Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of the members of the given cluster than a predictor trained on the entire data-set. Previous work has used this basic premise to construct a simple yet strong bagging strategy. However, such models have one significant drawback: Instances (such as students) are clustered while features (tutor usage features/items) are left alone. One-way clustering by using some objective function measures the degree of homogeneity between data instances. Often it is noticed that features also influence final prediction in homogeneous groups. This indicates a duality in the relationship between clusters of instances and clusters of features. Co-Clustering simultaneously measures the degree of homogeneity in both data instances and features, thus also achieving clustering and dimensionality reduction simultaneously. Students and features could be modelled as a bipartite graph and a simultaneous clustering could be posed as a bipartite graph partitioning problem. In this paper we integrate an effective bagging strategy with Co-Clustering and present results for prediction of out-of-tutor performance of students. We report that such a strategy is very useful and intuitive, even improving upon performance achieved by previous work. (Contains 4 figures and 2 tables.) [Additional funding for this research was provided by the United States Army. For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]