Learning Analytics: A Prescription for Student Success

LeRoy Butler, Ph.D., PgMP, PMP, Chief Information Officer, Lewis University

LeRoy Butler, Ph.D., PgMP, PMP, Chief Information Officer, Lewis University

Modern higher education is a competitive industry.  Students today have more access to education than at any time in our country’s history. Simultaneously, the traditional age undergraduate student population is declining (with further declines projected over the next decade) and the demand for flexible graduate and continuing education opportunities continue to rise.  These trends have created an environment in which data-informed decision-making on how students learn is important to the success of many institutions.

One solution for data-informed problem solving is creating an analytics program that focuses on student learning and outcomes. 

Learning analytics is the process of collecting and analyzing data on learners and their outcomes. The goal is to gain insights into improving student experiences (and environments in which these experiences occur). 

Learning analytics is a form of prescriptive analytics that focuses on the examination of current student data to make real-time and near real-time decisions. Unlike descriptive analytics (which focus on historical data) and predictive analytics (which try to predict the future), prescriptive analytics can provide numerous benefits to higher education institutions. These include the following

- Agile, innovative decision-making - the ability to make real-time organization adjustments 

- Organizational efficiency – making the most of available resources while maximize allocation to academic endeavors

- Continuous improvement – creating an environment for continuous improvement

Despite inherent benefits to higher education, many institutions struggle with creating a viable learning analytics program. One major obstacle is higher education’s traditional approach to analytics.  Of the three major categories of analytics (descriptive, prescriptive, and predictive), higher education has traditionally focused mostly on the descriptive analytics (in hopes of learning from past institutional performance) and predictive analytics (in trying to determine future student needs).  Learning analytics are prescriptive analytics that allows institutions to make informed decision-making in the moment.

“Learning analytics is a prescriptive approach to address many current issues in student learning and success”

Quality learning analytics program have many benefits. Learning analytics improve the students’ experiences by allowing institutions to address concerns and capitalize on positive trends in higher education in real-time or near real-time.  In addition, good analytics programs allow consumer access to data, which can assist them in making decisions about their academic future. Finally, analytics can help institution save resources by eliminating unneeded programs and services that do not enhance the students’ educational experiences.

Regardless of the mission, location, or classification, colleges and universities can build a quality learning analytics program.  All institutions already have access to students’ academic data, the most critical component in any learning analytics program.

The key to successful learning analytics program is each institution’s ability to organize, govern, and support its constituencies in using the many data sources already in existence.  There are six factors needing consideration as institutions move toward development of a learning analytics environment.

Vision

For learning analytics development to be successful, there needs to be a clear vision for what the program means for the institution.  This vision must be support by leadership, endorsed by faculty, and accepted by all members of the organization.

Governance

Data governance is important in maintaining the integrity of data in learning analytics programs. It is important to review and (when appropriate) amend existing data governance to accommodate structure and consistency in decision-making related to your learning analytics program.

Capacity

Colleges and universities will need to review their current capacity to accommodate a learning analytics program. This often includes reorganizing existing data systems, capabilities, and resources from across the institution to deliver the information to needed campus academic and business units. At a minimum, this review should include three components

- Systems that will gather, store, and secure data

- Analytics tools that allow aggregation and examination of data

- Human resources that can interpret and apply outcomes of data analysis to enhance student outcomes

Education

Educating the community on the importance of a learning analytics program is central to its acceptance on campus. The education program should include both educating the community on the need for informed decision making and teaching the community how to use the tools and information provided by the program for students’ benefits.

Communication

Institutions should develop a plan for systemic and appropriate communications to university constituencies on the progress and issues related to their learning analytics program. The communication plan should include all members of the organization involved in student learning outcomes and success.

Support

Support is essential to sustaining any learning analytics program. Providing appropriate support may include retooling technology professionals and institutional research staff to provide the technical and functional support needed by the institution.

Learning analytics can provide invaluable insights into the programs and services that enhance students’ experiences. With appropriate leadership endorsement, colleges and universities can implement and sustain quality learning analytics resources related to student learning. 

However, schools must remember to be patient in building and growing learning analytics programs. These efforts may take considerable time to implement. 

In addition, leaders and practitioners must understand that analytics is not a “one size fits all” endeavor.  Based on mission and strategic focus, learning analytics programs can vary widely from institution to institution. 

Finally, do not forget the training and professional development needed to sustain a learning analytics program.

Learning analytics is a prescriptive approach to address many current issues in student learning and success.

review

Predictive Analytics

Agile