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Data Mining as Driven by Knowledge Management in Higher Education-Persistence Clustering And Prediction

• What is data mining? • Where does data mining fit in the context of Knowledge Management? • What is a backpropogating neural net, entropy, decision tree? • How to transfer data mining techniques developed for the corporate world to higher education? • Which data mining model is the best for predicting a student's chance of persistence? Retention? Success? • Who are the ones that are transferring or dropping out? • Read on… EXECUTIVE SUMMARY This paper introduces a brand new and powerful decision support tool, data mining, in the context of knowledge management. Among other things, the most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a variety of outcomes of them, such as transferability, persistence, retention and success in classes. Compared to traditional analytical studies that are often hindsight and aggregate, data mining is forward looking and is oriented to individual students. A real life project presents the work of data mining in predicting the possibility of returning to school for every student currently enrolled at a community college in Silicon Valley. The project applies neural network, C&RT and C5.0 to choose the best prediction followed by a clustering analysis using TwoStep. The list of students who are predicted as less likely to return to school by data mining is then turned over to faculty and management for direct or indirect intervention. The benefits of data mining are its ability to gain deeper understanding of the patterns previously unseen using current available reporting capabilities. Further, prediction from data mining allows the college an opportunity to act before a student drops out or to plan for resource allocation with confidence gained from knowing how many students will transfer or take a particular course.