Leo Sylvio Rüdian received his Master‘s Degree in computer science from Humboldt University in October 2017. At his master thesis he explored the deanonymization of people by using fingerprinting approaches in social media.
Since November 2017 he is a research fellow at the Weizenbaum Institut and joined the research group of Prof. Pinkwart. During his studies, he focuses on machine learning in education, learning analytics and educational data mining.
His current investigations follow the aim to generate online courses by using textual inputs to overcome the limitation of the dependency to available online courses for educational recommender systems. He also investigates into adaption approaches (at micro and macro level) to personalize online courses according to the users' needs. Therefore, he examines culture and personality traits in learning systems for user modeling.
Research Group "Education and Advanced Training in the Digital Society"
Z. Liu, C. Yang, S. Rüdian, S. Liu, L. Zhao, and T. Wang: Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums, Interactive Learning Environments, 27:5-6, 598-627, 2019.
S. Rüdian and N. Pinkwart: Towards an Automatic Q&A Generation for Online Courses - A Pipeline Based Approach, Artificial Intelligence in Education (AIED), Springer, 2019.
S. Rüdian, G. Vladova, J. Gundlach, G. Kazimzade, and N. Pinkwart: Predicting Culture and Personality in Online Courses, SLLL@AIED 2019, 2019, personalization online courses e-learning big five personality culture machine learning.
S. Rüdian, N. Pinkwart, and Z. Liu: I know who you are: Deanonymization using Facebook Likes Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, Köllen Druck+Verlag GmbH, 2018.
Z. Liu, S. Rüdian, C. Yang, J. Sun, and S. Liu: Tracking the dynamics of SPOC discussion forums: a temporal emotion-topic modeling approach 7th International Conference of Educational Innovation, IEEE, 2018.
S. Rüdian, Z. Liu, and N. Pinkwart: Comparison and Prospect of Two Heaven Approaches: SVM and ANN for Identifying Students' Learning Performance, 7th International Conference of Educational Innovation, IEEE, 2018.