{"id":"https://openalex.org/W3127216137","doi":"https://doi.org/10.1109/tlt.2021.3056907","title":"Clustering Introductory Computer Science Exercises Using Topic Modeling Methods","display_name":"Clustering Introductory Computer Science Exercises Using Topic Modeling Methods","publication_year":2021,"publication_date":"2021-02-01","ids":{"openalex":"https://openalex.org/W3127216137","doi":"https://doi.org/10.1109/tlt.2021.3056907","mag":"3127216137"},"language":"en","primary_location":{"id":"doi:10.1109/tlt.2021.3056907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2021.3056907","pdf_url":null,"source":{"id":"https://openalex.org/S130363450","display_name":"IEEE Transactions on Learning Technologies","issn_l":"1939-1382","issn":["1939-1382","2372-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Learning Technologies","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.10748","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Laura Oliveira Moraes","orcid":"https://orcid.org/0000-0003-0965-6703"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Laura Oliveira Moraes","raw_affiliation_strings":["Systems and Computing Engineering Department (COPPE-PESC), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-0965-6703","affiliations":[{"raw_affiliation_string":"Systems and Computing Engineering Department (COPPE-PESC), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":null,"display_name":"Carlos Eduardo Pedreira","orcid":"https://orcid.org/0000-0001-8151-8909"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Carlos Eduardo Pedreira","raw_affiliation_strings":["Systems and Computing Engineering Department (COPPE-PESC), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-8151-8909","affiliations":[{"raw_affiliation_string":"Systems and Computing Engineering Department (COPPE-PESC), Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I122140584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I122140584"],"apc_list":null,"apc_paid":null,"fwci":4.2287,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.94874907,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"14","issue":"1","first_page":"42","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.18330000340938568,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.18330000340938568,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.10540000349283218,"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/T10028","display_name":"Topic Modeling","score":0.06360000371932983,"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/pointwise-mutual-information","display_name":"Pointwise mutual information","score":0.8324999809265137},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7174000144004822},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.7078999876976013},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6722999811172485},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.6365000009536743},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5785999894142151},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.49709999561309814},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.4472000002861023},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43220001459121704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8452000021934509},{"id":"https://openalex.org/C7797323","wikidata":"https://www.wikidata.org/wiki/Q3798612","display_name":"Pointwise mutual information","level":3,"score":0.8324999809265137},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7174000144004822},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.7078999876976013},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6722999811172485},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.6365000009536743},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.49709999561309814},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.4472000002861023},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4122999906539917},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3837999999523163},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3662000000476837},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.335099995136261},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3294999897480011},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.31869998574256897},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.310699999332428},{"id":"https://openalex.org/C55177894","wikidata":"https://www.wikidata.org/wiki/Q1064031","display_name":"Topic Maps","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26460000872612},{"id":"https://openalex.org/C139502532","wikidata":"https://www.wikidata.org/wiki/Q1122090","display_name":"Computational intelligence","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tlt.2021.3056907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2021.3056907","pdf_url":null,"source":{"id":"https://openalex.org/S130363450","display_name":"IEEE Transactions on Learning Technologies","issn_l":"1939-1382","issn":["1939-1382","2372-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Learning Technologies","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2104.10748","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10748","pdf_url":"https://arxiv.org/pdf/2104.10748","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.10748","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10748","pdf_url":"https://arxiv.org/pdf/2104.10748","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1732828232","https://openalex.org/W1902027874","https://openalex.org/W1972087783","https://openalex.org/W1972978214","https://openalex.org/W1984954093","https://openalex.org/W1988680150","https://openalex.org/W2012669829","https://openalex.org/W2015020974","https://openalex.org/W2018177083","https://openalex.org/W2026034143","https://openalex.org/W2038043464","https://openalex.org/W2039552226","https://openalex.org/W2042876500","https://openalex.org/W2056825598","https://openalex.org/W2058846446","https://openalex.org/W2068022668","https://openalex.org/W2069078812","https://openalex.org/W2076219102","https://openalex.org/W2109154616","https://openalex.org/W2128886782","https://openalex.org/W2135790056","https://openalex.org/W2150593711","https://openalex.org/W2150874198","https://openalex.org/W2152163007","https://openalex.org/W2160170318","https://openalex.org/W2164562020","https://openalex.org/W2168332560","https://openalex.org/W2187358498","https://openalex.org/W2234183288","https://openalex.org/W2250533720","https://openalex.org/W2529553635","https://openalex.org/W2537968169","https://openalex.org/W2553051037","https://openalex.org/W2572386335","https://openalex.org/W2586765313","https://openalex.org/W2782303220","https://openalex.org/W2799315307","https://openalex.org/W2806542221","https://openalex.org/W2809064672","https://openalex.org/W2858889286","https://openalex.org/W2887928931","https://openalex.org/W2911048623","https://openalex.org/W2917037155","https://openalex.org/W2944162469","https://openalex.org/W2967892987","https://openalex.org/W3141704971","https://openalex.org/W6604093662","https://openalex.org/W6604306396","https://openalex.org/W6636440780","https://openalex.org/W6639222966","https://openalex.org/W6639619044","https://openalex.org/W6674385629","https://openalex.org/W6678614127","https://openalex.org/W6679482899","https://openalex.org/W6682044806","https://openalex.org/W6683333316","https://openalex.org/W6691363302","https://openalex.org/W6695690082","https://openalex.org/W6712983591","https://openalex.org/W6751373637","https://openalex.org/W6752227367"],"related_works":[],"abstract_inverted_index":{"Manually":[0],"determining":[1],"concepts":[2,32,118,143],"present":[3,144],"in":[4,62,145,151],"a":[5,10,24,29,67],"group":[6],"of":[7,115,175],"questions":[8,34,102],"is":[9,18,42],"challenging":[11],"and":[12,33,39,65,91,103,124,169],"time-consuming":[13],"process.":[14],"However,":[15],"the":[16,82,98,105,113,116,125,133,137,141,146,152,165],"process":[17],"an":[19,108],"essential":[20],"step":[21],"while":[22],"modeling":[23,55],"virtual":[25],"learning":[26],"environment":[27],"since":[28],"mapping":[30],"between":[31,101],"using":[35,107,119,132],"mastery":[36],"level":[37],"assessment":[38],"recommendation":[40],"engines":[41],"required.":[43],"In":[44],"this":[45,63],"article,":[46],"we":[47,96],"investigated":[48],"unsupervised":[49],"semantic":[50],"models":[51],"(known":[52],"as":[53],"topic":[54],"techniques)":[56],"to":[57,69],"assist":[58],"computer":[59],"science":[60],"teachers":[61],"task":[64],"propose":[66],"method":[68,167],"transform":[70],"Computer":[71],"Science":[72],"1":[73],"teacher-provided":[74],"code":[75,83],"solutions":[76],"into":[77],"representative":[78],"text":[79],"documents,":[80],"including":[81],"structure":[84],"information.":[85],"By":[86],"applying":[87],"nonnegative":[88],"matrix":[89],"factorization":[90],"latent":[92],"Dirichlet":[93],"allocation":[94],"techniques,":[95],"extract":[97],"underlying":[99],"relationship":[100],"validate":[104],"results":[106,126],"external":[109],"dataset.":[110,135],"We":[111],"consider":[112],"interpretability":[114],"learned":[117],"14":[120],"university":[121],"professors'":[122],"data,":[123],"confirm":[127],"six":[128,138],"semantically":[129],"coherent":[130],"clusters":[131],"current":[134],"Moreover,":[136],"topics":[139],"comprise":[140],"main":[142],"test":[147],"dataset,":[148],"achieving":[149],"0.75":[150],"normalized":[153],"pointwise":[154],"mutual":[155],"information":[156],"metric.":[157],"The":[158],"metric":[159],"correlates":[160],"with":[161],"human":[162],"ratings,":[163],"making":[164],"proposed":[166],"useful":[168],"providing":[170],"semantics":[171],"for":[172],"large":[173],"amounts":[174],"unannotated":[176],"code.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-02-15T00:00:00"}