{"id":"https://openalex.org/W4412889196","doi":"https://doi.org/10.18653/v1/2025.bea-1.53","title":"A Bayesian Approach to Inferring Prerequisite Structures and Topic Difficulty in Language Learning","display_name":"A Bayesian Approach to Inferring Prerequisite Structures and Topic Difficulty in Language Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889196","doi":"https://doi.org/10.18653/v1/2025.bea-1.53"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.bea-1.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.bea-1.53","pdf_url":"https://aclanthology.org/2025.bea-1.53.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.bea-1.53.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043216819","display_name":"Anh\u2010Duc Vu","orcid":"https://orcid.org/0009-0005-1186-0510"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anh-Duc Vu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100721398","display_name":"Jue Hou","orcid":"https://orcid.org/0000-0001-9404-2022"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jue Hou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031452916","display_name":"Anisia Katinskaia","orcid":"https://orcid.org/0000-0003-4137-6760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anisia Katinskaia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088123595","display_name":"Ching\u2010Fan Sheu","orcid":"https://orcid.org/0000-0002-5978-5768"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ching-Fan Sheu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5048910943","display_name":"Roman Yangarber","orcid":"https://orcid.org/0000-0001-5264-9870"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roman Yangarber","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"737","last_page":"751"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9154000282287598,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9154000282287598,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7988152503967285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5620626211166382},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5357754230499268},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.500758171081543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3762582540512085},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35611921548843384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7988152503967285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5620626211166382},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5357754230499268},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.500758171081543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3762582540512085},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35611921548843384}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.bea-1.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.bea-1.53","pdf_url":"https://aclanthology.org/2025.bea-1.53.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.bea-1.53","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.bea-1.53","pdf_url":"https://aclanthology.org/2025.bea-1.53.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8199999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889196.pdf","grobid_xml":"https://content.openalex.org/works/W4412889196.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Understanding":[0],"how":[1],"linguistic":[2],"topics":[3],"are":[4],"related":[5],"to":[6,23,113,141],"each":[7],"another":[8],"is":[9],"essential":[10],"for":[11],"designing":[12],"effective":[13],"and":[14,27,39,85,93,133,145],"adaptive":[15,146],"second-language":[16],"(L2)":[17],"instruction.We":[18],"present":[19],"a":[20,31,43,67,124],"data-driven":[21],"framework":[22],"model":[24],"topic":[25,37,83],"dependencies":[26],"their":[28],"difficulty":[29,38,84],"within":[30],"L2":[32],"learning":[33,144],"curriculum.First,":[34],"we":[35,50,77,97],"estimate":[36],"student":[40],"ability":[41,104],"using":[42,91],"three-parameter":[44],"Item":[45],"Response":[46],"Theory":[47],"(IRT)":[48],"model.Second,":[49],"construct":[51],"topic-level":[52],"knowledge":[53],"graphs-as":[54],"directed":[55],"acyclic":[56],"graphs":[57,90],"(DAGs)-to":[58],"capture":[59],"the":[60,64,71,79,86,89,99,108,115,128,138],"prerequisite":[61],"relations":[62],"among":[63],"topics,":[65],"comparing":[66],"threshold-based":[68],"method":[69],"with":[70,105],"statistical":[72],"Grow-Shrink":[73],"Markov":[74],"Blanket":[75],"algorithm.Third,":[76],"evaluate":[78],"alignment":[80],"between":[81,127],"IRTinferred":[82],"structure":[87],"of":[88,102,107],"edge-level":[92],"global":[94],"ordering":[95],"metrics.Finally,":[96],"compare":[98],"IRT-based":[100],"estimates":[101],"learner":[103,120],"assessments":[106],"learners":[109],"provided":[110],"by":[111],"teachers":[112],"validate":[114],"model's":[116],"effectiveness":[117],"in":[118,149],"capturing":[119],"proficiency.Our":[121],"results":[122],"show":[123],"promising":[125],"agreement":[126],"inferred":[129],"graphs,":[130],"IRT":[131],"estimates,":[132],"human":[134],"teachers'":[135],"assessments,":[136],"highlighting":[137],"framework's":[139],"potential":[140],"support":[142],"personalized":[143],"curriculum":[147],"design":[148],"intelligent":[150],"tutoring":[151],"systems.":[152]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
