{"id":"https://openalex.org/W3212779567","doi":"https://doi.org/10.1109/tbdata.2021.3125204","title":"Predicting and Understanding Student Learning Performance Using Multi-Source Sparse Attention Convolutional Neural Networks","display_name":"Predicting and Understanding Student Learning Performance Using Multi-Source Sparse Attention Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-11-04","ids":{"openalex":"https://openalex.org/W3212779567","doi":"https://doi.org/10.1109/tbdata.2021.3125204","mag":"3212779567"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2021.3125204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2021.3125204","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052639870","display_name":"Yupei Zhang","orcid":"https://orcid.org/0000-0001-8348-0545"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Yupei Zhang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069365973","display_name":"Rui An","orcid":"https://orcid.org/0000-0003-2080-3081"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui An","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763282","display_name":"Shuhui Liu","orcid":"https://orcid.org/0000-0002-8196-6880"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhui Liu","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112405071","display_name":"Jiaqi Cui","orcid":"https://orcid.org/0000-0001-6006-4442"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Cui","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009944817","display_name":"Xuequn Shang","orcid":"https://orcid.org/0000-0002-7249-8210"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuequn Shang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052639870"],"corresponding_institution_ids":["https://openalex.org/I17145004","https://openalex.org/I79576946"],"apc_list":null,"apc_paid":null,"fwci":10.9486,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.98430821,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"9","issue":"1","first_page":"118","last_page":"132"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9998000264167786,"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":0.9998000264167786,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9232000112533569,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9179999828338623,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.830623209476471},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7223824262619019},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.681865394115448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5978811979293823},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5947211384773254},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5610119104385376},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.47697100043296814},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.458414763212204},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4244700074195862}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.830623209476471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7223824262619019},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.681865394115448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5978811979293823},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5947211384773254},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5610119104385376},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.47697100043296814},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.458414763212204},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4244700074195862},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2021.3125204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2021.3125204","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5299999713897705}],"awards":[{"id":"https://openalex.org/G1287980616","display_name":null,"funder_award_id":"U1811262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3342558426","display_name":null,"funder_award_id":"2021JGY31","funder_id":"https://openalex.org/F4320321392","funder_display_name":"Northwestern Polytechnical University"},{"id":"https://openalex.org/G592411238","display_name":null,"funder_award_id":"61802313","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321392","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W123693119","https://openalex.org/W643967429","https://openalex.org/W1584308190","https://openalex.org/W2028795295","https://openalex.org/W2096451472","https://openalex.org/W2206224721","https://openalex.org/W2293235549","https://openalex.org/W2328176404","https://openalex.org/W2418778378","https://openalex.org/W2518782678","https://openalex.org/W2543707867","https://openalex.org/W2567289326","https://openalex.org/W2576265988","https://openalex.org/W2604400023","https://openalex.org/W2611285220","https://openalex.org/W2699041389","https://openalex.org/W2750637936","https://openalex.org/W2789635809","https://openalex.org/W2799194347","https://openalex.org/W2804257937","https://openalex.org/W2805918584","https://openalex.org/W2810721714","https://openalex.org/W2885343160","https://openalex.org/W2893802547","https://openalex.org/W2895544739","https://openalex.org/W2910121883","https://openalex.org/W2917871576","https://openalex.org/W2919115771","https://openalex.org/W2942021041","https://openalex.org/W2963864707","https://openalex.org/W2963878746","https://openalex.org/W2965216240","https://openalex.org/W2969215180","https://openalex.org/W2970787375","https://openalex.org/W2983296418","https://openalex.org/W2983382509","https://openalex.org/W2990636069","https://openalex.org/W2996851010","https://openalex.org/W2997255974","https://openalex.org/W3010492793","https://openalex.org/W3012145951","https://openalex.org/W3023473905","https://openalex.org/W3037176799","https://openalex.org/W3039536671","https://openalex.org/W3045034478","https://openalex.org/W3049450989","https://openalex.org/W3083540916","https://openalex.org/W3091759883","https://openalex.org/W3100321043","https://openalex.org/W3151863540","https://openalex.org/W3156333129","https://openalex.org/W3185961657","https://openalex.org/W4287643567","https://openalex.org/W6602002561","https://openalex.org/W6739901393","https://openalex.org/W6743509132","https://openalex.org/W6750943009","https://openalex.org/W6765726137","https://openalex.org/W6766620829"],"related_works":["https://openalex.org/W2899027234","https://openalex.org/W4323060069","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W2947839263","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3120400911","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Predicting":[0],"and":[1,19,50,96,140,182],"understanding":[2],"student":[3,79,145],"learning":[4,12,35,42,118,195],"performance":[5,136,146],"has":[6],"been":[7],"a":[8,40,56,70,86,110,173],"long-standing":[9],"task":[10,29],"in":[11,69],"science,":[13],"which":[14],"can":[15,30],"benefit":[16],"personalized":[17,194],"teaching":[18],"learning.":[20],"This":[21,176],"study":[22,177],"shows":[23],"that":[24,44,132],"the":[25,46,51,66,92,129,150,170,184,188,193],"progress":[26],"towards":[27,113],"this":[28,156],"be":[31],"accelerated":[32],"by":[33,147,155,168],"using":[34],"record":[36],"data":[37],"to":[38,64,82,90,99],"feed":[39],"deep":[41,117],"model":[43],"considers":[45],"intrinsic":[47],"course":[48,67,152],"association":[49,161],"structured":[52,84],"features.":[53,102],"We":[54],"proposed":[55],"multi-source":[57,101],"sparse":[58],"attention":[59,88],"convolutional":[60],"neural":[61],"network":[62],"(MsaCNN)":[63],"predict":[65],"grades":[68],"general":[71],"formulation.":[72],"MsaCNN":[73,133],"adopts":[74],"multi-scale":[75],"convolution":[76],"kernels":[77],"on":[78,125],"grade":[80],"records":[81],"capture":[83],"features,":[85],"global":[87],"strategy":[89],"discover":[91],"relationship":[93],"between":[94],"courses,":[95,166],"multiple":[97],"input-heads":[98],"integrate":[100],"All":[103],"achieved":[104],"features":[105],"are":[106],"then":[107],"poured":[108],"into":[109,122],"softmax":[111],"classifier":[112],"an":[114,142,160],"end-to-end":[115],"supervised":[116],"model.":[119],"Conducting":[120],"insights":[121],"higher":[123],"education":[124],"real-world":[126],"university":[127],"datasets,":[128],"results":[130],"show":[131],"achieves":[134],"better":[135],"than":[137],"traditional":[138],"methods":[139],"delivers":[141],"interpretation":[143],"of":[144,149],"virtue":[148],"resulted":[151],"relationships.":[153],"Inspired":[154],"interpretation,":[157],"we":[158],"created":[159],"map":[162,171,186],"for":[163],"all":[164],"mentioned":[165],"followed":[167],"evaluating":[169],"with":[172],"questionnaire":[174],"survey.":[175],"provides":[178],"computer-aided":[179],"system":[180],"tools":[181],"discovers":[183],"course-space":[185],"from":[187],"educational":[189],"data,":[190],"potentially":[191],"facilitating":[192],"progress.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-23T07:41:27.035349","created_date":"2025-10-10T00:00:00"}