Knowledge Tracing
69 papers with code • 2 benchmarks • 1 datasets
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
Libraries
Use these libraries to find Knowledge Tracing models and implementations
EdNet
Latest papers
KTbench: A Novel Data Leakage-Free Framework for Knowledge Tracing
badranX/KTbench
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To address these problems, we introduce a general masking framework that mitigates the first problem and enhances the performance of such KT models while preserving the original model architecture without significant alterations.
Predictive, scalable and interpretable knowledge tracing on structured domains
mlcolab/psi-kt
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This requires estimates of both the learner's progress (''knowledge tracing''; KT), and the prerequisite structure of the learning domain (''knowledge mapping'').
Towards Modeling Learner Performance with Large Language Models
Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token sequences representing a wide array of tasks, including time-series prediction and robot control.
Prerequisite Structure Discovery in Intelligent Tutoring Systems
sino7/ks-discovery-for-its
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This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
jjcui96/rckt
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Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
devilyangs/enas-kt
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NeurIPS 2023
Knowledge tracing (KT) aims to trace students' knowledge states by predicting whether students answer correctly on exercises.
Forgetting-aware Linear Bias for Attentive Knowledge Tracing
skewondr/FoLiBi
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This problem arises from the bias that overprioritizes the correlation of questions while inadvertently ignoring the impact of forgetting behavior.
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
zmy-9/cmvf
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The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence.
Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing
lucky7-code/core
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Existing models tend to memorize the answer bias as a shortcut for achieving high prediction performance in KT, thereby failing to fully understand students' knowledge states.
Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
zmy-9/cmkt
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Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery.
