| Predicting MOOC dropout over weeks using machine learning methods M Kloft, F Stiehler, Z Zheng, N Pinkwart Proceedings of the EMNLP 2014 workshop on analysis of large scale social …, 2014 | 394 | 2014 |
| An Ensemble Method to Predict Student Performance in an Online Math Learning Environment M Stapel, Z Zheng, N Pinkwart Educational Data Mining, 2016 | 56 | 2016 |
| The Impact of Small Learning Group Composition on Student Engagement and Success in a MOOC Z Zheng, T Vogelsang, N Pinkwart the 8th International Conference on Educational Data Mining (EDM 2015), 500-503, 2015 | 49 | 2015 |
| A discrete particle swarm optimization approach to compose heterogeneous learning groups Z Zheng, N Pinkwart 2014 IEEE 14th international conference on advanced learning technologies, 49-51, 2014 | 43 | 2014 |
| A Dynamic Group Composition Method to Refine Collaborative Learning Group Formation Z Zheng Educational Data Mining, 2013 | 4 | 2013 |
| Learning Group Composition and Re-composition in Large-scale Online Learning Contexts Z Zheng Humboldt-Universität zu Berlin, 2017 | 3 | 2017 |
| Dynamic Re-Composition of Learning Groups Using PSO-Based Algorithms Z Zheng, N Pinkwart Educational Data Mining, 2014 | 3 | 2014 |
| Perfect Scores Indicate Good Students!? The Case of One Hundred Percenters in a Math Learning System Z Zheng, M Stapel, N Pinkwart Educational Data Mining, 2016 | 2 | 2016 |
| Recomposing Small Learning Groups at Scale—A Data-driven Approach and a Simulation Experiment Z Zheng, N Pinkwart Gesellschaft für Informatik, Bonn, 2017 | | 2017 |
Niels PinkwartHumboldt-Universität zu Berlin & DFKIinformatik.hu-berlin.de의 이메일 확인됨
