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 91–100 of 215 papers

TitleStatusHype
Interpreting Deep Knowledge Tracing Model on EdNet Dataset—0
Dynamic Knowledge embedding and tracing—0
A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability—0
Feature Engineering for Second Language Acquisition Modeling—0
Dual-State Personalized Knowledge Tracing with Emotional Incorporation—0
CLUF: a Neural Model for Second Language Acquisition Modeling—0
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation—0
Do we need to go Deep? Knowledge Tracing with Big Data—0
A Survey of Explainable Knowledge Tracing—0
CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer—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