ABSTRACT
Despite an increasing use of data systems for educational research in this era of big data, little research has been conducted to explore the use of analytics of student data to inform teaching and learning in higher education. This chapter showcases four learning analytics studies that have recently been conducted with student data, including studies on: (1) progression across courses; (2) effects on streaming; (3) disciplinary differences; and (4) alignment between assessments and learning outcomes. The results of these studies reveal numerous strengths of conducting learning analytics. First, learning analytics studies employing student-driven data can be used for more than assessment moderation purposes, allowing practitioners to collect evidence beyond the self-reported, perceptive, qualitative findings at student-staff consultative meetings. Second, analytics studies are highly adaptable to different contexts within higher education and can generate new insights that inform policy and impact curriculum review. Through the discussion of the four studies, this chapter contributes to the field by explaining various possibilities and challenges of using data analytics in higher education across cultural contexts.
