Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

Improving attitudes and knowledge in a citizen science project about urban bat ecology

In order to deal with the current, dramatic decline in biodiversity, the public at large needs to be aware of and participate in biodiversity research activities. One way to do this is citizen science projects, in which researchers collaborate with volunteering citizens in scientific research. However, it remains unclear whether engaging in such projects has an impact on the learning outcomes of volunteers. Previous research has so far presented mixed results on the improvement of citizens’ attitudes and knowledge, mostly because such research has focused only on single aspects of citizen science projects in case studies. To address these limitations, we investigated the impact of an urban bat ecology project on citizens’ attitudes and knowledge about bats, and on their engagement with citizen science. We also examined whether the degree of citizen participation (i.e., collecting data vs. collecting and analyzing data) had an influence on the outcomes. We conducted four field studies and used a survey-based, experimental, pre-/post-measurement design. To vary the degree of participation, we assessed the post measurement in one group directly after data collection, whereas, in a second group, we assessed it after data collection and analysis, at the end of the project. Across all studies, the results demonstrated that citizens’ content knowledge of urban bat ecology increased, and their attitudes toward bats and toward their engagement in citizen science improved during their participation. Citizens’ degrees of participation did not influence these outcomes. Thus, our research illustrates that citizen science can increase awareness of urban bat conservation, independently of citizens’ degree of participation. We discuss the implications of our findings for the citizen science community. © 2022 by the author(s)