The comparison results between the LSTKT models with two different architectures.
<p>The comparison results between the LSTKT models with two different architectures.</p>
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2025
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| _version_ | 1852016801658961920 |
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| author | Ailian Gao (20629841) |
| author2 | Zenglei Liu (20629838) |
| author2_role | author |
| author_facet | Ailian Gao (20629841) Zenglei Liu (20629838) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ailian Gao (20629841) Zenglei Liu (20629838) |
| dc.date.none.fl_str_mv | 2025-09-09T17:32:47Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330433.g009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_comparison_results_between_the_LSTKT_models_with_two_different_architectures_/30088480 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cancer Science Policy Biological Sciences not elsewhere classified students &# 8217 integrate temporal information conducted comparison experiments bidirectional lstm model series forecasting pipeline machine learning algorithms proposed lstkt model proposed informer model publicly available dataset individual knowledge states informer </ p achieved promising outcomes short sequence prediction probability sparse self implement knowledge tracing long sequence time sparse self series prediction knowledge tracing sequence time tracing studies time stamps time stamp ednet dataset assistments2017 dataset assistments2009 dataset current knowledge target exercises previous approaches learning performance extensively utilized existing models exercising recordings decoder architecture canonical encoder attention module attention mechanism answering records 82 %. 81 %. |
| dc.title.none.fl_str_mv | The comparison results between the LSTKT models with two different architectures. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The comparison results between the LSTKT models with two different architectures.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ea73d4e3d6f9c60fc22bc76fccf00e32 |
| identifier_str_mv | 10.1371/journal.pone.0330433.g009 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30088480 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The comparison results between the LSTKT models with two different architectures.Ailian Gao (20629841)Zenglei Liu (20629838)CancerScience PolicyBiological Sciences not elsewhere classifiedstudents &# 8217integrate temporal informationconducted comparison experimentsbidirectional lstm modelseries forecasting pipelinemachine learning algorithmsproposed lstkt modelproposed informer modelpublicly available datasetindividual knowledge statesinformer </ pachieved promising outcomesshort sequence predictionprobability sparse selfimplement knowledge tracinglong sequence timesparse selfseries predictionknowledge tracingsequence timetracing studiestime stampstime stampednet datasetassistments2017 datasetassistments2009 datasetcurrent knowledgetarget exercisesprevious approacheslearning performanceextensively utilizedexisting modelsexercising recordingsdecoder architecturecanonical encoderattention moduleattention mechanismanswering records82 %.81 %.<p>The comparison results between the LSTKT models with two different architectures.</p>2025-09-09T17:32:47ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0330433.g009https://figshare.com/articles/figure/The_comparison_results_between_the_LSTKT_models_with_two_different_architectures_/30088480CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300884802025-09-09T17:32:47Z |
| spellingShingle | The comparison results between the LSTKT models with two different architectures. Ailian Gao (20629841) Cancer Science Policy Biological Sciences not elsewhere classified students &# 8217 integrate temporal information conducted comparison experiments bidirectional lstm model series forecasting pipeline machine learning algorithms proposed lstkt model proposed informer model publicly available dataset individual knowledge states informer </ p achieved promising outcomes short sequence prediction probability sparse self implement knowledge tracing long sequence time sparse self series prediction knowledge tracing sequence time tracing studies time stamps time stamp ednet dataset assistments2017 dataset assistments2009 dataset current knowledge target exercises previous approaches learning performance extensively utilized existing models exercising recordings decoder architecture canonical encoder attention module attention mechanism answering records 82 %. 81 %. |
| status_str | publishedVersion |
| title | The comparison results between the LSTKT models with two different architectures. |
| title_full | The comparison results between the LSTKT models with two different architectures. |
| title_fullStr | The comparison results between the LSTKT models with two different architectures. |
| title_full_unstemmed | The comparison results between the LSTKT models with two different architectures. |
| title_short | The comparison results between the LSTKT models with two different architectures. |
| title_sort | The comparison results between the LSTKT models with two different architectures. |
| topic | Cancer Science Policy Biological Sciences not elsewhere classified students &# 8217 integrate temporal information conducted comparison experiments bidirectional lstm model series forecasting pipeline machine learning algorithms proposed lstkt model proposed informer model publicly available dataset individual knowledge states informer </ p achieved promising outcomes short sequence prediction probability sparse self implement knowledge tracing long sequence time sparse self series prediction knowledge tracing sequence time tracing studies time stamps time stamp ednet dataset assistments2017 dataset assistments2009 dataset current knowledge target exercises previous approaches learning performance extensively utilized existing models exercising recordings decoder architecture canonical encoder attention module attention mechanism answering records 82 %. 81 %. |