{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/saint-integrating-temporal-features-for-ednet","title":"SAINT+: Integrating Temporal Features for EdNet Correctness Prediction","arxiv_id":"2010.12042","date":"2020-10-19","proceeding":null,"authors":["Dongmin Shin","Yugeun Shim","Hangyeol Yu","Seewoo Lee","Byungsoo Kim","Youngduck Choi"],"abstract":"We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset.","url_abs":"https://arxiv.org/abs/2010.12042v2","url_pdf":"https://arxiv.org/pdf/2010.12042v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"saint-integrating-temporal-features-for-ednet","repo_url":"https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"saint-integrating-temporal-features-for-ednet","repo_url":"https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"saint-integrating-temporal-features-for-ednet","repo_url":"https://github.com/maroxtn/SAINT-Transformer-riiid-kaggle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"saint-integrating-temporal-features-for-ednet","repo_url":"https://github.com/arshadshk/SAINT-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/knowledge-tracing-on-ednet","task":"Knowledge Tracing","dataset":"EdNet","model":"SAINT+","rank_in_archive_order":1,"of":8,"metrics":{"AUC":"0.7914","Acc":"72.52"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-tracing-on-ednet","task":"Knowledge Tracing","dataset":"EdNet","model":"DKVMN","rank_in_archive_order":5,"of":8,"metrics":{"AUC":"0.7663","Acc":"70.79"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-tracing-on-ednet","task":"Knowledge Tracing","dataset":"EdNet","model":"DKT","rank_in_archive_order":6,"of":8,"metrics":{"AUC":"0.7638","Acc":"70.6"},"uses_additional_data":false},{"leaderboard":"/sota/knowledge-tracing-on-ednet","task":"Knowledge Tracing","dataset":"EdNet","model":"SAKT","rank_in_archive_order":8,"of":8,"metrics":{"Acc":"70.73"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.12042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12042"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/maroxtn/SAINT-Transformer-riiid-kaggle","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/arshadshk/SAINT-pytorch","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch","reach":null}],"summary":{"ran_draft_wrong":1,"ran_violates":1,"ran_honours":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"891b8ebab395921f","entry":"get_clones","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"891b8ebab395921f"}},{"code_sha256_prefix":"bd0f5539ff1d6cbe","entry":"get_mask","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"bd0f5539ff1d6cbe"}},{"code_sha256_prefix":"31d0bea1c5a0c6d5","entry":"get_pos","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"31d0bea1c5a0c6d5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}