{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/309615"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/309615","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"FROM FAMILY INTERACTION TO DYNAMIC FEEDBACK:ADVANCES IN MANDARIN LANGUAGE LEARNING APPLICATIONS","abstract":"Mandarin Chinese is vital as Singapore's second most spoken language, but pronunciation training remains underrepresented in learning. This thesis covers two Computer-Assisted Language Learning (CALL) projects enhancing Mandarin acquisition. The first focuses on a tablet app for children and parents, using Automatic Speech Evaluation (ASE) to give phoneme and tone feedback, encouraging parent-child interaction. The second project integrates a Large Language Model (LLM) into a language app for young adults, providing personalized feedback based on the Zone of Proximal Development (ZPD) theory. Both projects use mixed methods to evaluate effectiveness and user interaction. The findings aim to contribute to the fields of Human-Computer Interaction (HCI) and language education, offering insights into the co-use of technology for language learning in family settings and the potential of LLMs to support dynamic feedback in pronunciation training.","abstract_html":"Mandarin Chinese is vital as Singapore's second most spoken language, but pronunciation training remains underrepresented in learning. This thesis covers two Computer-Assisted Language Learning (CALL) projects enhancing Mandarin acquisition. The first focuses on a tablet app for children and parents, using Automatic Speech Evaluation (ASE) to give phoneme and tone feedback, encouraging parent-child interaction. The second project integrates a Large Language Model (LLM) into a language app for young adults, providing personalized feedback based on the Zone of Proximal Development (ZPD) theory. Both projects use mixed methods to evaluate effectiveness and user interaction. The findings aim to contribute to the fields of Human-Computer Interaction (HCI) and language education, offering insights into the co-use of technology for language learning in family settings and the potential of LLMs to support dynamic feedback in pronunciation training.","abstract_has_math":false,"creators":["SHI MINGQIAN"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08-28","date_published":"2024-08-28","updated_at":"2026-07-24T03:33:34Z","subjects":["Large Language Model","Computer-Assisted Pronunciation Training","Computer-Assisted Language Learning","Human-Computer Interaction"],"languages":[],"rights":[],"rights_urls":["https://scholarbank.nus.edu.sg/bitstreams/f90c9d46-505d-4c2b-ba71-d267fb0b0e13/download"],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["SHI MINGQIAN"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-08-28"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/309615"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Large Language Model","Computer-Assisted Pronunciation Training","Computer-Assisted Language Learning","Human-Computer Interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["https://scholarbank.nus.edu.sg/bitstreams/f90c9d46-505d-4c2b-ba71-d267fb0b0e13/download"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/4bd0d3e3-57cd-4038-a14e-df4f7b96be8e/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Mandarin Chinese is vital as Singapore's second most spoken language, but pronunciation training remains underrepresented in learning. This thesis covers two Computer-Assisted Language Learning (CALL) projects enhancing Mandarin acquisition. The first focuses on a tablet app for children and parents, using Automatic Speech Evaluation (ASE) to give phoneme and tone feedback, encouraging parent-child interaction. The second project integrates a Large Language Model (LLM) into a language app for young adults, providing personalized feedback based on the Zone of Proximal Development (ZPD) theory. Both projects use mixed methods to evaluate effectiveness and user interaction. The findings aim to contribute to the fields of Human-Computer Interaction (HCI) and language education, offering insights into the co-use of technology for language learning in family settings and the potential of LLMs to support dynamic feedback in pronunciation training."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["9f3c6f2aa8f96291ef77b0a18484521d","5b8d347c362a21d1b6ca1d228ae197e2","8efab5074d3c123aeb0a9597907a6bcd"]},{"key":"dc:title","label":"Title","values":["FROM FAMILY INTERACTION TO DYNAMIC FEEDBACK:ADVANCES IN MANDARIN LANGUAGE LEARNING APPLICATIONS"]}]}],"canonical_facts":{"dc:creator":["SHI MINGQIAN"],"dc:date.issued":["2024-08-28"],"dc:description.abstract":["Mandarin Chinese is vital as Singapore's second most spoken language, but pronunciation training remains underrepresented in learning. This thesis covers two Computer-Assisted Language Learning (CALL) projects enhancing Mandarin acquisition. The first focuses on a tablet app for children and parents, using Automatic Speech Evaluation (ASE) to give phoneme and tone feedback, encouraging parent-child interaction. The second project integrates a Large Language Model (LLM) into a language app for young adults, providing personalized feedback based on the Zone of Proximal Development (ZPD) theory. Both projects use mixed methods to evaluate effectiveness and user interaction. The findings aim to contribute to the fields of Human-Computer Interaction (HCI) and language education, offering insights into the co-use of technology for language learning in family settings and the potential of LLMs to support dynamic feedback in pronunciation training."],"dc:format.checksum.md5":["9f3c6f2aa8f96291ef77b0a18484521d","5b8d347c362a21d1b6ca1d228ae197e2","8efab5074d3c123aeb0a9597907a6bcd"],"dc:identifier.uri":["https://scholarbank.nus.edu.sg/bitstreams/4bd0d3e3-57cd-4038-a14e-df4f7b96be8e/download"],"dc:relation.isreferencedby":["https://scholarbank.nus.edu.sg/handle/10635/309615"],"dc:rights":["https://scholarbank.nus.edu.sg/bitstreams/f90c9d46-505d-4c2b-ba71-d267fb0b0e13/download"],"dc:subject":["Large Language Model","Computer-Assisted Pronunciation Training","Computer-Assisted Language Learning","Human-Computer Interaction"],"dc:title":["FROM FAMILY INTERACTION TO DYNAMIC FEEDBACK:ADVANCES IN MANDARIN LANGUAGE LEARNING APPLICATIONS"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:33:34Z"}