Mobile terminal-assisted interactive English learning design to facilitate knowledge deepening

This study designs and evaluates a mobile-assisted language learning system that integrates knowledge graphs with multimodal interactionincluding touch, voice, and augmented reality -to support deep knowledge comprehension.A controlled experiment compared an experimental group using the proposed system against control groups using either a Bayesian knowledge-tracing tutor or a conventional multimodal application.Results demonstrated that the experimental group achieved significantly higher posttest scores, with a learning gain improvement of nearly 40%.Cognitive load measurements showed a significant reduction, supported by a greatly increased effective interaction ratio.Over 85% of interactions were meaningful, contributing to enhanced knowledge structure formation.These outcomes confirm that tightly coupling knowledge-aware scaffolding with multimodal mobile interaction improves learning efficiency, reduces cognitive burden, and supports integrated knowledge framework development.This research provides theoretical and practical implications for designing intelligent, cognitive-friendly mobile learning systems.