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来源可信度的作用已被广泛研究。B05的主要研究人员使用一组简短的离线文本语料库，也证明了语言特征对于预测大学生在特定领域的知识测试中的表现是重要的。然而，这种相关性在多大程度上可以推广到在线信息景观(IL)仍未得到充分研究。B05解决了关于在线IL中使用的语言线索的建模的关键需求，学生在解决关键的在线推理(COR)任务时导航该IL。B05旨在开发一个语言特征的理论基础模型，该模型可以根据学生在COR任务解决过程中处理或产生的文本来预测学生COR过程(嵌入到IL中)和成绩。B05回答了以下研究问题：(I)所涉及的语言特征在普通和特定领域以及经济学、医学、社会科学和物理学四个领域的学生表现上有多大程度的差异？(Ii)这些特征在在线信息获取、关键信息评估和证据推理、论证和综合这三个认知核心方面有何不同？(Iii)这些特征适用于哪个层面：单一文本、多个文本、领域、体裁、整个IL还是作为一个整体的基础语言(S)(例如德语)？B05包括定量和定性两个部分。它首先从语言特征的定性选择开始，这些特征提供关于证据地位、信息来源和文本组织的信息。定量部分执行的任务是使这些特征可操作，使用基于机器学习的模型来扩展它们，并测试它们对上述研究问题的预测能力和特异性。定性和定量分析的结合具有计算解释学循环的形式，其中定量部分产生统计评估和预测，这些统计评估和预测可以解释为定性部分的语言分析的结果。B05提供了机器学习模型，该模型基于细粒度语言信息单元级别的COR的语言分析，将多个文本的语言特征作为信息环境的一部分，可供自动分析访问。A-项目从纵向样本中提供关于学生表现的文本和数据，并将从B05‘S的语言分析中获得结果。由于语言特征是迄今为止研究单位分析的最详细的信息单位，它们在媒体和内容属性(B04)以及叙述性和潜在意义结构(B06)方面与其他B-项目的研究相关。C08的多模式学习数据科学系统是整合B05所有数据的关键。

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.