Redesigning a First Year Physiology Course using Learning Analytics to Improve Student Performance

Abstract

Learning analytics (LA), a fast emerging concept in higher education, is used to understand and optimize the student learning process and the envi-ronment in which it occurs. Knowledge obtained from the LA paradigm is often utilized to construct statistical models aimed at identifying students who are at risk of failing the unit/course, and to subsequently design inter-ventions that are targeted towards improving the course outcomes for these students. In previous studies, models were constructed using a wide variety of variables, but emerging evidence suggests that the models constructed us-ing course-specific variables are more accurate, and provide a better under-standing of the learning context. For our current study, student performance in the various course assessment tasks was used as a basis for the predictive models and future intervention design, as they are conventionally used to evaluate student learning outcomes and the degree to which the various course learning objectives are met. Further, students in our course are pri-marily first-year university students, who are still unfamiliar with the learning and assessment context of higher education, and this prevents them from adequately preparing for the tasks, and consequently reduces their course performance and outcome. We first constructed statistical models that would be used to identify students who are at risk of failing the course and to identify assessment tasks that students in our course find challeng-ing, as a guide for the design of future interventional activities. Every con-structed predictive model had an excellent capacity to discriminate between students who passed the course and those who failed. Analysis revealed that not only at-risk students, but the whole cohort, would benefit from in-terventions improving their conceptual understanding and ability to con-struct high-scoring answers to Short Answer Questions.