SOTAVerified

Knowledge Tracing

Knowledge Tracing is the task of modelling student knowledge over time so that we can accurately predict how students will perform on future interactions. Improvement on this task means that resources can be suggested to students based on their individual needs, and content which is predicted to be too easy or too hard can be skipped or delayed.

Source: Deep Knowledge Tracing

Papers

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Showing 151–160 of 215 papers

TitleStatusHype
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing ModelsCode1
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive SequencesCode1
Option Tracing: Beyond Correctness Analysis in Knowledge TracingCode1
On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs—0
Temporal Cross-Effects in Knowledge Tracing—0
Last Query Transformer RNN for knowledge tracingCode1
MUSE: Multi-Scale Temporal Features Evolution for Knowledge Tracing—0
LSTM-SAKT: LSTM-Encoded SAKT-like Transformer for Knowledge Tracing—0
On the Interpretability of Deep Learning Based Models for Knowledge Tracing—0
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All datasetsEdNetAssistments

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAKTAcc70.73—Unverified
2SAINT+AUC0.79—Unverified
3SAINTAUC0.78—Unverified
4PEBG+DKTAUC0.78—Unverified
5PEBG+DKVMNAUC0.78—Unverified
6DKVMNAUC0.77—Unverified
7DKTAUC0.76—Unverified
8GIKTAUC0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1DKTAUC0.86—Unverified
2BKTAUC0.67—Unverified