April 2019 - Talks - lists.usi.ch

[Reminder] Modeling and Individualizing Learning in Computer-Based Environments (Apr 30, 9.30am)

by Announcements of talks＠IDSIA

Today, 9.30am, Idsia meeting room. > On 26 Apr 2019, at 10:50, Alessandro Antonucci <alessandro(a)idsia.ch> wrote: > > Speaker: Tanja Christina Kaeser Jacober (Stanford University) > > Title: Modeling and Individualizing Learning in Computer-Based Environments > > 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. > > When: Tuesday 30th April, 9.30am. > > Where: IDSIA Meeting Room, Galleria 1, Manno

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Modeling and Individualizing Learning in Computer-Based Environments (Apr 30, 9.30am)

by Announcements of talks＠IDSIA

Speaker: Tanja Christina Kaeser Jacober (Stanford University) Title: Modeling and Individualizing Learning in Computer-Based Environments 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. When: Tuesday 30th April, 9.30am. Where: IDSIA Meeting Room, Galleria 1, Manno

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Predicting Topics in Scholarly Papers

by Announcements of talks＠IDSIA

Title: Predicting Topics in Scholarly Papers Time: 09:00 Location: Manno, Galleria 1, 2nd floor, room G-222 Speaker: Seyed Ali Bahrainian, PhD candidate, USI Abstract: In the last few decades, topic models have been extensively used to discover the latent topical structure of large text corpora; however, very little has been done to model the continuation of such topics in the near future. In this paper we present a novel approach for tracking topical changes over time and predicting the topics which would continue in the near future. For our experiments, we used a publicly available corpus of conference papers, since scholarly papers lead the technological advancements and represent an important source of information that can be used to make decisions regarding the funding strategies in the scientific community. The experimental results show that our model outperforms two major baselines for dynamic topic modeling in terms of predictive power.

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PIDSIA Seminar by Jobst Landgrebe and Barry Smith, April 11th

by Announcements of talks＠IDSIA

*Speakers: Jobst Landgrebe and Barry Smith *Title: Ontology-Driven Automotive Repair Claims Management *Abstract: We are experiencing once again a period of enthusiasm in the AI research field, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this talk we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, and illustrate this approach in relation to a specific example in the field of claims management. Summarized here: https://www.cognotekt.com/en/news/rechnungen-mit-ki-pruefen/ *Bios: - Jobst Landgrebe is owner and managing director of Cognotekt GmbH, a German company specializing in AI-based language processing applications - Barry Smith is Director of the (US) National Center for Ontological Research in Buffalo, NY and author of Building Ontologies with Basic Formal Ontology (MIT Press, 2015) *Date and Time: Thursday, 11th of April 2019, 12:00-13:00 *Location: Manno, Galleria 1, 2nd floor, room G1-201 *Doodle registration: Pizza and drinks will be offered at the end of the talk. If you plan to attend, please register in a timely fashion at the following link so that we will have no shortage of food: https://doodle.com/poll/8ukw2zqzsy83u995

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Talks April 2019