Performance indicators for online secondary education: a case study

There is little consensus about what variables extracted from learner data are the most reliable indicators of learning performance. The aim of this study is to determine such indicators by taking a wide range of variables into consideration concerning overall learning activity and content processing. A genetic algorithm is used for the selection process and variables are evaluated based on their predictive power in a classification task. Variables extracted from exercise activities turn out to be most informative. Exercises designed to train students in understanding and applying material are found to be especially informative.