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

B05：信息景观 (IL) 建模，用于评估和分析特定领域和通用关键在线推理（DOM-COR 和 GEN-COR）

• 批准号: 520621868
• 负责人: Professor Dr. Walter Bisang
• 金额: --
• 依托单位: Lehrstuhl für Texttechnologie und Web Mining
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/520621868?language=en
• 关键词: B05; Modelling; Information; Landscape; IL

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.

语言提示在文本可读性或Web源信誉方面的作用已广泛研究。 B05的主要研究人员使用简短的离线文本语料库还表明，语言特征对于预测特定领域知识测试的大学生表现很重要。但是，这种相关性在多大程度上可以推广到在线信息格局（IL）的程度上。 B05解决了有关在线IL中使用的语言提示建模的关键欲望，学生在解决关键的在线推理（COR）任务时会导航。 B05旨在开发一种理论基础的语言特征模型，该模型可以预测学生的过程（嵌入在IL中）和表演，具体取决于学生在COR任务解决过程中的文本或产生的文本。 B05解决了以下研究问题：（i）在多大程度上，在多大程度上，学生在通用和特定领域的COR中以及在经济学，医学，社会科学和物理学的四个领域中的表现有所不同？ （ii）这些特征在在线信息获取，关键信息评估以及证据，论证和综合的推理的三个认知COR方面有何不同？ （iii）这些功能适用于哪个级别：单书，多个文本，域，流派，整个IL或基础语言（例如德语）整体？ B05由定量和定性零件组成。它始于语言特征的定性选择，这些特征提供有关证据状态，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，并在上述研究问题上测试其预测能力和特殊性。定性和定量分析的整合具有计算诠释学圆的形式，其中定量部分会产生统计评估和预测，这些评估和预测可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，这些模型将基于对精细语言信息单位的COR的语言分析，使自动分析可访问的信息景观的语言特征作为信息景观的一部分。 A-Projects提供了纵向样本中学生表现的文本和数据，并将从B05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项目中的研究相关，从媒体和内容属性（B04）以及叙事和潜在含义结构（B06）方面。 C08的多模式学习数据科学系统对于整合B05的所有数据至关重要。