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Knowledge Tracing
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/persai-lab/2025-aied-kmap
공식 코드Personalized Student Knowledge Modeling for Future Learning Resource Prediction
https://github.com/persai-lab/2025-contrastivemultitaskktbm
공식 코드Personalized Student Knowledge Modeling for Future Learning Resource Prediction
https://github.com/persai-lab/bigdata2022-tamkot
공식 코드https://github.com/persai-lab/umap2021-grate
공식 코드Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization
https://github.com/pokerme7777/hitskt
공식 코드HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing
https://github.com/pykt-team/pykt-toolkit
공식 코드simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
https://github.com/rattlesnakey/lorekt
공식 코드https://github.com/rattlesnakey/q-mckt
공식 코드https://github.com/researcher-tiger/moepg
공식 코드Reinforcement Learning Guided Multi-Objective Exam Paper Generation
https://github.com/riiid/ednet
공식 코드https://github.com/rschmucker/large-scale-knowledge-tracing
공식 코드Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy
https://github.com/rui9812/knowtrace
공식 코드KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
https://github.com/sahanbull/TrueLearn
공식 코드TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
https://github.com/shalini1194/RKT
공식 코드https://github.com/simon-tan/DKT-DSC
공식 코드Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
https://github.com/simon-tan/ikt
공식 코드Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
https://github.com/sino7/ks-discovery-for-its
공식 코드Prerequisite Structure Discovery in Intelligent Tutoring Systems
https://github.com/skewondr/FoLiBi
공식 코드Forgetting-aware Linear Bias for Attentive Knowledge Tracing
https://github.com/soonwook34/crkt
공식 코드Enhancing Knowledge Tracing with Concept Map and Response Disentanglement
https://github.com/spneshaei/towards-modeling-learner-performance-with-llms
공식 코드Towards Modeling Learner Performance with Large Language Models
https://github.com/tbs17/MathBERT
공식 코드MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
https://github.com/thu-keg/mooc-radar
공식 코드https://github.com/tubehao/sinkt
공식 코드SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
https://github.com/ucsb-nlp-chang/kt2
공식 코드A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings
https://github.com/umass-ml4ed/dialogue-kt
공식 코드Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
https://github.com/umass-ml4ed/kcgen-kt
공식 코드Automated Knowledge Component Generation and Knowledge Tracing for Coding Problems
https://github.com/umass-ml4ed/neurips-challenge-22
공식 코드A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
https://github.com/umass-ml4ed/tiktoc
공식 코드Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks
https://github.com/uncertaintyforknowledgetracing/ukt
공식 코드https://github.com/unknownben/qakt
공식 코드https://github.com/xiaopengguo/atkt
공식 코드https://github.com/yangazure/code-dkt
공식 코드Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
https://github.com/yoonseok312/gram
공식 코드GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering
https://github.com/yumo216/dgekt
공식 코드DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing
https://github.com/zmy-9/cmkt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.