Doctoral student in Organizational Leadership and Development. Dissertation in development concerns evaluation of factors likely to increase faculty adoption of learning analytics when there is organizational support. Current possibilities include faculty computer self-efficacy, emotional intelligence, faculty peer support, organizational support.Â
Analytics that integrate data from multiple sources are helping improve organizations and equipping organizational leaders for better decision-making. Learning Analytics (LA) measures, collects, analyzes, and reports data about student learners and their contexts (Gasevic et al., 2015). When the powerful principles of data analytics are applied to data collected from multiple sources, an institution’s ability to understand and predict personal learning needs and performance is increased (Greller & Drachsler, 2012). However, whenever data is being examined, issues related to the ethical treatment of data, data ownership, and data privacy will certainly surface. When organizations begin to analyze behaviors, predict outcomes, and inform strategic decision making, individual resistance to adoption increases due to concerns over the ethical handling of data (Macfayden & Dawson, 2012). The emergence of LA has produced many journal articles and case studies providing insight into the benefits and application of LA for learning and teaching seeking to address the best way to address ethical issues and increase adoption. When Tsai et al. (2019) evaluated organizational resistance to the improvements in teacher quality, learning experience, and administrative efficiency, a new form of leadership was proposed to increase responsiveness to pressures and help manage conflict. When Klein et al. (2019) evaluated the adoption of LA at a large research university, they concluded that a well-communicated implementation plan is what helps ensure quicker adoption by faculty and staff. The purpose of this white paper is to review available literature in order to propose the best ways to ethically handle data in order to accelerate the adoption of LA and sustain its use.