{"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/last-query-transformer-rnn-for-knowledge","title":"Last Query Transformer RNN for knowledge tracing","arxiv_id":"2102.05038","date":"2021-02-10","proceeding":null,"authors":["SeungKee Jeon"],"abstract":"This paper presents an efficient model to predict a student's answer correctness given his past learning activities. Basically, I use both transformer encoder and RNN to deal with time series input. The novel point of the model is that it only uses the last input as query in transformer encoder, instead of all sequence, which makes QK matrix multiplication in transformer Encoder to have O(L) time complexity, instead of O(L^2). It allows the model to input longer sequence. Using this model I achieved the 1st place in the 'Riiid! Answer Correctness Prediction' competition hosted on kaggle.","url_abs":"https://arxiv.org/abs/2102.05038v1","url_pdf":"https://arxiv.org/pdf/2102.05038v1.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":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/arshadshk/Last_Query_Transformer_RNN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/bcaitech1/p4-dkt-no_caffeine_no_gain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/MindSpore-scientific-2/code-9/tree/main/Last-Query-Transformer-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/MindSpore-scientific/code-1/tree/main/Last-Query-Transformer-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/4/Last-Query-Transformer-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"last-query-transformer-rnn-for-knowledge","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/7/Last-Query-Transformer-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"knowledge-tracing","task_name":"Knowledge Tracing"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}