[2025S] Molly Han (BS)

Reducing Cognitive Load in Digital Reading

Molly Han

Date: 2025-02-21 / 2:00 - 3:00 PM
Location: White Hall 100

Abstract

This research investigates how pre-trained large language models (LLMs) can generate concept maps to enhance digital reading comprehension in higher education. While particularly focused on supporting neurodivergent students with their distinct information processing patterns, this approach benefits all learners facing the cognitive challenges of digital text. The study employs GPT-4o-mini to extract concepts and relationships from educational texts across ten diverse disciplines using open-domain prompts without predefined concept categories or relation types, enabling truly discipline-agnostic extraction applicable to all educational domains. Evaluation of three text processing approaches against a manually annotated gold dataset reveals that for concept extraction, section-level processing achieves the highest precision (83.62%), while paragraph-level processing demonstrates superior recall (74.51%). For relation extraction, similar patterns emerge with section-level processing showing the highest precision (78.61%) and paragraph-level processing yielding better recall (69.08%). Disciplinary variations are observed in both extraction tasks, with biology showing the strongest concept (F1=77.52%) and relation (F1=73.65%) extraction performance while humanities disciplines have comparatively lower performance. An interactive web-based visualization tool was developed that transforms extracted concepts into navigable concept maps using D3.js force-directed layouts, accessible at https://simplified-cognitext.streamlit.app/. User evaluation (n=14) revealed that while participants spent more time engaging with concept maps (22.6% increase), they experienced substantially reduced cognitive load (31.5% decrease in perceived mental effort) and completed comprehension assessments more efficiently (14.1% faster) with marginal improvements in accuracy. Qualitative feedback (mean rating: 4.21/5) highlighted the tool's effectiveness in visualizing conceptual relationships, though initial adaptation challenges were noted. This work contributes to educational technology by establishing a framework for LLM-based concept extraction, providing evidence on processing granularity effects, developing a concept categorization system for educational mapping, and creating a visualization tool with demonstrated learning benefits.

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