Predicting overall student performance and monitoring progress have attracted more attention in the past five years than before. Demographic data, high school grades and test result constitute much of the data used for building prediction models. This study demonstrates how data from a peer-assessment environment can be used to build student progress prediction models. The possibility for automating tasks, coupled with minimal teacher intervention, make peer-assessment an efficient platform for gathering student activity data in a continuous manner. The performances of the prediction models are comparable with those trained using other educational data. Considering the fact that the student performance data do not include any teacher assessments, the results are more than encouraging and shall convince the reader that peer-assessment has yet another advantage to offer in the realm of automated student progress monitoring and supervision. [For the full proceedings, see ED592609.]