Papers › SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

19 Oct 2020arXiv:2010.12042archive 2025-07-28

Dongmin Shin, Yugeun Shim, Hangyeol Yu, Seewoo Lee, Byungsoo Kim, Youngduck Choi

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.

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maroxtn/SAINT-Transformer-riiid-kaggle mentioned on GitHubpytorch report

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Tasks

DecoderKnowledge TracingPrediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Knowledge Tracing EdNet SAINT+ AUC 0.7914 #1 of 8 Archive leaderboard report
Knowledge Tracing EdNet SAINT+ Acc 72.52 #1 of 8 Archive leaderboard report
Knowledge Tracing EdNet DKVMN AUC 0.7663 #5 of 8 Archive leaderboard report
Knowledge Tracing EdNet DKVMN Acc 70.79 #5 of 8 Archive leaderboard report
Knowledge Tracing EdNet DKT AUC 0.7638 #6 of 8 Archive leaderboard report
Knowledge Tracing EdNet DKT Acc 70.6 #6 of 8 Archive leaderboard report
Knowledge Tracing EdNet SAKT Acc 70.73 #8 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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