For effective cooperative learning grouping student is important. Grouping students can be generalized to the problem that clustering objects into some clusters from a computer science point of view. The large datasets, expensive task of clustering computationally and high dimensionality makes clustering of very large scale of data a challenging task. To effectively process very large datasets for clustering, parallel and distributed architectures have developed. MapReduce is a programming model that is used for handling large volumes of data over a distributed computing environment in parallel. In this paper, we present a Parallel Balanced Team Formation (PBTF) clustering algorithm for the MapReduce framework. The purpose of PBTF is to find partitions with high homogeneity in a group and high heterogeneity between groups in parallel.