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Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Machine learning-based classifications show that the distributional differences can be reproduced by computational linguistic = ,

Results show that participants from different domains indeed inquire different sets of online sources for the same task. Here the dfdl:element is a DFDL format annotation and the properties in it are generally called DFDL representation properties.

In this way, we develop a computational linguistic model that correlates GEN-COR abilities with properties of documents consulted for solving the GEN-COR tasks. Within the above annotation element, each attribute is a DFDL property, and each property-value pair is called a property binding.In the above the attribute representation is a DFDL property name. That is, we work with competing classifications to support our findings. A major contribution of our analyses is a multi-part text classification system that contrasts human annotation and rating of the documents used with a semi-automatic classification to predict the document type of web pages. We use machine learning to predict domain-specific group membership based on documents consulted during task solving. We present a computational model for educationally relevant texts that combines features at multiple levels (lexical, syntactic, semantic). Here, we focus on GEN-COR to distinguish between different groups of graduates from the three disciplines in the context of generic COR tasks.

For this reason, we collected data by requiring participants to solve domain-specific (DOM-COR) and generic (GEN-COR) tasks in an authentic Internet-based COR performance assessment (CORA), allowing us to disentangle the assumed components of COR abilities. To investigate this assumption, we focus on commonalities and differences in textual preferences in solving COR-related tasks between graduates/young professionals from three domains. 2016), studies have found evidence supporting their domain-specificity (Toplak et al. Although Critical Online Reasoning (COR) is often viewed as a general competency (e.g.