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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–215 of 215 papers

TitleStatusHype
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge TracingCode0
Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent RegularizationCode0
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job PredictionCode0
Second Language Acquisition Modeling—0
Deep Factorization Machines for Knowledge TracingCode0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Feature Engineering for Second Language Acquisition Modeling—0
Deep Trustworthy Knowledge Tracing—0
Deep Factorization Machines for Knowledge TracingCode0
Knowledge Tracing in Sequential Learning of Inflected Vocabulary—0
Dynamic Key-Value Memory Networks for Knowledge TracingCode0
A Trainable Spaced Repetition Model for Language LearningCode0
Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimationCode0
How deep is knowledge tracing?—0
Time-varying Learning and Content Analytics via Sparse Factor Analysis—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