B05: Modelling the Information Landscape (IL) for Assessing and Analyzing Domain-Specific and Generic Critical Online Reasoning (DOM-COR and GEN-COR)

语言线索对文本可读性或Web源可信度的作用已被广泛研究。使用简短的离线文本语料库，B 05的主要研究人员还表明，语言特征对于预测大学生在特定领域知识测试中的表现非常重要。然而，在多大程度上这种相关性可以推广到在线信息景观（IL）的研究仍然不足。B 05解决了在线IL中使用的语言线索建模的关键需求，学生在解决关键在线推理（COR）任务时导航。B 05旨在开发一个理论基础的语言特征模型，允许预测学生COR过程（嵌入在IL中）和表现，这取决于学生在COR任务解决过程中处理或产生的文本。B 05解决了以下研究问题：（一）在何种程度上所涉及的语言特征不同的学生的表现，在一般与特定领域的COR -在经济，医学，社会科学和物理学四个领域？(ii)这些特征与在线信息获取、关键信息评估和证据推理、论证和综合这三个认知COR方面有何不同？(iii)这些特征适用于哪些层面：单个文本、多个文本、领域、体裁、整个国际语言还是作为一个整体的基础语言（例如德语）？B 05由定量和定性两部分组成。它从定性选择语言特征开始，这些语言特征提供关于证据地位、信息来源和文本组织的信息。定量部分执行操作这些功能的任务，使用基于机器学习的模型扩展它们，并测试它们对上述研究问题的预测能力和特异性。定性和定量分析的整合具有计算解释学循环的形式，其中定量部分产生可解释为定性部分语言分析结果的统计评估和预测。B 05提供了机器学习模型，基于在细粒度语言信息单元级别对COR的语言分析，使多个文本的语言特征作为信息景观的一部分可用于自动分析。A-项目提供纵向样本中学生表现的文本和数据，并将从B 05的语言分析中获得结果。由于语言特征是迄今为止研究单元中分析的最详细的信息单元，因此它们与其他B项目中的媒体和内容属性（B 04）以及叙事和潜在意义结构（B 06）的研究相关。C 08的多模态学习数据科学系统对于整合B 05的所有数据至关重要。

The role of linguistic cues for text readability or Web source credibility has been widely studied. Using a corpus of short offline texts, principal investigators of B05 have also shown that linguistic features are important for predicting university student performance in domain-specific knowledge tests. However, it remains under-researched to what extent such correlations can be generalized to the online information landscape (IL). B05 addresses a key desideratum regarding the modelling of linguistic cues used in the online IL, which students navigate when solving critical online reasoning (COR) tasks. B05 aims to develop a theoretically grounded model of linguistic features that allows predictions of student COR processes (that are embedded in the IL) and performances, depending on the texts students process or produce during COR task-solving. B05 addresses the following research questions: (i) to what extent do the linguistic features involved differ in students’ performance in generic vs. domain-specific COR – and within the four domains of economics, medicine, social sciences and physics? (ii) How do these features differ with respect to the three cognitive COR facets of online information acquisition, critical information evaluation, and reasoning with evidence, argumentation and synthesis? (iii) At which levels do these features apply: single texts, multiple texts, domains, genres, the entire IL or the underlying language(s) (e.g. German) as a whole? B05 consists of both quantitative and qualitative parts. It starts with the qualitative selection of linguistic features that provide information on the evidentiality status, the source of information and the organization of texts. The quantitative part performs the task of operationalizing these features, expanding them using a machine learning-based model, and testing their predictive power and specificity with regard to the above research questions. The integration of qualitative and quantitative analyses has the form of a computational hermeneutic circle in which the quantitative part generates statistical evaluations and predictions that are interpretable as the results of the qualitative part’s linguistic analyses. B05 provides machine learning models that make linguistic features of multiple texts as part of the information landscape accessible to automated analysis, based on a linguistic analysis of COR at the level of fine-grained linguistic information units. The A-projects provide texts and data on students’ performances from the longitudinal sample and will obtain results from B05’s linguistic analyses. Since linguistic features are by far the most detailed information units analyzed in the research unit, they are relevant for research in the other B-projects in terms of media and content properties (B04) and narrative and latent meaning structures (B06). The Multimodal Learning Data Science System of C08 is crucial for integrating all data from B05.