Learning Analytics in a Liberal Arts Context

How would we rewrite Malcolm’s synthesis of learning analytics and big data for a liberal arts context?

 

 

Whenever I talk about digital learning I never start with the digital. The focus is always on the learning. Digital is a tool, a mechanism, and a bridge that will ideally help us to reach our real goals around learning.

 

My thinking is that we should think and talk about learning analytics and big data in much the same way as most of us talk about other educational technologies. We should be clear about our values and our objectives. We should be the first to be critical of the use of learning analytics and big data in cases where investing in these methods and technologies do not support our core goals.  

 

In the liberal arts context, our core educational values are about developing critical thinking and leadership skills. Liberal arts institutions privilege a relational model of teaching and learning. Intensive, rigorous, persistent, and consistent interactions with educators are at the core of the liberal arts learning experience. We strive (I’m not saying we always succeed) to build an educational ecosystem that nurtures passion, vision, ethics, and a sense of responsibility. Learning a body of knowledge and a set of skills is an important element of a liberal arts education. This acquisition of skills and knowledge, however, is in the service of larger educational aims.

 

It is not immediately obvious exactly where learning analytics and big data can be applied to advancing the core values of a liberal arts education. Analytics depends on measurement. How do we measure judgement, honesty, bravery, integrity, and wisdom? The first step, I think, is to have an honest discussion about our goals - and about the potential (and limits) of analytics in reaching them.  It may be that our analytics are more about inputs than outputs. Our unit of analysis may need to move from the individual learner to the class or the program.  We may need to work harder to capture data from sources that are non-obvious. A longitudinal approach may be necessary.  The key is to get the conversation started.

 

 

Interest in learning analytics and big data offers an unparalleled opportunity to get a campus discussion going about evaluation and assessment. This is a discussion that has so been less than pervasive at every level of the typical liberal arts campus. The professionals in the Institutional Advancement Offices and Teaching and Learning Centers have been talking about learning evaluation for many years. Creating productive discussions about evaluation and assessment throughout the larger campus is community is, however, made more difficult as issues of grading and course evaluations often get put into the mix.

 

The excitement around learning analytics provides a terrific opportunity to reset the debate around assessment.  We can, hopefully, move away from a discussion of specific methods - and more towards a conversation about how we can create a culture of assessment on our campuses. All this really means is developing a commitment to make decisions with the benefit of evidence. Teaching and learning decisions shouldn’t be any different.  No professor would argue for a research agenda that was not built on hypothesis testing. The opportunity here is to bring some of the same methods and cultural orientations that are normative in research to learning.  

 

 

The 3rd area where I think investment in learning analytics makes sense in a liberal arts context is in building new institutional competencies and capacities. Most schools have quite strong institutional analytical capabilities. Tasks ranging from accreditation to enrollment management require the ability to track, manage, and add value to data. Research Computing divisions have long partnered with faculty to work with big data for in a range of scholarship activities. 

 

What is potentially new in the liberal arts context is the use of these analytical methods and techniques for the direct promotion of student learning. What is necessary, therefore, in building up capacities in learning analytics is to develop institutional connectors. Someone has to connect faculty engaged in inquiry on student learning to resources and expertise in the Institutional Advancement, Research Computing, and other divisions. Campus professionals who have not been traditionally pulled into the scholarship of teaching and learning (SOTL) need to add this work to their portfolios.  

 

Developing capabilities and competencies in learning analytics also represents a shift to how institutions view their learning technology platforms. The learning management system (LMS) and the media management system (MMS) are rich sources of potential learning intelligence. How learning data from these platforms is collected, analyzed, synthesized, shared, and presented are suddenly key concerns. Data that is not shared is not useful. Analysis efforts that are not grounded in theory and based on hypothesis testing will quickly devolve into fishing expeditions. 

 

Every campus, and in particular liberal arts institutions, needs to evolve the teaching ecosystem to include the utilization of analytics to improve learning.