Research Interests: My research sits at the intersection of cognition, learning, language, and human-computer interaction. I study how people attend to, comprehend, and respond to information in digital environments, with a particular interest in the cognitive and metacognitive processes that drive understanding, decision-making, engagement, and behavior. My work combines controlled experimentation, behavioral analytics, qualitative methods, and computational approaches to answer questions with direct implications for product design and user experience.
Current Work: I lead mixed-methods research on AI-powered educational technologies. My recent work examines how generative AI, personalization, reward systems, interface design, and communication modality influence engagement, metacognition, and learning. I also develop methods for evaluating AI-generated content and use large-scale behavioral data to identify opportunities for improving digital learning experiences.
Dissertation: I examined how readers evaluate their comprehension of text and how this evaluation affects their learning outcomes. Specifically, I applied the region of proximal learning model from Janet Metcalfe and colleagues to the domain of reading comprehension, and explored how readers’ perception of text as easy to process can lead to gist processing and miscomprehension. Through my testing, I also discovered that drawing attention to more challenging text areas can help reduce reader overconfidence. http://d-scholarship.pitt.edu/42533/7/Norberg_Dissertation_2022_ETD_4.pdf
How I would describe my work ethic: bias for action, meticulous about data integrity, focused, persistent, innovative, leader, communicative