IC Colloquium: Modeling and Individualizing Learning in Computer-Based Environments
Event details
| Date | 19.09.2019 |
| Hour | 10:15 › 11:00 |
| Location | |
| Category | Conferences - Seminars |
By: Tanja Käser - Swiss Data Science Center
Digital Vocational Education & Training candidate
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. With the increased digitization, continued learning is becoming important for every profession and these types of learning environments have the potential to yield insights into how we can prepare students for our digital society. Given the very different backgrounds of the users of these educational systems, for example in terms of age, prior knowledge, and learning speed, adaptation to the specific needs of the individual user is a key factor for motivation and learning success.
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 around strategies in open-ended learning environments and discuss its benefits for vocational education training.
Bio:
Tanja Käser is currently a senior data scientist at the Swiss Data Science Center (SDSC). Before joining the SDSC, she was as postdoctoral researcher at the Graduate School of Education 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 for the best Computer Science thesis at a Swiss university. Tanja works in the field of artificial intelligence in education and is especially interested in modeling and predicting student thinking and learning to optimize and improve (computer-based) learning.
Digital Vocational Education & Training candidate
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. With the increased digitization, continued learning is becoming important for every profession and these types of learning environments have the potential to yield insights into how we can prepare students for our digital society. Given the very different backgrounds of the users of these educational systems, for example in terms of age, prior knowledge, and learning speed, adaptation to the specific needs of the individual user is a key factor for motivation and learning success.
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 around strategies in open-ended learning environments and discuss its benefits for vocational education training.
Bio:
Tanja Käser is currently a senior data scientist at the Swiss Data Science Center (SDSC). Before joining the SDSC, she was as postdoctoral researcher at the Graduate School of Education 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 for the best Computer Science thesis at a Swiss university. Tanja works in the field of artificial intelligence in education and is especially interested in modeling and predicting student thinking and learning to optimize and improve (computer-based) learning.
Practical information
- General public
- Free
- This event is internal
Contact
- Host: Pierre Dillenbourg