{"id":"https://openalex.org/W3155376332","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534159","title":"Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning","display_name":"Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3155376332","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534159","mag":"3155376332"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.10258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075072841","display_name":"Leandro M. de Lima","orcid":"https://orcid.org/0000-0001-5744-6511"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Leandro M. De Lima","raw_affiliation_strings":["Graduate Program in Computer Science PPGI, Federal University of Espirito Santo, UFES, Vit\u00f3ria, Brazil","Universidade Federal do Esp\u00edrito Santo"],"affiliations":[{"raw_affiliation_string":"Graduate Program in Computer Science PPGI, Federal University of Espirito Santo, UFES, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]},{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015026639","display_name":"Renato A. Krohling","orcid":"https://orcid.org/0000-0001-8861-4274"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Renato A. Krohling","raw_affiliation_strings":["Graduate Program in Computer Science PPGI LABCIN, Federal University of Espirito Santo, UFES, Vit\u00f3ria, Brazil","Universidade Federal do Esp\u00edrito Santo"],"affiliations":[{"raw_affiliation_string":"Graduate Program in Computer Science PPGI LABCIN, Federal University of Espirito Santo, UFES, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]},{"raw_affiliation_string":"Universidade Federal do Esp\u00edrito Santo","institution_ids":["https://openalex.org/I51235708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075072841"],"corresponding_institution_ids":["https://openalex.org/I51235708"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04217496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11122","display_name":"Online Learning and Analytics","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9769999980926514,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.8289927244186401},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6542672514915466},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5987794399261475},{"id":"https://openalex.org/keywords/evasion","display_name":"Evasion (ethics)","score":0.5446048378944397},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4279707372188568},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.4195418357849121},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36878836154937744},{"id":"https://openalex.org/keywords/risk-analysis","display_name":"Risk analysis (engineering)","score":0.3295815587043762},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.11729121208190918}],"concepts":[{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.8289927244186401},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6542672514915466},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5987794399261475},{"id":"https://openalex.org/C2781251061","wikidata":"https://www.wikidata.org/wiki/Q5416089","display_name":"Evasion (ethics)","level":3,"score":0.5446048378944397},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4279707372188568},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.4195418357849121},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36878836154937744},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3295815587043762},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.11729121208190918},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C8891405","wikidata":"https://www.wikidata.org/wiki/Q1059","display_name":"Immune system","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534159","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534159","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2104.10258","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10258","pdf_url":"https://arxiv.org/pdf/2104.10258","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3155376332","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2104.10258.pdf","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2104.10258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2104.10258","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.10258","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10258","pdf_url":"https://arxiv.org/pdf/2104.10258","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W192920577","https://openalex.org/W1585610988","https://openalex.org/W1771410628","https://openalex.org/W1809653203","https://openalex.org/W1834446384","https://openalex.org/W2073459066","https://openalex.org/W2121863487","https://openalex.org/W2123123669","https://openalex.org/W2145339207","https://openalex.org/W2155968351","https://openalex.org/W2160642098","https://openalex.org/W2169889617","https://openalex.org/W2173564293","https://openalex.org/W2209913494","https://openalex.org/W2334782222","https://openalex.org/W2557283755","https://openalex.org/W2592955949","https://openalex.org/W2768978543","https://openalex.org/W2859967432","https://openalex.org/W2896893468","https://openalex.org/W2898621204","https://openalex.org/W2911048623","https://openalex.org/W2915650894","https://openalex.org/W2949608212","https://openalex.org/W2952523895","https://openalex.org/W2953981431","https://openalex.org/W2963376229","https://openalex.org/W2963704132","https://openalex.org/W2964068481","https://openalex.org/W2964291307","https://openalex.org/W2964297722","https://openalex.org/W2975999915","https://openalex.org/W2979211489","https://openalex.org/W2991355586","https://openalex.org/W3003555941","https://openalex.org/W3016525976","https://openalex.org/W3022566517","https://openalex.org/W3033639178","https://openalex.org/W3033818228","https://openalex.org/W3034084488","https://openalex.org/W3034607397","https://openalex.org/W3034786558","https://openalex.org/W3037024314","https://openalex.org/W3043057128","https://openalex.org/W3093286081","https://openalex.org/W3120374777","https://openalex.org/W3131920644","https://openalex.org/W6635035540","https://openalex.org/W6638018090","https://openalex.org/W6668990524","https://openalex.org/W6685444567","https://openalex.org/W6704084210","https://openalex.org/W6733989970","https://openalex.org/W6744838376","https://openalex.org/W6746030470","https://openalex.org/W6748645729","https://openalex.org/W6753183898","https://openalex.org/W6755489178","https://openalex.org/W6757469721","https://openalex.org/W6763704811","https://openalex.org/W6764976746","https://openalex.org/W6768425981","https://openalex.org/W6768617876","https://openalex.org/W6771270455","https://openalex.org/W6774583691","https://openalex.org/W6776438516","https://openalex.org/W6776601253","https://openalex.org/W6779733183","https://openalex.org/W6779963924","https://openalex.org/W6784792365","https://openalex.org/W6840678999"],"related_works":["https://openalex.org/W36993090","https://openalex.org/W607273292","https://openalex.org/W2124425328","https://openalex.org/W2061650261","https://openalex.org/W2949457065","https://openalex.org/W2330303633","https://openalex.org/W2068359380","https://openalex.org/W2965121436","https://openalex.org/W3174205737","https://openalex.org/W2950031077","https://openalex.org/W2536197402","https://openalex.org/W2139262411","https://openalex.org/W3098974127","https://openalex.org/W3158551097","https://openalex.org/W2556248233","https://openalex.org/W2945825390","https://openalex.org/W3206450628","https://openalex.org/W2979489903","https://openalex.org/W189498254","https://openalex.org/W3193357523"],"abstract_inverted_index":{"High":[0],"dropout":[1],"rates":[2],"in":[3,40],"tertiary":[4],"education":[5],"expose":[6],"a":[7,55,79],"lack":[8],"of":[9,14,62,81,97],"efficiency":[10],"that":[11,104],"causes":[12],"frustration":[13],"expectations":[15],"and":[16,38],"financial":[17],"waste.":[18],"Predicting":[19],"students":[20,66,99],"at":[21],"risk":[22],"is":[23,90,124],"not":[24],"enough":[25],"to":[26,59,71,111,126],"avoid":[27,75],"student":[28,76,136],"dropout.":[29,77,137],"Usually,":[30],"an":[31],"appropriate":[32,130],"aid":[33,63,131],"action":[34],"must":[35],"be":[36],"discovered":[37],"applied":[39],"the":[41,60,105,119],"proper":[42],"time":[43],"for":[44,65],"each":[45],"student.":[46],"To":[47],"tackle":[48],"this":[49],"sequential":[50],"decision-making":[51],"problem,":[52],"we":[53],"propose":[54],"decision":[56],"support":[57,72],"method":[58,106],"selection":[61],"actions":[64,132],"using":[67,94],"offline":[68],"reinforcement":[69],"learning":[70],"decision-makers":[73,128],"effectively":[74],"Additionally,":[78],"discretization":[80],"student's":[82],"state":[83],"space":[84],"applying":[85],"two":[86],"different":[87],"clustering":[88],"methods":[89],"evaluated.":[91],"Our":[92],"experiments":[93],"logged":[95,120],"data":[96],"real":[98],"shows,":[100],"through":[101],"off-policy":[102],"evaluation,":[103],"should":[107],"achieve":[108],"roughly":[109],"1.0":[110],"1.5":[112],"times":[113],"as":[114,118],"much":[115],"cumulative":[116],"reward":[117],"policy.":[121],"So,":[122],"it":[123],"feasible":[125],"help":[127],"apply":[129],"and,":[133],"possibly,":[134],"reduce":[135]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
