TY - JOUR A1 - Traini, Claudia A1 - Kleinert, Corinna A1 - Bittmann, Felix T1 - How does exposure to a different school track influence learning progress? BT - explaining scissor effects by track in Germany JF - Research in social stratification and mobility N2 - German secondary education is known for its early, strict selection of students into different schooling tracks based on prior academic performance, based on the assumption that students learn more efficiently when the learning environment is tailored to their individual abilities and needs. While much previous research has shown that entry into tracks is socially selective, less is known whether there are effects of being exposed to a particular school track on educational success and which mechanisms are contributing to these effects. We investigate this question by comparing the learning progress in reading and mathematics of students in the upper and intermediate schooling track over five years of secondary schooling, based on large-scale German-wide longitudinal data (NEPS-SC3). Even when restricting our sample to a group of students with similar preconditions and controlling for skills at the beginning of secondary schooling, we find that the learning progress in the upper track is higher for both domains, suggesting scissor effects of track exposure. It is mainly the average performance level of the class, and to a lesser degree its social background composition, which mediates these effects. In contrast, migration background composition of the class and instructional quality perceived by students hardly contribute to explaining increasing learning gains in the upper track. KW - Tracking KW - Learning progress KW - German secondary education KW - Learning KW - environment KW - Social stratification Y1 - 2021 U6 - https://doi.org/10.1016/j.rssm.2021.100625 SN - 0276-5624 VL - 76 SP - 285 EP - 298 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten H. A1 - Merico, Davide A1 - Bisiani, Roberto T1 - Knowledge-based multi-criteria optimization to support indoor positioning JF - Annals of mathematics and artificial intelligence N2 - Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting. KW - Knowledge representation KW - Answer Set Programming KW - Wireless Sensor Networks KW - Localization KW - Tracking Y1 - 2011 U6 - https://doi.org/10.1007/s10472-011-9241-2 SN - 1012-2443 SN - 1573-7470 VL - 62 IS - 3-4 SP - 345 EP - 370 PB - Springer CY - Dordrecht ER -