{"id":"https://openalex.org/W7117959905","doi":"https://doi.org/10.48550/arxiv.2512.23941","title":"Disentangling Learning from Judgment: Representation Learning for Open Response Analytics","display_name":"Disentangling Learning from Judgment: Representation Learning for Open Response Analytics","publication_year":2025,"publication_date":"2025-12-30","ids":{"openalex":"https://openalex.org/W7117959905","doi":"https://doi.org/10.48550/arxiv.2512.23941"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.23941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23941","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.23941","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037442366","display_name":"Conrad Borchers","orcid":"https://orcid.org/0000-0003-3437-8979"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Borchers, Conrad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006009233","display_name":"Manit Rajendrakumar Patel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patel, Manit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050462802","display_name":"Seiyon M. Lee","orcid":"https://orcid.org/0009-0001-9041-3313"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Seiyon M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016746228","display_name":"Anthony F. Botelho","orcid":"https://orcid.org/0000-0002-7373-4959"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Botelho, Anthony F.","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":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.4381999969482422,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.4381999969482422,"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"}},{"id":"https://openalex.org/T10636","display_name":"Innovative Teaching and Learning Methods","score":0.1006999984383583,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10959","display_name":"Student Assessment and Feedback","score":0.04729999974370003,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6729000210762024},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5378000140190125},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.36309999227523804},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.3594000041484833},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.35600000619888306},{"id":"https://openalex.org/keywords/grading","display_name":"Grading (engineering)","score":0.3513999879360199},{"id":"https://openalex.org/keywords/rubric","display_name":"Rubric","score":0.3513999879360199}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6729000210762024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6261000037193298},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5121999979019165},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4683000147342682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.392300009727478},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C2777286243","wikidata":"https://www.wikidata.org/wiki/Q5591926","display_name":"Grading (engineering)","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.34929999709129333},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2784000039100647},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.23941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23941","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.23941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23941","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8747848272323608}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-ended":[0],"responses":[1],"are":[2,108],"central":[3],"to":[4,70,152,180],"learning,":[5],"yet":[6],"automated":[7],"scoring":[8],"often":[9],"conflates":[10],"what":[11],"students":[12,157],"wrote":[13],"with":[14,52,68,110,188],"how":[15,156],"teachers":[16,177],"grade.":[17],"We":[18,55],"present":[19],"an":[20],"analytics-first":[21],"framework":[22],"that":[23,95,165],"separates":[24],"content":[25,111,135],"signals":[26],"from":[27,134,168],"rater":[28,126],"tendencies,":[29],"making":[30],"judgments":[31],"visible":[32],"and":[33,49,59,63,73,85,142,178,193],"auditable":[34],"via":[35],"analytics.":[36],"Using":[37],"de-identified":[38],"ASSISTments":[39],"mathematics":[40],"responses,":[41],"we":[42],"model":[43,65,86],"teacher":[44,66,96],"histories":[45],"as":[46,150],"dynamic":[47],"priors":[48,69,97,107],"represent":[50],"text":[51],"sentence":[53],"embeddings.":[54],"apply":[56],"centroid":[57],"normalization":[58],"response-problem":[60],"embedding":[61,140],"differences,":[62],"explicitly":[64],"effects":[67,127],"reduce":[71],"problem-":[72],"teacher-related":[74],"confounds.":[75],"Temporally-validated":[76],"linear":[77],"models":[78,116],"quantify":[79],"the":[80,102,129],"contributions":[81],"of":[82,131,190],"each":[83],"signal,":[84],"disagreements":[87],"surface":[88],"observations":[89],"for":[90,125,174],"qualitative":[91],"inspection.":[92],"Results":[93],"show":[94],"heavily":[98],"influence":[99],"grade":[100],"predictions;":[101],"strongest":[103],"results":[104],"arise":[105],"when":[106],"combined":[109],"embeddings":[112,167],"(AUC~0.815),":[113],"while":[114],"content-only":[115],"remain":[117],"above":[118],"chance":[119],"but":[120],"substantially":[121],"weaker":[122],"(AUC~0.626).":[123],"Adjusting":[124],"sharpens":[128],"selection":[130],"features":[132,170],"derived":[133],"representations,":[136],"retaining":[137],"more":[138],"informative":[139],"dimensions":[141],"revealing":[143],"cases":[144],"where":[145,182],"semantic":[146],"evidence":[147,189],"supports":[148],"understanding":[149],"opposed":[151],"surface-level":[153],"differences":[154],"in":[155],"respond.":[158],"The":[159],"contribution":[160],"presents":[161],"a":[162],"practical":[163],"pipeline":[164],"transforms":[166],"mere":[169],"into":[171],"learning":[172],"analytics":[173],"reflection,":[175],"enabling":[176],"researchers":[179],"examine":[181],"grading":[183],"practices":[184],"align":[185],"(or":[186],"conflict)":[187],"student":[191],"reasoning":[192],"learning.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-02T00:00:00"}