Knowledge Tracing
69 papers with code • 2 benchmarks • 2 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
No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
zmy-9/sfkt
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Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors.
Adaptive and Personalized Exercise Generation for Online Language Learning
We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises.
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
umass-ml4ed/neurips-challenge-22
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In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i. e., finding the underlying causal relationship among different skills from real-world student response data.
Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing
In this work, a novel knowledge tracing model, named Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of the subjective labeling by calibrating the skill relation matrix and the Q-matrix and apply the Graph Convolutional Network(GCN) to model the heterogeneous interactions between students, exercises, and skills.
Attentive Q-Matrix Learning for Knowledge Tracing
unknownben/qakt
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This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms.
MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education.
Reinforcement Learning Guided Multi-Objective Exam Paper Generation
To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria.
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i. e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}.
simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems.
Transition-Aware Multi-Activity Knowledge Tracing
persai-lab/bigdata2022-tamkot
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TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities.
