Cultural capital in context: Heterogeneous returns to cultural capital across schooling environments

AbstractThis paper tests two competing explanations of differences in returns to cultural capital across schooling environments: Cultural reproduction (cultural capital yields a higher returns in high-achieving environments than in low-achieving ones) and cultural mobility (cultural capital yields higher returns in low-achieving environments). Using multilevel mixture models, empirical results from analyses based on PISA data from three countries (Canada, Germany, and Sweden) show that returns to cultural capital tend to be higher in low-achieving schooling environments than in high-achieving ones. These results principally support the cultural mobility explanation and suggest that research should pay explicit attention to the institutional contexts in which cultural capital is converted into educational success

Exploring School Culture: Technical report for data collection

This report describes the process of selecting and recruiting schools, classes and teachers to take part in the Exploring School Culture (ESCU) survey. The ESCU survey was part of the “Exploring School Culture” research project, funded by the Velux foundation. The survey was conducted among Danish 6th and 9th grade students and their respective teachers in the subjects mathematics and Danish during spring 2019

Both sides of the story:comparing student-level data on reading performance from administrative registers to application generated data from a reading app

Abstract The use of various learning apps in school settings is growing and thus producing an increasing amount of usage generated data. However, this usage generated data has only to a very little extend been used for monitoring and promoting learning progress. We test if application usage generated data from a reading app holds potential for measuring reading ability, reading speed progress and for pointing out features in a school setting that promotes learning. We analyze new data from three different sources: (1) Usage generated data from a widely used reading app, (2) Data from a national reading ability test, and (3) Register data on student background and family characteristics. First, we find that reading app generated data to some degree tells the same story about reading ability as does the formal national reading ability test. Second, we find that the reading app data has the potential to monitor reading speed progress. Finally, we tested several models including machine learning models. Two of these were able to identify variables associated with reading speed progress with some degree of success and to point at certain conditions that promotes reading speed progress. We discuss the results and avenues for further research are presented