| What is a true gamer? The male gamer stereotype and the marginalization of women in video game culture B Paaßen, T Morgenroth, M Stratemeyer Sex Roles 76, 421-435, 2017 | 438 | 2017 |
| The continuous hint factory-providing hints in vast and sparsely populated edit distance spaces B Paaßen, B Hammer, TW Price, T Barnes, S Gross, N Pinkwart arXiv preprint arXiv:1708.06564, 2017 | 58 | 2017 |
| Transfer learning for rapid re-calibration of a myoelectric prosthesis after electrode shift C Prahm, B Paassen, A Schulz, B Hammer, O Aszmann Converging Clinical and Engineering Research on Neurorehabilitation II …, 2016 | 46 | 2016 |
| Counteracting electrode shifts in upper-limb prosthesis control via transfer learning C Prahm, A Schulz, B Paaßen, J Schoisswohl, E Kaniusas, G Dorffner, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (5 …, 2019 | 45 | 2019 |
| A comparison of the quality of data-driven programming hint generation algorithms TW Price, Y Dong, R Zhi, B Paaßen, N Lytle, V Cateté, T Barnes International Journal of Artificial Intelligence in Education 29, 368-395, 2019 | 40 | 2019 |
| Example-based feedback provision using structured solution spaces S Gross, B Mokbel, B Paaßen, B Hammer, N Pinkwart International Journal of Learning Technology 10 9 (3), 248-280, 2014 | 39 | 2014 |
| Metric learning for sequences in relational LVQ B Mokbel, B Paassen, FM Schleif, B Hammer Neurocomputing 169, 306-322, 2015 | 36 | 2015 |
| Domain-independent proximity measures in intelligent tutoring systems B Mokbel, S Gross, B Paassen, N Pinkwart, B Hammer Educational Data Mining 2013, 2013 | 33 | 2013 |
| Expectation maximization transfer learning and its application for bionic hand prostheses B Paaßen, A Schulz, J Hahne, B Hammer Neurocomputing 298, 122-133, 2018 | 32 | 2018 |
| Adaptive structure metrics for automated feedback provision in intelligent tutoring systems B Paaßen, B Mokbel, B Hammer Neurocomputing 192, 3-13, 2016 | 28 | 2016 |
| Tree edit distance learning via adaptive symbol embeddings B Paaßen, C Gallicchio, A Micheli, B Hammer International Conference on Machine Learning, 3976-3985, 2018 | 24 | 2018 |
| Learning interpretable kernelized prototype-based models D Hofmann, FM Schleif, B Paaßen, B Hammer Neurocomputing 141, 84-96, 2014 | 24 | 2014 |
| Revisiting the tree edit distance and its backtracing: A tutorial B Paaßen arXiv preprint arXiv:1805.06869, 2018 | 20 | 2018 |
| Time series prediction for graphs in kernel and dissimilarity spaces B Paaßen, C Göpfert, B Hammer Neural Processing Letters 48 (2), 669-689, 2018 | 18 | 2018 |
| Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. B Paaßen, J Jensen, B Hammer International Educational Data Mining Society, 2016 | 17 | 2016 |
| The gendered nature and malleability of gamer stereotypes T Morgenroth, M Stratemeyer, B Paaßen Cyberpsychology, Behavior, and Social Networking 23 (8), 557-561, 2020 | 14 | 2020 |
| Dynamic fairness-Breaking vicious cycles in automatic decision making B Paaßen, A Bunge, C Hainke, L Sindelar, M Vogelsang arXiv preprint arXiv:1902.00375, 2019 | 14 | 2019 |
| Linear supervised transfer learning for generalized matrix lvq B Paaßen, A Schulz, B Hammer Proceedings of the Workshop New Challenges in Neural Computation 2016, 2016 | 14 | 2016 |
| Mapping python programs to vectors using recursive neural encodings B Paassen, J McBroom, B Jeffries, I Koprinska, K Yacef Journal of Educational Data Mining 13 (3), 1-35, 2021 | 13 | 2021 |
| Progress networks as a tool for analysing student programming difficulties J McBroom, B Paassen, B Jeffries, I Koprinska, K Yacef Proceedings of the 23rd Australasian Computing Education Conference, 158-167, 2021 | 13 | 2021 |
Barbara HammerProfessor, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
