Machine Learning Methodology for the Analysis of Engineering Student Retention Data

Numerous studies have investigated the correlation of predictive indicators for a relationship to identify students-at-risk of failure. As data sets begin to include a large number of potential indicators, methods of visual inspection or empirical comparison can be flawed or inefficient. The authors have begun applying statistical data reduction methods for the selection of ideal predictive indicators of studentsat-risk. Once the predictors are selected, a random subset are used for the supervised training of the machine learning methodology. The Support-VectorMachine is a machine learning algorithm that offers advantages of small training sets and tolerance of noisy data. The focus of this paper is not the actual predictors, but instead the theory and methodology of using data reduction and machine learning for selection of the best predictors of the interested researchers own data set. Example results from the authors initial analyses will be presented. This paper will be of interest to researchers, instructors, and administrators seeking an algorithmic approach to analyzing the large volume of data available for each engineering cohort.