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What and Who
Title:Modeling and Individualizing Learning in Computer-Based Environments
Speaker:Tanja Käser
coming from:Stanford University
Speakers Bio:

Tanja Käser is a senior data scientist at the Swiss Data Science Center (SDSC). Before joining the SDSC, she was as postdoctoral researcher at Stanford University. Tanja also worked as a postdoctoral researcher at ETH Zurich and as a consultant for Disney Research Zurich and Dybuster AG. She received her PhD in Computer Science from ETH Zurich; her thesis was distinguished with the Fritz Kutter Award of ETH Zurich. Tanja works in the field of artificial intelligence in education and is especially interested in modeling and predicting student thinking and learning to provide optimal computer-based learning environments.

Event Type:SWS Colloquium
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Wednesday, 21 August 2019
Time:10:30
Duration:60 Minutes
Location:Saarbrücken
Building:E1 5
Room:029
Abstract

Learning technologies are becoming increasingly important in today's education. This includes game-based learning and simulations, which produce high volume output, and MOOCs (massive open online courses), which reach a broad and diverse audience at scale. The users of such systems often are of very different backgrounds, for example in terms of age, prior knowledge, and learning speed. Adaptation to the specific needs of the individual user is therefore essential. In this talk, I will present two of my contributions on modeling and predicting student learning in computer-based environments with the goal to enable individualization. The first contribution introduces a new model and algorithm for representing and predicting student knowledge. The new approach is efficient and has been demonstrated to outperform previous work regarding prediction accuracy. The second contribution introduces models, which are able to not only take into account the accuracy of the user, but also the inquiry strategies of the user, improving prediction of future learning. Furthermore, students can be clustered into groups with different strategies and targeted interventions can be designed based on these strategies. Finally, I will also describe lines of future research.

Contact
Name(s):Gretchen Gravelle
Phone:068193039102
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:YesTo Location:Kaiserslautern
To Building:G26To Room:112
Meeting ID:SWS Space 2 (6312)
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Created:
Gretchen Gravelle/MPI-SWS, 07/22/2019 09:33 AM
Last modified:
Uwe Brahm/MPII/DE, 08/21/2019 07:01 AM
  • Gretchen Gravelle, 07/22/2019 09:37 AM -- Created document.