Report on risks of #learning analytics in education #educause

In June 2012 Educause already launched a 44 page report on possible risks and benefits of using learning analytics in Higher Education written by Randall Stiles. While Bert De Coutere and Wilfred Rubens shared a small reflection during Online Educa Berlin assuming that none of us would have ever have made it if quota on Learning analytics would have been used while we were studying, I  wanted to revisit the report of Educause. It is a really nice report taking a deeper look at all the trajectories that can come out of learning analytics and their implementation, as well as guidelines on what to take into account when deploying a learning analytics department inside a Higher Ed institute.

Synopsis of major points raised in the report:

• Risks for institutional leaders 
• Data and information governance risk 
• Data and information quality risk 
• Data and information compliance risk, specifically, those associated with “learning analytics” and the use of cloud services or software as a service (SaaS) 

And gladly adding the conclusion

This guide has provided an introduction and overview of major risk categories for an institution considering investments of time, energy, and money in analytics work. High-quality data and information (meaningful patterns of data) are essential for success, as is a data-friendly culture. It is important to have an understanding of the balance of intuitive and analytics-based decision making that corresponds to the local culture. Major and explicit risks are associated with data privacy and security as a result of various federal and state compliance requirements, as well as any internal policy requirements. In addition, special care and expertise are needed for managing outsourced (cloud or SaaS) services. In the case of the developing research area of learning analytics, questions remain about the ethical use of data—a general understanding of these issues is still in development. In Analytics: The Widening Divide, three key competencies in organizations that have been most successful with analytics are identified. These competencies are recommended as a concise summary of developmental objectives for colleges and universities that choose to invest in analytics. 

1.Information management: the use of methodologies, techniques, and technologies that address data architecture, transformation, movement, storage, integration, and governance of enterprise information and master data management. 

2. Analytical skills and tools: enhance performance by applying advanced techniques such as modeling, deep computing, simulation, data analytics, and optimization to improve efficiency and guide strategies that address specific business processes. 

3. Data-oriented culture: a pattern of behaviors and processes by a group of people who share a belief that having, understanding, and using certain kinds of data and information plays a critical role in the success of their organization.