Improving Higher Education: Learning Analytics & Recommender Systems Research

An enduring issue in higher education is student retention to successful graduation. Studies in the U.S. report that average six-year graduation rates across higher-education institutions is 59% and have remained relatively stable over the last 15 years. For those that do complete a college degree, less than half complete within four-years. Requiring additional terms or leaving college without receiving a bachelor's degree has high human and monetary costs and deprives students from the economic benefits of a college credential (over $1 million in a lifetime and even higher in STEM fields). Moreover, when students do not succeed in graduating, local and national communities struggle to create an educated workforce. Estimates indicate that by 2020 over 64% of the jobs in the U.S. will require at least some post-secondary education. These challenges have been recognized by the U.S. National Research Council, which identified that there is a critical need to develop innovative approaches to enable higher-education institutions retain students, ensure their timely graduation, and are well-trained and workforce ready in their field of study. Failure to do so represents a significant problem as it deprives the U.S. of the highly skilled workforce that it needs to successfully compete in the modern world. This talk describes various efforts under way to develop \"Big Data\" methods to analyze in a comprehensive manner, the large and diverse types of education and learning-related data in order to improve undergraduate education. These methods are motivated by and are designed to address various interrelated issues that have a significant impact on college student success and include: (i) academic pathways towards successful and timely graduation from the student perspective; (ii) effective pedagogy by instructors; and (iii) retention and persistence of students from the institutional and advisor perspective. In addition, the talk will discuss areas in which research methods and approaches originally developed by the recommender systems community can be applied to this domain.