- [Arxiv 2024, DisKCD] Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge
- [Arxiv 2024, ASG-CD] Improving Cognitive Diagnosis Models with Adaptive Relational Graph Neural Networks
- [WWW 2024, UCD] Unified Uncertainty Estimation for Cognitive Diagnosis Models
- [WWW 2024, ID-CDF] Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
- [WWW 2024, ICDM] Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
- [AAAI 2024, ACD] Boosting Neural Cognitive Diagnosis with Student’s Affective State Modeling
- [AAAI 2024, CMES] Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach
- [AAAI 2024, DZCD] Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives
- [TKDE 2024, RGDT] RDGT: Enhancing Group Cognitive Diagnosis with Relation-Guided Dual-Side Graph Transformer
- [ECAI 2023, QCCDM] QCCDM: A Q-Augmented Causal Cognitive Diagnosis Model for Student Learning
- [SIGIR 2023, TechCD] Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis
- [ICONIP 2023, CBICDM] A Causality-Based Interpretable Cognitive Diagnosis Model
- [IJCAI 2023, EIRS] Exploiting Non-Interactive Exercises in Cognitive Diagnosis
- [AAAI 2023, BETA-CD] BETA-CD: A Bayesian Meta-Learned Cognitive Diagnosis Framework for Personalized Learning
- [AAAI 2023, SCD] Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
- [ESWA 2023, GLNC] Global and Local Neural Cognitive Modeling for Student Performance Prediction
- [CIKM 2023, DAISim] Simulating Student Interactions with Two-stage Imitation Learning for Intelligent Educational Systems
- [CIKM 2023, HomoGCD] Homogeneous Cohort-Aware Group Cognitive Diagnosis: A Multi-grained Modeling Perspective
- [NeurIPS 2023, DCD] Disentangling Cognitive Diagnosis with Limited Exercise Labels
- [NeurIPS 2023, FairLISA] FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
- [SCIS 2023, FairCD] Understanding and Improving Fairness in Cognitive Diagnosis
- [TKDE 2023, MvCRF] Multivariate Cognitive Response Framework for Student Performance Prediction on MOOC
- [TLT 2023, DNeuralCDM] Dynamic Cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective
- [CIKM 2022, CDMFKC] Cognitive Diagnosis Focusing on Knowledge Concepts
- [CIKM 2022, KSCD] Knowledge-Sensed Cognitive Diagnosis for Intelligent Education Platforms
- [ESWA 2022, ICD] ICD: A new interpretable cognitive diagnosis model for intelligent tutor systems
- [TKDE 2022, KaNCD] Neuralcd: A general framework for cognitive diagnosis
- [TKDE 2022, CNCD-F] Neuralcd: A general framework for cognitive diagnosis
- [KDD 2022, HierCDF] HierCDF: A Bayesian Network-based Hierarchical Cognitive Diagnosis Framework
- [KDD 2022, ICD] Incremental Cognitive Diagnosis for Intelligent Education
- [IJCAI 2021, IRR] Item Response Ranking for Cognitive Diagnosis
- [SIGIR 2021, RCD] RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems
- [ICDM 2021, MGCD] Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective
- [KDD 2021, ECD] Modeling Context-aware Features for Cognitive Diagnosis in Student Learning
- [CIKM 2021, CDGK] Using Knowledge Concept Aggregation towards Accurate Cognitive Diagnosis
- [AAAI 2020, NCDM+(CNCD-Q)] Neural cognitive diagnosis for intelligent education systems
- [AAAI 2020, NCDM] Neural cognitive diagnosis for intelligent education systems
- [CIKM 2019, DIRT] DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis
- [TIST 2018, FuzzyCDF] Fuzzy cognitive diagnosis for modeling examinee performance
- [JEBS 2009, DINA] Dina model and parameter estimation: A didactic
- Multidimensional item response theory models, MIRT
- Item response theory, IRT
- [Arxiv 2024] A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
- [Arxiv 2024] A Survey of Explainable Knowledge Tracing
- [ACM 2023] Knowledge Tracing: A Survey
- [KBS 2022] A survey on deep learning based knowledge tracing
- [Arxiv 2021] A Survey of Knowledge Tracing
- [Arxiv 2024, Mamba4KT] Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model
- [Arxiv 2024, T-DEKT] Dual-State Personalized Knowledge Tracing with Emotional Incorporation
- [Arxiv 2024] Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning
- [Arxiv 2024, LoReKT] Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
- [MM 2024, PSKT] Remembering is Not Applying: Interpretable Knowledge Tracing for Problem-solving Processes
- [TOIS 2024, FDKT] FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning
- [SCIS 2024, CPF] Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
- [SDM 2024, GSKPM] Graph-based Student Knowledge Profile for Online Intelligent Education
- [TNNLS 2024, HyperKT] Dual-Channel Adaptive Scale Hypergraph Encoders With Cross-View Contrastive Learning for Knowledge Tracing
- [TLT 2024, CoSKT] CoSKT: A Collaborative Self-supervised Learning Method for Knowledge Tracing
- [TLT 2024, SYNSAINT] Enhanced Deep Knowledge Tracing via Synthetic Embeddings
- [WWW 2024, HD-KT] HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection
- [WWW 2024, MIKT] Interpretable Knowledge Tracing with Multiscale State Representation
- [WWW 2024, QDCKT] Question Difficulty Consistent Knowledge Tracing
- [LAK 2024] Investigating Algorithmic Bias on Bayesian Knowledge Tracing and Carelessness Detectors
- [IEEE/CAA 2023, GraphCA] GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing
- [NeurIPS 2023, ENAS-KT] Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing
- [KDD 2023, LBKT] Learning Behavior-oriented Knowledge Tracing
- [WWW 2023, MAKT] Integrating fine-grained attention into multi-task learning for knowledge tracing
- [WWW 2023, DTransformer] Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer
- [ICLR 2023, SimpleKT] simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing
- [TOIS 2023, MRT-KT] Fine-Grained Interaction Modeling with Multi- nnjn Relational Transformer for Knowledge Tracing
- [TOIS 2023, DLKT] MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge Tracing
- [TKDD 2023, BiCo] Pre-training uestion Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive Learning
- [Arxiv 2023, CORE] Do We Fully Understand Students’ Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing
- [Arxiv 2023, RCKT] Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
- [AHPCAI 2023, MA-DKT] A deep knowledge tracing model based on multi-head self-attention mechanism
- [TLT 2023, MVGKT] Multi-Variate Knowledge Tracking based on Graph Neural Network in ASSISTments
- [TLT 2023, OPKT] OPKT: Enhancing Knowledge Tracing with Optimized Pre-training Mechanisms in Intelligent Tutoring
- [TLT 2023, AAKT] AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
- [MM 2023, ABQR] Adversarial Bootstrapped Question Representation Learning for Knowledge Tracing
- [ESWA 2023, QRCL] Question-response Representation with Dual-level Contrastive Learning for Improving Knowledge Tracing
- [ESWA 2023, DKTMR] Towards More Accurate and Interpretable Model: Fusing Multiple Knowledge Relations into Deep Knowledge Tracing
- [ESWA 2023, TSKT] Heterogeneous graph-based knowledge tracing with spatiotemporal evolution
- [ESWA 2023, FKT] Response speed enhanced fine-grained knowledge tracing: A multi-task learning perspective
- [ICDM 2023, CMVF] Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
- [CIKM 2023, CMKT] Counterfactual Monotonic Knowledge Tracing for Assessing Students’ Dynamic Mastery of Knowledge Concepts
- [CIKM 2023, CPKT] Continuous Personalized Knowledge Tracing: Modeling Long-Term Learning in Online Environments
- [CIKM 2023, FoLiBi] Forgetting-aware Linear Bias for Attentive Knowledge Tracing
- [CIKM 2023, SFKT] No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
- [AAAI 2023, DAKTN] Deep Attentive Model for Knowledge Tracing
- [AAAI 2023, QIKT] Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations
- [EDM 2022, AGKT] Automatical Graph-based Knowledge Tracing
- [TLT 2022, CMKT] CMKT: Concept map driven knowledge tracing
- [TKDE 2022, DGMN] Deep graph memory networks for forgetting-robust knowledge tracing
- [TKDE 2022, LPKT-S] Monitoring Student Progress for Learning Process-Consistent Knowledge Tracing
- [IJCAI 2022, CT-NCM] Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware Neural Network Approach
- [CIKM 2022, LFBKT] Knowledge Tracing Model with Learning and Forgetting Behavior
- [SIGIR 2022, HGKT] Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing
- [SIGIR 2022, DIMKT] Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect
- [WWW 2022, CL4KT] Contrastive learning for knowledge tracing
- [WSDM 2022, CoKT] Improving Knowledge Tracing with Collaborative Information
- [WSDM 2022, AdaptKT] AdaptKT: A domain adaptable method for knowledge tracing
- [AAAI 2022, IKT] Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations
- [KDD 2021, LPKT] Learning Process-consistent Knowledge Tracing
- [ACM MM 2021, ATKT] Enhancing Knowledge Tracing via Adversarial Training
- [CIKM 2021, MF_DAKT] Multi-Factors Aware Dual-Attentional Knowledge Tracing
- [LAK 2021, SAINT+] SAINT+: Integrating Temporal Features for EdNet Correctness Prediction
- [KBS 2021, TC-MIRT] Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing
- [WSDM 2021, HawkesKT] Temporal cross-effects in knowledge tracing
- [WSDM 2021, FDKT] Federated Deep Knowledge Tracing
- [SIGIR 2021, IEKT] Tracing Knowledge State with Individual Cognition and Acquisition Estimation
- [IJCAI 2020, PEBG] Improving Knowledge Tracing via Pre-training Question Embeddings
- [ECML PKDD 2020, GIKT] GIKT: A Graph-Based Interaction Model for Knowledge Tracing
- [ICDM 2020, SKT] Structure-based Knowledge Tracing: An Influence Propagation View
- [L@S 2020, SAINT] Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
- [CIKM 2020, RKT] RKT: Relation-Aware Self-Attention for Knowledge Tracing
- [SIGIR 2020, CKT] Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process
- [KDD 2020, AKT] Context-Aware Attentive Knowledge Tracing
- [Applied Intelligence 2020, KTM-DLF] Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing
- [EDM 2020, qDKT] qDKT: Question-centric Deep Knowledge Tracing
- [TKDE 2019, EKT] EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction
- [WI 2019, GKT] Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network
- [SIGIR 2019, SKVMN] Knowledge Tracing with Sequential Key-Value Memory Networks
- [WWW 2019, DKTForget] Augmenting Knowledge Tracing by Considering Forgetting Behavior
- [LAK 2019, KQN] Knowledge query network for knowledge tracing: How knowledge interacts with skills
- [EDM 2019, SAKT] A self-attentive model for knowledge tracing
- [EDM 2019, Deep-IRT] Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory
- [AAAI 2019, KTM] Knowledge tracing machines: Factorization machines for knowledge tracing
- [AAAI 2018, EERNN] Exercise-enhanced sequential modeling for student performance prediction
- [ICDM 2018, P-DKTC] Prerequisite-Driven Deep Knowledge Tracing
- [ICDM 2018, DKT_DSC] Deep knowledge tracing and dynamic student classification for knowledge tracing
- [L@S 2018, DKT+] Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization
- [CIKM 2017, KPT] Tracking knowledge proficiency of students with educational priors
- [WWW 2017, DKVMN] Dynamic Key-Value Memory Networks for Knowledge Tracing
- [TLT 2017, DBKT/DBN] Dynamic Bayesian Networks for Student Modeling
- [EDM 2016, HIRT, TIRT] Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation
- [NIPS 2015, DKT] Deep knowledge tracing
- [2009, PFA] Performance Factors Analysis --A New Alternative to Knowledge Tracing
- [ITS 2008, AFM] Comparing two IRT models for conjunctive skills
- [ICITS 2006, LFA] Learning Factors Analysis – A General Method for Cognitive Model Evaluation and Improvement
- [UMUAI 1994, BKT] Bayesian knowledge tracing