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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/CAHLR/pyBKT
공식 코드pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models
https://github.com/Knewton/edm2016
공식 코드Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation
https://github.com/Rimoku/GIKT
공식 코드https://github.com/Soptq/LANA-pytorch
공식 코드LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
https://github.com/alxzzhou/aakt
공식 코드AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
https://github.com/arghosh/AKT
공식 코드https://github.com/arghosh/OptionTracing
공식 코드Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
https://github.com/b-castleman/hierarchical-mab-tutoring
공식 코드https://github.com/badranX/KTbench
공식 코드https://github.com/bigdata-ustc/EduKTM
공식 코드A Survey of Knowledge Tracing: Models, Variants, and Applications
https://github.com/chrispiech/DeepKnowledgeTracing
공식 코드https://github.com/ckyeungac/ADM2017
공식 코드https://github.com/ckyeungac/deep-knowledge-tracing-plus
공식 코드Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization
https://github.com/codebase-2025/dkt2
공식 코드Revisiting Applicable and Comprehensive Knowledge Tracing in Large-Scale Data
https://github.com/codingchild2424/MonaCoBERT
공식 코드MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
https://github.com/destiny123456qwer/ngfkt
공식 코드https://github.com/devilyangs/enas-kt
공식 코드Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
https://github.com/dmic-lab-hfut/dagkt
공식 코드DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
https://github.com/donimoon/llmkt
공식 코드https://github.com/dqyzhwk/transkt
공식 코드Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
https://github.com/iwangjian/Coding-Tutor
공식 코드https://github.com/jilljenn/ktm
공식 코드https://github.com/jjcui96/grkt
공식 코드https://github.com/jjcui96/rckt
공식 코드Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
https://github.com/leavesli1015/explainable-few-shot-knowledge-tracing
공식 코드https://github.com/liangzhang2017/3dgai
공식 코드Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
https://github.com/lucky7-code/core
공식 코드https://github.com/lucy66666/okt
공식 코드https://github.com/lyf-1/PEBG
공식 코드Improving Knowledge Tracing via Pre-training Question Embeddings
https://github.com/meghabyte/acl2021-education
공식 코드https://github.com/mlcolab/psi-kt
공식 코드Predictive, scalable and interpretable knowledge tracing on structured domains
https://github.com/nkondapa/visualknowledgetracing
공식 코드https://github.com/nlpcui/adaptiveqg
공식 코드Adaptive and Personalized Exercise Generation for Online Language Learning
https://github.com/oezyurty/exrec
공식 코드Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
https://github.com/oezyurty/kcqrl
공식 코드Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.