Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Fast Radiosity Repropagation For Interactive Virtual Environments Using A Shadow-Form-Factor-List

The radiosity method became a very important tool in order to enable photorealistic rendering in virtual reality systems. Based on the geometric description of a scene, the view-independent illumination is computed in a preprocess and colors are assigned to each patch vertex. These virtual environments look very impressive, but any interaction with the scene geometry or its materials results in a time expensive recalculation of the radiosity simulation. This leads to the common phrase: Radiosity scenes are like museums, you may look around, but do not touch anything! In this paper, a new algorithm is presented to overcome this problem. The algorithm is based on the fact that most of the information needed for the radiosity repropagation after any scene modification was already computed during the radiosity preprocess. Therefore, the radiosity method is extended by storing shadow- and form-factor-information in an efficient data structure, the so-called shadow-form-factor-list (SFFL)...