visible the interaction of social and individual learning.