This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Educational institutions are increasingly turning to learning analytics to identify and intervene with
students at risk of underperformance or discontinuation. However, the extent to which the current
evidence base supports this investment is currently unclear, and particularly so in relation to the
effectiveness of interventions based on predictive models. The aim of the present paper was to
conduct a systematic review and quality assessment of studies on the use of learning analytics in
higher education, focusing specifically on intervention studies. Search terms identified 689 articles,
but only 11 studies evaluated the effectiveness of interventions based on learning analytics. These
studies highlighted the potential of such interventions, but the general quality of the research was
moderate, and left several important questions unanswered. The key recommendation based on this
review is that more research into the implementation and evaluation of scientifically driven learning
analytics is needed to build a solid evidence base for the feasibility, effectiveness, and
generalizability of such interventions. This is particularly relevant when considering the increasing
tendency of educational institutions around the world to implement learning analytics interventions
with only little evidence of their effectiveness.The research reported in this paper was supported by the University of
Exeter’s Effective Learning Analytics project
