Yu-Tang Hsueh

National Yunlin University of Science and Technology

Papers

Total Citations

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About

Yu-Tang Hsueh is a researcher at the forefront of technology-enhanced language learning, specializing in human-computer interaction, multimodal learning systems, and educational robotics. His most cited work, "An Innovative Multimodal Learning System Based on Robot and Tangible Objects for Chinese Numeral-Classifier-Noun Phrase Learning" (2022, 3 citations), introduces a groundbreaking approach that combines social robots with Internet of Things (IoT) tangible objects to tackle one of the most difficult aspects of Chinese language acquisition: numeral-classifier-noun (NCN) phrases. By integrating physical manipulatives with robotic feedback, Hsueh’s system creates an immersive, hands-on learning environment that bridges the gap between abstract linguistic rules and concrete sensory experiences. This multimodal methodology not only enhances learner engagement but also demonstrates significant potential for improving retention and accuracy in complex grammatical structures. Hsueh’s work contributes to the growing field of embodied cognition in education, offering a scalable model for interactive language instruction. With a focus on innovative pedagogical design and real-world applicability, his research continues to shape how technology can support second language acquisition, particularly for learners facing the unique challenges of tonal and classifier-rich languages like Mandarin Chinese.