Feeling supported: Enabling students in diverse cohorts through personalised, data-informed feedback

Abstract

Students entering enabling programs as an alternative pathway to University tend to have higher rates of attrition than their peers admitted via more traditional pathways. Students in enabling programs require high levels of personalised feedback to support their transition to study. However, the size and diversity of the enabling student cohort presents formidable challenges for instructors. The field of learning analytics offers a viable solution for scaling the communication of personalised, data-informed feedback to support student learning. This study describes the use of a novel learning analytics-based feedback system called OnTask, to provide personalised feedback and support to students in an enabling course at one Australian higher education institution. An end-of-course student survey (N=41; 17% response rate) was employed to gain insights into their perceptions of personalised, data-informed feedback messages. Using importance-performance analysis (IPA), the survey results indicated that this technology-mediated form of feedback exceeded students’ expectations of learning support, as well as the enhancement of their overall course experience. The implications for using learning analytics and data-informed feedback mechanisms in teaching and learning are discussed.