This poster synthesizes the design features of a visualization layer applied on the Open Academic Analytics Initiative (OAAI), an open source academic early alert system based on predictive analytics. The poster explores ways to convey the predictive model outputs and benchmark student performances using visually intuitive radar plots.

This paper synthesizes some of the technical decisions, design strategies & concepts developed during the execution of Open Academic Analytics Initiative (OAAI), a research program aimed at improving student retention rates in colleges, by deploying an open-source academic early alert system to identify the students at academic risk. The paper explains the prototype demonstration of the system, detailing several dimensions of data mining & analysis such as: data integration, predictive modelling and scoring with reporting. The paper should be relevant to practitioners and academicians who want to better understand the implementation of an OAAI academic early-alert system.

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. We report initial findings on the predictive performance of those models, their portability across pilot programs in different institutions and the results of interventions applied on those pilots.

In this paper we report ongoing research on the Open Academic Analytics Initiative (OAAI), a project aimed at increasing college student retention by performing early detection of academic risk using data mining methods. The paper describes the goals and objectives of the OAAI, and lays out a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records. Preliminary results on initial model development using several data mining algorithms for classification are presented.