Predictive modelling: students at risk of failure

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

This paper describes the results on research work performed by the Open Academic Analytics Initiative, an on-going research project aimed at developing an early detection system of college students at academic risk, using data mining models trained using student personal and demographic data, as well as course management data. It reports initial findings on the predictive performance of those models, their portability across pilot programmes in different institutions and the results of interventions applied on those pilots.

Comment from Martyn Cooper: This paper looks at the integration of demographic information and analytics of online activity as a means of predicting students at risk. Importantly, it looks across different programmes in several institutions. Th authors claim that predictive models are more portable than may have been initially assumed. However, again the study is an initial investigation and only looked at data over one semester. They highlight that some students improve after “treatment” and others do not and this needs further investigation.

Citation: E. J. M. Lauria, E. W. Moody, S. M. Jayaprakash, N. Jonnalagadda, J. D. Baron (2013). Open academic analytics initiative: initial research findings, In: Third International Learning Analytics and Knowledge Conference (LAK13), 8-12 April, Leuven, Belgium. | Url: http://dl.acm.org/citation.cfm?id=2460296