The climate for entrepreneurship at higher education institutions

While the importance of a supportive context for entrepreneurship is widely acknowledged, its antecedents are rarely investigated. We apply the concept of organizational climate to higher education institutions and examine the drivers of students’ perceptions of the entrepreneurial climate in their university. Combining data from two unique datasets and using multilevel techniques, we analyze the relationship between university characteristics and such climate perceptions of 8009 students at public universities in Germany. We find university entrepreneurship measures to have a positive effect on students’ climate perceptions, which also depend on students’ background and gender. In addition, we find evidence for different peer effects, depending on students’ affinity for entrepreneurship. For the general student population, including entrepreneurship content in their normal studies seems to be required to initiate a social process of sensemaking. However, students’ perception of the entrepreneurial climate only depend to a certain degree on intentional entrepreneurship measures. In our study, general university characteristics have the strongest influence on climate perceptions. Overall, our study adds to our understanding of which parameters are important for establishing a more favorable and inspiring climate for becoming an entrepreneur at higher education institutions.     

A professional development model to facilitate teacher adoption of interactive,immersive digital games for classroom learning

The benefits of using digital games in the curriculum are well documented in literature. Most teachers who use digital games use short-form drill-and-practice learning games rather than the kinds of games most students would choose to play in their free time. The use of more interactive, immersive digital games (IDGs) in classrooms tends to be sporadic, dependent upon the enthusiasm and ingenuity of individual teachers. Previous studies have indicated that many teachers have concerns about using digital games in classrooms and have difficulties knowing how to best use digital games. This qualitative study presents a professional development model for teachers that was inductively derived through analysing in-depth, semi-structured interviews with 13 educators who have used digital games in the classroom. Implementing this model can help teachers develop attitudes and skills necessary to meaningfully use interactive, IDGs in their classrooms. Evidence suggests that a mentor should have a strong understanding of how to use digital games effectively in classrooms, work with small cohorts of teachers to collegially plan the use of digital games within a unit of class work, trial the unit of work with a small number of enthusiastic teachers, and then implement it with the remaining teachers. This approach is based on interviews with educators who have successfully mentored teachers in their schools to use interactive, IDGs in their classrooms. These educators encountered little resistance from their teaching colleagues. Their approach further supported teachers who were open to the possibilities of using digital games in their classrooms.     

A simple ratio adjustment for baseline recording in natural settings

The usefulness of a baseline record is often compromised by extraneous events in the natural environment that artificially intervene to prevent the performance of a specific target behavior. To attenuate this problem, a simple ratio adjustment procedure was suggested that compares the observed response frequency with a maximum possible frequency for each observation period. This ratio is then transformed into a response percentage with a more general predictive value for the behavior modification practitioner.     

Efficient reverse k-nearest neighbor estimation

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.