@article{BrunnerKellerWengeretal.2017,
  author    = {Brunner, Martin and Keller, Ulrich and Wenger, Marina and Fischbach, Antoine and L{\"u}dtke, Oliver},
  title     = {Between-School Variation in Students' Achievement, Motivation, Affect, and Learning Strategies},
  series = {Journal of research on educational effectiveness / Society for Research on Educational Effectiveness (SREE)},
  volume    = {11},
  journal   = {Journal of research on educational effectiveness / Society for Research on Educational Effectiveness (SREE)},
  number    = {3},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1934-5747},
  doi       = {10.1080/19345747.2017.1375584},
  pages     = {452 -- 478},
  year      = {2017},
  abstract  = {To plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated.},
  language  = {en}
}
@misc{BrunnerKellerWengeretal.2017,
  author    = {Brunner, Martin and Keller, Ulrich and Wenger, Marina and Fischbach, Antoine and L{\"u}dtke, Oliver},
  title     = {Between-school variation in students' achievement, motivation, affect, and learning strategies},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {465},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412662},
  pages     = {28},
  year      = {2017},
  abstract  = {To plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated.},
  language  = {en}
}
@misc{ChujfiMeinel2017,
  author    = {Chujfi, Salim and Meinel, Christoph},
  title     = {Patterns to explore cognitive preferences and potential collective intelligence empathy for processing knowledge in virtual settings},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401789},
  pages     = {16},
  year      = {2017},
  abstract  = {Organizations continue building virtual working teams (Teleworkers) to become more dynamic as part of their strategic innovation, with great benefits to individuals, business and society. However, during such transformations it is important to note that effective knowledge communication is particularly difficult in distributed environments as well as in non-interactive settings, because the interlocutors cannot use gestures or mimicry and have to adapt their expressions without receiving any feedback, which may affect the creation of tacit knowledge. Collective Intelligence appears to be an encouraging alternative for creating knowledge. However, in this scenario it faces an important goal to be achieved, as the degree of ability of two or more individuals increases with the need to overcome barriers through the aggregation of separately processed information, whereby all actors follow similar conditions to participate in the collective. Geographically distributed organizations have the great challenge of managing people's knowledge, not only to keep operations running, but also to promote innovation within the organization in the creation of new knowledge. The management of knowledge from Collective Intelligence represents a big difference from traditional methods of information allocation, since managing Collective Intelligence poses new requirements. For instance, semantic analysis has to merge information, coming both from the content itself and the social/individual context, and in addition, the social dynamics that emerge online have to be taken into account. This study analyses how knowledge-based organizations working with decentralized staff may need to consider the cognitive styles and social behaviors of individuals participating in their programs to effectively manage knowledge in virtual settings. It also proposes assessment taxonomies to analyze online comportments at the levels of the individual and community, in order to successfully identify characteristics to help evaluate higher effectiveness of communication. We aim at modeling measurement patterns to identify effective ways of interaction of individuals, taking into consideration their cognitive and social behaviors.},
  language  = {en}
}