It is a pleasure to invite you to the following two talks: Prof. Peter Brusilovsky, Computer Science Department: „Open Social Student Modeling“ and Prof. Denis Parra Santander, Computer Science Department, School of Engineering: „The Millenium Institute for Foundational Research on Data and the Agenda for Studying Explainable Machine Learning“
Abstract: In this talk, Prof. Parra will introduce the recently created Millennium Institute for Foundational Research on Data, a multidisciplinary project with researchers from four universities in Chile, spanning several areas: Computer Science, Mathematics, Statistics, Political Science and Journalism. This project is funded for 10 years by the Chilean government, and it aims at studying several aspects of data: databases, machine learning, information retrieval, and its impact on societal issues such as politics and news. Among the different work packages of this project, one is dedicated to studying explainable machine learning and Prof. Parra will focus on this aspect during the talk.
Machine learning has shown important advances in the last five years, specially thanks to deep learning models which empower applications in computer vision, automatic machine translation, recommendation algorithms and many others. However, one aspect which hinders the wide adoption of these models is their lack of interpretability: good predictions with little algorithmic transparency. In this talk, Prof. Parra will provide examples where visualization and interactivity has helped to unlock machine learning applications in the past and how they can help in the challenge of opening the black boxes of current state of the art machine learning models.
For those who can stay, there will be a talk at 17:00 by Prof. Peter Brusilovsky from University of Pittsburgh (Title and abstract TBA).
Abstract: In this talk I will introduce the emerging technology of Open Social Student Modeling (OSSM) and review several projects performed in our research lab to investigate the potential of OSSM. OSSM is a recent extension of Open Student Modeling (OSM), a popular technology in the area of personalized learning systems. While in traditional personalized systems, student models were hidden “under the hood” and used to personalize the educational process; open student modeling introduced the ability to view and modify the state of students’ own knowledge to support reflection, selforganized learning, and system transparency. Open Social Student Modeling takes this idea one step further by allowing students to explore each other’s models or an aggregated model of the class. The idea to make OSM social was originally suggested and explored by Bull [1; 2]. Over the last few years, our team explored several approaches to present OSSM in a highly visual form and evaluated these approaches in a sequence of classroom and lab studies. I will present a summary of this work introducing such systems as QuizMap, Progressor, and Mastery Grids and reviewing most interesting research evidence collected by the studies.
Bio: Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh, where he directs Personalized Adaptive Web Systems (PAWS) lab. Peter Brusilovsky has been working in the field of adaptive educational systems, user modeling, and intelligent user interfaces for more than 30 years. He published numerous papers and edited several books on adaptive hypermedia, adaptive educational systems, user modeling, and the adaptive Web. Peter is the Editor-in-Chief of IEEE Transactions on Learning Technologies and a board member of several journals including User Modeling and User Adapted Interaction and ACM Transactions on Social Computing.
Speaker: Prof. Peter Brusilovsky, Computer Science Department
University of Pittsburgh
When: Wed 20.06.2018 @ 17:00
Title: „Open Social Student Modeling“
Speaker: Prof. Denis Parra Santander, Computer Science Department, School of Engineering
Pontificia Universidad Catolica de Chile
When: Wed 20.06.2018 @ 16:00, f
Where: HS i6, Inffeldgasse 25
Title: „The Millenium Institute for Foundational Research on Data and the Agenda for Studying Explainable Machine Learning“