Z. Zheng. Learning Group Composition and Re-composition in Large-scale Online Learning Contexts. PhD thesis, HU Berlin, 2017.

Z. Zheng and N. Pinkwart. Recomposing Small Learning Groups at Scale - A Data-driven Approach and a Simulation Experiment. Bildungsräume 2017, Gesellschaft für Informatik, Bonn, pages 321-332, 2017.

M. Stapel, Z. Zheng, and N. Pinkwart. An Ensemble Method to Predict Student Performance in an Online Math Learning Environment. Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), 2016.

Z. Zheng, M. Stapel, and N. Pinkwart. Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System. Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016), 2016.

Z. Zheng, T. Vogelsang, and N. Pinkwart. The Impact of Small Learning Group Composition on Student Engagement and Success in a MOOC. Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), 2015.

Z. Zheng and N. Pinkwart. Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms. Proceedings of the 7th International Conference on Educational Data Mining (EDM), 2014.

M. Kloft, F. Stiehler, Z. Zheng, and N. Pinkwart. Predicting MOOC Dropout over Weeks Using Machine Learning Methods. Proceedings of the 2014 Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses, Association for Computational Linguistics, 2014.

Z. Zheng and N. Pinkwart. A Discrete Particle Swarm Optimization Approach to Compose Heterogeneous Learning Groups. Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE Computer Society Press, 2014.

Z. Zheng. A Dynamic Group Composition Method to Refine Collaborative Learning Group Formation. Proceedings of the 6th International Conference on Educational Data Mining (EDM), 2013.