Konstantin Bauman, Ph.D., Ass. Prof. Fox School of Business@Temple University: “Intelligence Augmentation for Higher-Quality Peer Feedback: Supporting Students by Recommending Features of Written Feedback that Should be Improved”
The value of peer feedback is growing for large-scale courses in universities, as well as for MOOCs, where it is used as a method to provide students with formative feedback and support them in their learning journey. However, students usually have little to no experience in providing feedback and especially in formulating ideas in a way that would help feedback recipients understand all the raised critical issues and improve their submission accordingly. In his research talk, Konstantin Bauman will present his work in collaboration with Roman Rietsche, Matthias Söllner and Jan Marco Leimeister from the Institute of Information Management (HSG) and the University of Kassel. In their work they developed an intelligence augmentation approach which was implemented and proposed to support students in the process of creating peer feedback with personalized recommendations of the most critical text features that need improvement. They designed a novel framework that provides such recommendations aiming to maximize the feedback quality as perceived by the feedback recipient. Furthermore, they also designed a novel Feature Utility Saturation Model (FUSM) that works as part of the framework. To test how the proposed method works in practice, they conduct a field experiment with 490 students taking three different courses at a public university in Europe. The results show that the personalized recommendation approach works well as it outperforms baselines and helps students create longer feedback and of significantly higher quality, as evaluated by feedback recipients.
Dr. Konstantin Bauman joined the Fox School on a tenure track appointment within the Department of Management Information Systems on January 1, 2018. He arrives at Fox from the Stern School of Business at New York University, where he served as a postdoctoral research fellow. Bauman’s research interests lie in the areas of technical information systems, with focus on the fields of quantitative modeling and data science. In particular, he works on developing novel machine learning methods for predicting customer preferences, and designing novel approaches to recommender systems that provide personalized advice to customers. Before joining NYU, Bauman worked as the head of a machine-learning group within the research department of Yandex, where he dealt with large-scale machine learning and data science problems on a daily basis. He also served as a software engineer at Yandex and the Russian Academy of Foreign Trade.
Bauman received his PhD in Mathematics (Geometry and Topology) from Russia’s Moscow State University, where he also earned a Master of Science degree in Mathematics. He also obtained a Master of Science degree in Machine Learning from a joint program between the Moscow Institute of Physics and Technology and the Yandex School of Data Analysis in Russia.
