{"id":"https://openalex.org/W2593401995","doi":"https://doi.org/10.1145/3027385.3027429","title":"Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment","display_name":"Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment","publication_year":2017,"publication_date":"2017-02-27","ids":{"openalex":"https://openalex.org/W2593401995","doi":"https://doi.org/10.1145/3027385.3027429","mag":"2593401995"},"language":"en","primary_location":{"id":"doi:10.1145/3027385.3027429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3027385.3027429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","raw_type":"proceedings-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/A5040309398","display_name":"Alejandro Andrade","orcid":"https://orcid.org/0000-0001-8169-0223"},"institutions":[{"id":"https://openalex.org/I592451","display_name":"Indiana University","ror":"https://ror.org/01kg8sb98","country_code":"US","type":"education","lineage":["https://openalex.org/I592451"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alejandro Andrade","raw_affiliation_strings":["Indiana University"],"affiliations":[{"raw_affiliation_string":"Indiana University","institution_ids":["https://openalex.org/I592451"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5040309398"],"corresponding_institution_ids":["https://openalex.org/I592451"],"apc_list":null,"apc_paid":null,"fwci":5.6436,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.95624005,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"70","last_page":"79"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9884999990463257,"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":0.9884999990463257,"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/T11431","display_name":"Action Observation and Synchronization","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T10636","display_name":"Innovative Teaching and Learning Methods","score":0.9391000270843506,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/embodied-cognition","display_name":"Embodied cognition","score":0.8554744720458984},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.6871858835220337},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6683680415153503},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.6268547773361206},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.6084888577461243},{"id":"https://openalex.org/keywords/learning-analytics","display_name":"Learning analytics","score":0.5815296173095703},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.46074405312538147},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41291549801826477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36561593413352966},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.24389752745628357}],"concepts":[{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.8554744720458984},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6871858835220337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6683680415153503},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.6268547773361206},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.6084888577461243},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.5815296173095703},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.46074405312538147},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41291549801826477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36561593413352966},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.24389752745628357},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"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/C157915830","wikidata":"https://www.wikidata.org/wiki/Q2928001","display_name":"Bubble","level":2,"score":0.0},{"id":"https://openalex.org/C129307140","wikidata":"https://www.wikidata.org/wiki/Q6795880","display_name":"Maximum bubble pressure method","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3027385.3027429","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3027385.3027429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Seventh International Learning Analytics & Knowledge Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W193988059","https://openalex.org/W1769933788","https://openalex.org/W1924281037","https://openalex.org/W1948664808","https://openalex.org/W1969031799","https://openalex.org/W1981737016","https://openalex.org/W2007213485","https://openalex.org/W2023015865","https://openalex.org/W2032406331","https://openalex.org/W2050896993","https://openalex.org/W2078482049","https://openalex.org/W2103763726","https://openalex.org/W2114759324","https://openalex.org/W2152315455","https://openalex.org/W2159065226","https://openalex.org/W2186797923","https://openalex.org/W2330244257","https://openalex.org/W2518954009","https://openalex.org/W2520384474","https://openalex.org/W2522300916","https://openalex.org/W2530938597","https://openalex.org/W2724784967","https://openalex.org/W4212844288","https://openalex.org/W4298696186"],"related_works":["https://openalex.org/W1808810477","https://openalex.org/W2212007970","https://openalex.org/W200960509","https://openalex.org/W1965192441","https://openalex.org/W2108964641","https://openalex.org/W2088956580","https://openalex.org/W2120734010","https://openalex.org/W1544165406","https://openalex.org/W1974644051","https://openalex.org/W2593401995"],"abstract_inverted_index":{"The":[0],"aim":[1],"of":[2,22,30,52,66,104,107,111,139],"this":[3],"paper":[4],"is":[5],"to":[6,69,119,126,145],"show":[7,83,135],"how":[8,16,73,120,136],"multimodal":[9],"learning":[10,80],"analytics":[11],"(MMLA)":[12],"can":[13],"help":[14],"understand":[15],"elementary":[17],"students":[18,124],"explore":[19],"the":[20,41,79,130],"concept":[21],"feedback":[23,108,140],"loops":[24,141],"while":[25],"controlling":[26],"an":[27,38],"embodied":[28,90],"simulation":[29],"a":[31,76],"predator-prey":[32],"ecosystem":[33],"using":[34],"hand":[35],"movements":[36],"as":[37,118],"interface":[39],"with":[40,99,154],"computer":[42],"simulation.":[43,131],"We":[44],"represent":[45],"student":[46,67,112],"motion":[47,62,86,94],"patterns":[48,63,95,117],"from":[49],"fine-grained":[50],"logs":[51],"hands":[53],"and":[54,57,92,101,122],"gaze":[55,113],"data,":[56],"then":[58],"map":[59],"these":[60,93],"observed":[61],"against":[64],"levels":[65,103],"performance":[68],"make":[70],"inferences":[71],"about":[72],"embodiment":[74,152],"plays":[75],"role":[77],"in":[78,88,129],"process.":[81],"Results":[82],"five":[84],"distinct":[85],"sequences":[87],"students'":[89,105,137],"interactions,":[91],"are":[96],"statistically":[97],"associated":[98],"initial":[100],"post-tutorial":[102],"understanding":[106],"loops.":[109],"Analysis":[110],"also":[114],"shows":[115],"distinctive":[116],"low-":[121],"high-performing":[123],"attended":[125],"information":[127],"presented":[128],"Using":[132],"MMLA,":[133],"we":[134],"explanations":[138],"look":[142],"differently":[143],"according":[144],"cluster":[146],"membership,":[147],"which":[148],"provides":[149],"evidence":[150],"that":[151],"interacts":[153],"conceptual":[155],"understanding.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}