TY - CHAP A1 - Schütt, Anan A1 - Huber, Tobias A1 - Aslan, Ilhan A1 - André, Elisabeth ED - Feng, Mingyu ED - Käser, Tanja ED - Talukdar, Partha T1 - Fast Dynamic Difficulty Adjustment for Intelligent Tutoring Systems with Small Datasets T2 - Proceedings of the 16th International Conference on Educational Data Mining N2 - This paper studies the problem of automatically adjusting the difficulty level of educational exercises to facilitate learning. Previous work on this topic either relies on large datasets or requires multiple interactions before it adjusts properly. Although this is sufficient for large-scale online courses, there are also scenarios where students are expected to only work through a few trials. In these cases, the adjustment needs to respond to only a few data points. To accommodate this, we propose a novel difficulty adjustment method that requires less data and adapts faster. Our proposed method refits an existing item response theory model to work on smaller datasets by generalizing based on attributes of the exercises. To adapt faster, we additionally introduce a discount value that weakens the influence of past interactions. We evaluate our proposed method on simulations and a user study using an example graph theory lecture. Our results show that our approach indeed succeeds in adjusting to learners quickly. UR - https://doi.org/10.5281/zenodo.8115740 Y1 - 2023 UR - https://doi.org/10.5281/zenodo.8115740 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57594 SN - 978-1-7336736-4-8 SP - 482 EP - 489 PB - IEDMS CY - Worcester ER - TY - CHAP A1 - Flutura, Simon A1 - Seiderer, Andreas A1 - Huber, Tobias A1 - Weitz, Katharina A1 - Aslan, Ilhan A1 - Schlagowski, Ruben A1 - André, Elisabeth A1 - Rathmann, Joachim T1 - Interactive Machine Learning and Explainability in Mobile Classification of Forest-Aesthetics T2 - Proceedings of the 6th EAI International Conference on Smart Objects and Technologies for Social Good UR - https://doi.org/10.1145/3411170.3411225 Y1 - 2020 UR - https://doi.org/10.1145/3411170.3411225 SN - 978-1-4503-7559-7 SP - 90 EP - 95 PB - ACM CY - New York ER -