@article{KniggeKrauskopfWagner2019,
  author    = {Knigge, Michel and Krauskopf, Karsten and Wagner, Simon},
  title     = {Improving Socio-Emotional Competencies Using a Staged Video-Based Learning Program?},
  series = {Frontiers in Education},
  volume    = {4},
  journal   = {Frontiers in Education},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2504-284X},
  doi       = {10.3389/feduc.2019.00142},
  pages     = {12},
  year      = {2019},
  abstract  = {Relationship quality between teachers and their students is a critical aspect for well-being and effective learning in school. Accordingly, teacher training should promote competencies for creating and maintaining positive relationships in the classroom. The Helga Breuninger Foundation developed a video-based online training (Intus³) that intends to focus on student teachers' interpersonal competencies by reflecting on staged videos. Although this training is well-designed, there is only little empirical evidence in general and so far no experimental research investigating the effects of Intus³. Accordingly, we investigated whether this program is able to improve the capacities of student teachers' interpersonal competencies, affective well-being, and affective attitudes toward challenging students. We conducted two randomized experimental studies (n1 = 132, n2 = 242) within lectures in teacher education at the University of Potsdam, introducing the basics of inclusive education in two consecutive semesters. We compared groups first working with Intus³ to waiting control groups that wrote an expository text based on empirical research discussing the relevance of teacher-student relationships with a longitudinal design with four measurement points. Latent change models showed that prior work with Intus³ showed few effects but complex effects in comparison to the prior text work groups. In the larger and extended study 2, an increase of empathic concern was significant after the prior work with Intus³. The results will be discussed with the perspective of the potential of further development of online training courses for affective learning for teachers and teacher students.},
  language  = {en}
}
@article{vonSteinkellerGrosse2022,
  author    = {von Steinkeller, Annika and Grosse, Gerlind},
  title     = {Children are more social when playing analog games together than digital games},
  series = {Computers in Human Behavior Reports},
  volume    = {6},
  journal   = {Computers in Human Behavior Reports},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2451-9588},
  doi       = {10.1016/j.chbr.2022.100195},
  pages     = {10},
  year      = {2022},
  abstract  = {Digital media are being used more and more frequently by children and for a wide variety of functions. However, there are no studies to date that examine the effect of such use on peer interactions and the occurrence of prosocial behavior in peers. For parents, it has been found that when using digital media only few parents respond responsively to their children's attempts at interaction and also very rarely, they communicate with them verbally and nonverbally. In the present study, we investigated how playing a game in a digital versus analog form influences in-teractions (especially prosocial behavior) of peers. We used an experimental situation, where 24 dyads of 4-10-year-old children were examined. Each of the dyads was randomly assigned to a condition where they played either a digital or analog game together. Various interaction parameters and prosocial behavior during and after the game were analyzed. Results show that children in the analog condition communicated verbally with each other more often, responded more often to interaction attempts of their partners and showed less often negative forms of inter-action and more often positive forms of interaction than children in the digital condition. However, the type of medium had no influence on prosocial behavior after the game situation. These results suggest that the format of a game (digital vs. analog) has a decisive influence on peer interactions concerning their communication during but not their prosocial behavior after the game situation.},
  language  = {en}
}
@article{HampfNendelStreyetal.2021,
  author    = {Hampf, Anna and Nendel, Claas and Strey, Simone and Strey, Robert},
  title     = {Biotic yield losses in the Southern Amazon, Brazil},
  series = {Frontiers in plant science : FPLS},
  volume    = {12},
  journal   = {Frontiers in plant science : FPLS},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2021.621168},
  pages     = {16},
  year      = {2021},
  abstract  = {Pathogens and animal pests (P\&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P\&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P\&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P\&A, (2) map the spatial distribution of P\&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P\&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P\&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P\&A, whereas soybean is mainly affected by P\&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16\%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US\$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.},
  language  = {en}
}