| 2026 | AIED | Using LLMs for Knowledge Component-Level Correctness Labeling in Open-Ended Coding Problems. | Zhangqi Duan, Arnav Kankaria, Dhruv Kartik, Andrew S. Lan |
| 2026 | AIED | A Multi-agent Approach to Validate and Refine LLM-Generated Personalized Math Problems. | Fareya Ikram, Nischal Ashok Kumar, Junyang Lu, Hunter McNichols, Candace A. Walkington, Neil T. Heffernan, Andrew S. Lan |
| 2025 | AIED | Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs. | Wanyong Feng, Peter Tran, Stephen Sireci, Andrew S. Lan |
| 2025 | AIED | Learning Code-Edit Embeddings to Model Student Debugging Behavior. | Hasnain Heickal, Andrew S. Lan |
| 2025 | AIED | Training LLM-Based Tutors to Improve Student Learning Outcomes in Dialogues. | Alexander Scarlatos, Naiming Liu, Jaewook Lee, Richard G. Baraniuk, Andrew S. Lan |
| 2025 | AIED | The Efficiency of Teacher-Driven Context Personalization in Mathematics with Large Language Models. | Candace A. Walkington, Theodora Beauchamp, Andrew S. Lan, Tiffini Pruitt-Britton |
| 2025 | EDM | 9th Educational Data Mining in Computer Science Education (CSEDM) Workshop. | Bita Akram, Yang Shi, Peter Brusilovsky, Thomas W. Price, Ken Koedinger, Paulo Carvalho, Shan Zhang, Andrew S. Lan, Juho Leinonen |
| 2025 | EMNLP | SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction. | Alexander Scarlatos, Nigel Fernandez, Christopher Ormerod, Susan Lottridge, Andrew S. Lan |
| 2025 | LAK | Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks. | Zhangqi Duan, Nigel Fernandez, Alexander Hicks, Andrew S. Lan |
| 2025 | LAK | Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs. | Alexander Scarlatos, Ryan Shaun Baker, Andrew S. Lan |
| 2024 | ACL | SyllabusQA: A Course Logistics Question Answering Dataset. | Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan |
| 2024 | AIED | Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. | Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan |
| 2024 | AIED | Automatic Short Answer Grading in College Mathematics Using In-Context Meta-learning: An Evaluation of the Transferability of Findings. | Michael Smalenberger, Elham Sohrabi, Mengxue Zhang, Sami Baral, Kelly Smalenberger, Andrew S. Lan, Neil T. Heffernan |
| 2024 | EDM | 8th Educational Data Mining in Computer Science Education (CSEDM) Workshop. | Yang Shi, Peter Brusilovsky, Bita Akram, Thomas W. Price, Juho Leinonen, Kenneth R. Koedinger, Andrew S. Lan |
| 2024 | EDM | Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. | Jaewook Lee, Digory Smith, Simon Woodhead, Andrew S. Lan |
| 2024 | EDM | Interpreting Latent Student Knowledge Representations in Programming Assignments. | Nigel Fernandez, Andrew S. Lan |
| 2024 | EDM | Leveraging Large Language Models for Next-Generation Educational Technologies. | Neil T. Heffernan, Rose E. Wang, Christopher MacLellan, Arto Hellas, Chenglu Li, Candace A. Walkington, Joshua Littenberg-Tobias, David Joyner, Steven Moore, Adish Singla, Zach A. Pardos, Maciej Pankiewicz, Juho Kim, Shashank Sonkar, Clayton Cohn, Anthony Botelho, Andrew S. Lan, Lan Jiang, Mingyu Feng, Tanja Kser, Eamon Worden |
| 2024 | EDM | Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models. | Hasnain Heickal, Andrew S. Lan |
| 2024 | EDM | Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? | Hunter McNichols, Jaewook Lee, Stephen Fancsali, Steven Ritter, Andrew S. Lan |
| 2024 | EDM | From Reaction to Anticipation: Predicting Future Affect. | Andres Felipe Zambrano, Ryan S. Baker, Sami Baral, Neil T. Heffernan, Andrew S. Lan |
| 2024 | EMNLP | Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank. | Jaewook Lee, Hunter McNichols, Andrew S. Lan |
| 2024 | EMNLP | DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. | Nigel Fernandez, Alexander Scarlatos, Wanyong Feng, Simon Woodhead, Andrew S. Lan |
| 2024 | NAACL | Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models. | Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Otero Ornelas, Andrew S. Lan |
| 2023 | AAAI | DiFA: Differentiable Feature Acquisition. | Aritra Ghosh, Andrew S. Lan |
| 2023 | ACII | Active Learning for a Classroom Observer who Can't Time Travel. | Andres Felipe Zambrano, Ryan S. Baker, Andrew S. Lan |
| 2023 | ACL | Tree-Based Representation and Generation of Natural and Mathematical Language. | Alexander Scarlatos, Andrew S. Lan |
| 2023 | ACL | Interpretable Math Word Problem Solution Generation via Step-by-step Planning. | Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan |
| 2023 | AIED | SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. | Jaewook Lee, Andrew S. Lan |
| 2023 | AIED | Balancing Test Accuracy and Security in Computerized Adaptive Testing. | Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan |
| 2023 | AIED | Algebra Error Classification with Large Language Models. | Hunter McNichols, Mengxue Zhang, Andrew S. Lan |
| 2023 | AIED | Intelligent Textbooks: The Fifth International Workshop. | Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan |
| 2023 | EDM | A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. | Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew S. Lan |
| 2023 | EDM | Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. | Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan |
| 2022 | AAAI | DiPS: Differentiable Policy for Sketching in Recommender Systems. | Aritra Ghosh, Saayan Mitra, Andrew S. Lan |
| 2022 | AIED | Automated Scoring for Reading Comprehension via In-context BERT Tuning. | Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benot Choffin, Richard G. Baraniuk, Andrew S. Lan |
| 2022 | AIED | Intelligent Textbooks: Themes and Topics. | Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan |
| 2022 | CIKM | Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. | Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton |
| 2022 | EDM | Process-BERT: A Framework for Representation Learning on Educational Process Data. | Alexander Scarlatos, Christopher Brinton, Andrew S. Lan |
| 2022 | EDM | Automatic Short Math Answer Grading via In-context Meta-learning. | Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan |
| 2022 | EMNLP | Open-ended Knowledge Tracing for Computer Science Education. | Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan |
| 2021 | AIED | Mathematical Formula Representation via Tree Embeddings. | Zichao Wang, Andrew S. Lan, Richard G. Baraniuk |
| 2021 | AIED | Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. | Aritra Ghosh, Jay Raspat, Andrew S. Lan |
| 2021 | CVPR | Contrastive Learning Improves Model Robustness Under Label Noise. | Aritra Ghosh, Andrew S. Lan |
| 2021 | EDM | Math Operation Embeddings for Open-ended Solution Analysis and Feedback. | Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan |
| 2021 | EMNLP | Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. | Zichao Wang, Andrew S. Lan, Richard G. Baraniuk |
| 2021 | IJCAI | BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. | Aritra Ghosh, Andrew S. Lan |
| 2021 | LAK | Using Past Data to Warm Start Active Machine Learning: Does Context Matter? | Shamya Karumbaiah, Andrew S. Lan, Sachit Nagpal, Ryan S. Baker, Anthony Botelho, Neil T. Heffernan |
| 2021 | LAK | Linguistic Skill Modeling for Second Language Acquisition. | Brian Zylich, Andrew S. Lan |
| 2021 | WACV | Do We Really Need Gold Samples for Sample Weighting under Label Noise? | Aritra Ghosh, Andrew S. Lan |
| 2020 | AIED | Exploring Automated Question Answering Methods for Teaching Assistance. | Brian Zylich, Adam Viola, Brokk Toggerson, Lara Al-Hariri, Andrew S. Lan |
| 2020 | CISS | MSE-Optimal Neural Network Initialization via Layer Fusion. | Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer |
| 2020 | EDM | VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. | Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk |
| 2020 | EDM | qDKT: Question-centric Deep Knowledge Tracing. | Shashank Sonkar, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk |
| 2020 | EMNLP | Robust and Interpretable Grounding of Spatial References with Relation Networks. | Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan |
| 2020 | ICDM | Learning Student Interest Trajectory for MOOC Thread Recommendation. | Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava |
| 2020 | KDD | Context-Aware Attentive Knowledge Tracing. | Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan |
| 2019 | DSAA | Grade Prediction with Neural Collaborative Filtering. | Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala |
| 2019 | EDM | Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. | Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala |
| 2019 | EDM | A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading. | Zichao Wang, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk |
| 2019 | EDM | Active Learning for Student Affect Detection. | Tsung-Yen Yang, Ryan S. Baker, Christoph Studer, Neil T. Heffernan, Andrew S. Lan |
| 2019 | HCI | Adaptive Remediation with Multi-modal Content. | Yuwei Tu, Christopher G. Brinton, Andrew S. Lan, Mung Chiang |
| 2019 | ICDCS | Predicting the Timing and Quality of Responses in Online Discussion Forums. | Patrick Hansen, Richard Junior Bustamante, Tsung-Yen Yang, Elizabeth Tenorio, Christopher G. Brinton, Mung Chiang, Andrew S. Lan |
| 2019 | INFOCOM | Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning. | Parinaz Naghizadeh, Maria Gorlatova, Andrew S. Lan, Mung Chiang |
| 2018 | AIED | Learner Behavioral Feature Refinement and Augmentation Using GANs. | Da Cao, Andrew S. Lan, Weiyu Chen, Christopher G. Brinton, Mung Chiang |
| 2018 | CISS | PhaseLin: Linear phase retrieval. | Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer |
| 2018 | CISS | Linearized binary regression. | Andrew S. Lan, Mung Chiang, Christoph Studer |
| 2018 | EDM | Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. | Weiyu Chen, Andrew S. Lan, Da Cao, Christopher G. Brinton, Mung Chiang |
| 2018 | EDM | Textbook annotations as an early predictor of student learning. | Adam Winchell, Michael Mozer, Andrew S. Lan, Phillip Grimaldi, Harold Pashler |
| 2018 | ICASSP | Insense: Incoherent Sensor Selection for Sparse Signals. | Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk |
| 2018 | ICML | Linear Spectral Estimators and an Application to Phase Retrieval. | Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer |
| 2018 | ICML | An Estimation and Analysis Framework for the Rasch Model. | Andrew S. Lan, Mung Chiang, Christoph Studer |
| 2018 | INFOCOM | Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. | Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang |
| 2017 | EDM | Behavior-Based Latent Variable Model for Learner Engagement. | Andrew S. Lan, Christopher G. Brinton, Tsung-Yen Yang, Mung Chiang |
| 2017 | EDM | Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks. | Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk |
| 2017 | EDM | Data-Mining Textual Responses to Uncover Misconception Patterns. | Joshua J. Michalenko, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk |
| 2017 | EDM | A Latent Factor Model For Instructor Content Preference Analysis. | Jack Z. Wang, Andrew S. Lan, Phillip Grimaldi, Richard G. Baraniuk |
| 2017 | EDM | Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning. | Andrew E. Waters, Phillip Grimaldi, Andrew S. Lan, Richard G. Baraniuk |
| 2017 | ICASSP | Contextual multi-armed bandit algorithms for personalized learning action selection. | Indu Manickam, Andrew S. Lan, Richard G. Baraniuk |
| 2017 | IJCAI | RHash: Robust Hashing via L_infinity-norm Distortion. | Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk |
| 2016 | EDM | A Contextual Bandits Framework for Personalized Learning Action Selection. | Andrew S. Lan, Richard G. Baraniuk |
| 2016 | ICML | Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. | Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer |
| 2014 | EDM | Quantized Matrix Completion for Personalized Learning. | Andrew S. Lan, Christoph Studer, Richard G. Baraniuk |
| 2014 | ICASSP | Matrix recovery from quantized and corrupted measurements. | Andrew S. Lan, Christoph Studer, Richard G. Baraniuk |
| 2014 | KDD | Time-varying learning and content analytics via sparse factor analysis. | Andrew S. Lan, Christoph Studer, Richard G. Baraniuk |
| 2013 | EDM | Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics. | Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk |
| 2013 | EDM | Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data. | Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk |
| 2013 | EDM | Test-size Reduction for Concept Estimation. | Divyanshu Vats, Christoph Studer, Andrew S. Lan, Lawrence Carin, Richard G. Baraniuk |
| 2013 | ICASSP | Sparse probit factor analysis for learning analytics. | Andrew E. Waters, Andrew S. Lan, Christoph Studer |