Zhilin Zheng, M.Eng.

Zhilin Zheng, M.Eng.Humboldt-Universität zu BerlinPhD studentZhilin Zheng, M.Eng. Address: Rudower Chaussee 25, Room 3.404
Phone: +49 30 2093-3180
Email: zhilin.zheng@informatik.hu-berlin.de
    Zhilin Zheng holds a Master degree of Engineering in Software Engineering from Southeast University, China. He joined the research group "Human-Centered Information Systems" of Prof. Pinkwart at Clausthal University of Technology as a PhD student since September 2012. His research focuses on developing algorithms to compose and re-compose learning groups in computer supported collaborative learning context. In October 2013, he followed Prof. Pinkwart to work for the research group "Computer Science Education / Computer Science and Society" (CSES) at Humboldt-Universität zu Berlin.

    Publications

    2016
    [7] M. Stapel, Z. Zheng, N. Pinkwart (2016). An Ensemble Method to Predict Student Performance in an Online Math Learning Environment. In Tiffany Barnes, Min Chi, Mingyu Feng, eds., Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 231--238). Raleigh NC, US.
    [BIB] [URL]
    [6] Z. Zheng, M. Stapel, N. Pinkwart (2016). Perfect Scores Indicate Good Students !? The Case of One Hundred Percenters in a Math Learning System. In Tiffany Barnes, Min Chi, Mingyu Feng, eds., Proceedings of the 9th International Conference on Educational Data Mining (EDM 2016) (pp. 660--661). Raleigh NC, US.
    [BIB] [URL]
    2015
    [5] Z. Zheng, T. Vogelsang, N. Pinkwart (2015). The Impact of Small Learning Group Composition on Student Engagement and Success in a MOOC. In J. G. Boticario, O. C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J. M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais, eds., Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015) (pp. 500--503). Madrid, Spain.
    [BIB] [URL]
    2014
    [4] M. Kloft, F. Stiehler, Z. Zheng, N. Pinkwart (2014). Predicting MOOC Dropout over Weeks Using Machine Learning Methods. In Proceedings of the 2014 Empirical Methods in Natural Language Processing Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses (pp. 60--65). Qatar, Association for Computational Linguistics.
    [BIB] [PDF]
    [3] Z. Zheng, N. Pinkwart (2014). Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms. In J. Stamper, Z. Pardos, M. Mavrikis, B. M. McLaren, eds., Proceedings of the 7th International Conference on Educational Data Mining (EDM) (pp. 357--358). London, United Kingdom.
    [BIB] [PDF]
    [2] Z. Zheng, N. Pinkwart (2014). A Discrete Particle Swarm Optimization Approach to Compose Heterogeneous Learning Groups. In N. S. Chen, R. Huang, Kinshuk, D. G. Sampson, J. M. Spector, eds., Proceedings of the 14th IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 49--51). Los Alamitos, CA, IEEE Computer Society Press.
    [BIB] [DOI]
    2013
    [1] Z. Zheng (2013). A Dynamic Group Composition Method to Refine Collaborative Learning Group Formation. In S. K. D'Mello, R. A. Calvo, A. Olney, eds., Proceedings of the 6th International Conference on Educational Data Mining (EDM) (pp. 360--361). Memphis, TN.
    [BIB] [PDF]