CORE aims to understand students’ self-directed learning on the Internet, their ability to process and select online information, and the key influences on students’ use of online sources in higher education. With the rapid spread of generative AI tools such as ChatGPT, this also includes how students interact with and evaluate AI-generated content in learning contexts.
The Research Unit investigates how students’ COR skills, the characteristics of the online information landscape, and students’ learning processes are interrelated and how these relationships develop over the course of higher education.
The Research Unit focuses on three interrelated areas:
AREA A: Critical online reasoning (COR) skills of university students in economics and medicine (including social sciences and physics as comparison disciplines) and the development of students’ COR skills over the course of their university studies. CORE distinguishes between generic COR skills, which students need in everyday, cross-domain situations, and domain-specific COR skills, which draw on disciplinary knowledge and reasoning.
AREA B: Characteristics of the online sources and content university students select and use for learning about course-related topics (online Information Landscape), including accuracy and comprehensibility, media and linguistic features, narrative frames, and latent meaning structures, and how these characteristics relate to students’ source use and COR in higher education.
AERA C: Relationship between (1) the level and development of students’ COR skills, (2) the features of the online Information Landscape students use for their learning, and (3) students’ learning success in higher education.
In addition, CORE investigates how students use generative AI tools and how AI-supported information use relates to COR skills and learning outcomes.