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Analytics generates a system alert for advisors to initiate an Individual Learning Plan (ILP)

Analytics can be a powerful tool for higher education

As the practice of analytics is refined, colleges and universities can place more and better information into the hands of a greater number of people, enabling informed decision-making.

the focus of future analytics efforts can shift from predicting who is going to be successful to customizing learning environments so that the most effective instructional approaches are used for each student. Eventually, institutions may be able to provide unique learning paths, matching instructional activities to a student's learning needs.

How do big data and analytics generate value for higher education? They can improve administrative decision-making and organizational resource allocation. They can identify at-risk learners and provide intervention to assist learners in achieving success. By analyzing discussion messages posted, assignments completed, and messages read in LMSs such as Moodle and Desire2Learn, educators can identify students who are at risk of dropping out.13 They can create, through transparent data and analysis, a shared understanding of the institution’s successes and challenges. They can innovate and transform the college/university system, as well as academic models and pedagogical approaches. They can assist in making sense of complex topics through the combination of social networks and technical and information networks: that is, algorithms can recognize and provide insight into data and at-risk challenges. They can help leaders transition to holistic decision-making through analyses of what-if scenarios and experimentation to explore how various elements within a complex discipline (e.g., retaining students, reducing costs) connect and to explore the impact of changing core elements. They can increase organizational productivity and effectiveness by providing up-to-date information and allowing rapid response to challenges. They can help institutional leaders determine the hard (e.g., patents, research) and soft (e.g., reputation, profile, quality of teaching) value generated by faculty activity.14 They can provide learners with insight into their own learning habits and can give recommendations for improvement. Learning-facing analytics, such as the University of Maryland, Baltimore County (UMBC) Check My Activity tool, allows learners to “compare their own activity . . . against an anonymous summary of their course peers.”15

Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.

Undoubtedly, analytics and big data have a significant role to play in the future of higher education. The growing role of analysis techniques and technologies in government and business sectors affirms this trend. In education the value of analytics and big data can be found in (1) their role in guiding reform activities in higher education, and (2) how they can assist educators in improving teaching and learning.

Learning analytics is essential for penetrating the fog that has settled over much of higher education. Educators, students, and administrators need a foundation on which to enact change. For educators, the availability of real-time insight into the performance of learners—including students who are at-risk—can be a significant help in the planning of teaching activities. For students, receiving information about their performance in relation to their peers or about their progress in relation to their personal goals can be motivating and encouraging. Finally, administrators and decision-makers are today confronted with tremendous uncertainty in the face of budget cuts and global competition in higher education. Learning analytics can penetrate the fog of uncertainty around how to allocate resources, develop competitive advantages, and most important, improve the quality and value of the learning experience.