Andrew S. Lan

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 117

Top Venues

Publications

Michael Smalenberger, Elham Sohrabi, Mengxue Zhang, Sami Baral, Kelly Smalenberger, Andrew S. Lan, Neil T. Heffernan
Automatic Short Answer Grading in College Mathematics Using In-Context Meta-learning: An Evaluation of the Transferability of Findings. AIED Companion (1) (2024)
Nigel Fernandez, Alexander Scarlatos, Simon Woodhead, Andrew S. Lan
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. CoRR (2024)
Nigel Fernandez, Andrew S. Lan
Interpreting Latent Student Knowledge Representations in Programming Assignments. CoRR (2024)
Jaewook Lee, Digory Smith, Simon Woodhead, Andrew S. Lan
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. CoRR (2024)
Nischal Ashok Kumar, Andrew S. Lan
Improving Socratic Question Generation using Data Augmentation and Preference Optimization. CoRR (2024)
Alexander Scarlatos, Wanyong Feng, Digory Smith, Simon Woodhead, Andrew S. Lan
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank. CoRR (2024)
Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. CoRR (2024)
Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. AIED (1) (2024)
Nischal Ashok Kumar, Andrew S. Lan
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education. CoRR (2024)
Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan
SyllabusQA: A Course Logistics Question Answering Dataset. ACL (1) (2024)
Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Otero Ornelas, Andrew S. Lan
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models. CoRR (2024)
Hunter McNichols, Jaewook Lee, Stephen Fancsali, Steven Ritter, Andrew S. Lan
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? CoRR (2024)
Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan
SyllabusQA: A Course Logistics Question Answering Dataset. CoRR (2024)
Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew S. Lan
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. EDM (2023)
Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan
Intelligent Textbooks: The Fifth International Workshop. AIED (Posters/Late Breaking Results/...) (2023)
Hunter McNichols, Mengxue Zhang, Andrew S. Lan
Algebra Error Classification with Large Language Models. CoRR (2023)
Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning. CoRR (2023)
Alexander Scarlatos, Andrew S. Lan
Tree-Based Representation and Generation of Natural and Mathematical Language. CoRR (2023)
Alexander Scarlatos, Andrew S. Lan
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning. CoRR (2023)
Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew S. Lan
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. CoRR (2023)
Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan
Balancing Test Accuracy and Security in Computerized Adaptive Testing. AIED (2023)
Aritra Ghosh, Andrew S. Lan
DiFA: Differentiable Feature Acquisition. AAAI (2023)
Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew S. Lan
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. BEA@ACL (2023)
Alexander Scarlatos, Andrew S. Lan
Tree-Based Representation and Generation of Natural and Mathematical Language. ACL (1) (2023)
Andres Felipe Zambrano, Ryan S. Baker, Andrew S. Lan
Active Learning for a Classroom Observer who Can't Time Travel. ACIIW (2023)
Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan
Balancing Test Accuracy and Security in Computerized Adaptive Testing. CoRR (2023)
Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. EDM (2023)
Aritra Ghosh, Saayan Mitra, Andrew S. Lan
DiPS: Differentiable Policy for Sketching in Recommender Systems. AAAI (2022)
Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan
Intelligent Textbooks: Themes and Topics. AIED (2) (2022)
Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan
Automated Scoring for Reading Comprehension via In-context BERT Tuning. AIED (1) (2022)
Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan
Open-Ended Knowledge Tracing. CoRR (2022)
Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan
Open-ended Knowledge Tracing for Computer Science Education. EMNLP (2022)
Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan
Automated Scoring for Reading Comprehension via In-context BERT Tuning. CoRR (2022)
Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan
Automatic Short Math Answer Grading via In-context Meta-learning. EDM (2022)
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. CoRR (2022)
Alexander Scarlatos, Christopher G. Brinton, Andrew S. Lan
Process-BERT: A Framework for Representation Learning on Educational Process Data. CoRR (2022)
Alexander Scarlatos, Christopher Brinton, Andrew S. Lan
Process-BERT: A Framework for Representation Learning on Educational Process Data. EDM (2022)
Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan
Automatic Short Math Answer Grading via In-context Meta-learning. CoRR (2022)
Zichao Wang, Andrew S. Lan, Richard G. Baraniuk
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. EMNLP (1) (2021)
Aritra Ghosh, Andrew S. Lan
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? CoRR (2021)
Zichao Wang, Mengxue Zhang, Richard G. Baraniuk, Andrew S. Lan
Scientific Formula Retrieval via Tree Embeddings. IEEE BigData (2021)
Aritra Ghosh, Andrew S. Lan
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. CoRR (2021)
Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava
Learning Student Interest Trajectory for MOOCThread Recommendation. CoRR (2021)
Aritra Ghosh, Andrew S. Lan
Contrastive Learning Improves Model Robustness Under Label Noise. CVPR Workshops (2021)
Yun-Wei Chu, Elizabeth Tenorio, Laura Melissa Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. CoRR (2021)
Zichao Wang, Andrew S. Lan, Richard G. Baraniuk
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. CoRR (2021)
Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. EDM (2021)
Aritra Ghosh, Andrew S. Lan
Do We Really Need Gold Samples for Sample Weighting under Label Noise? WACV (2021)
Yun-Wei Chu, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. IEEE BigData (2021)
Aritra Ghosh, Andrew S. Lan
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. IJCAI (2021)
Aritra Ghosh, Jay Raspat, Andrew S. Lan
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. CoRR (2021)
Zichao Wang, Andrew S. Lan, Richard G. Baraniuk
Mathematical Formula Representation via Tree Embeddings. iTextbooks@AIED (2021)
Aritra Ghosh, Andrew S. Lan
Contrastive Learning Improves Model Robustness Under Label Noise. CoRR (2021)
Guohao Lan, Mohammadreza F. Imani, Zida Liu, José Manjarrés, Wenjun Hu, Andrew S. Lan, David R. Smith, Maria Gorlatova
MetaSense: Boosting RF Sensing Accuracy Using Dynamic Metasurface Antenna. IEEE Internet Things J. 8 (18) (2021)
Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar
Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Process. Mag. 38 (3) (2021)
Shamya Karumbaiah, Andrew S. Lan, Sachit Nagpal, Ryan S. Baker, Anthony Botelho, Neil T. Heffernan
Using Past Data to Warm Start Active Machine Learning: Does Context Matter? LAK (2021)
Aritra Ghosh, Saayan Mitra, Andrew S. Lan
DiPS: Differentiable Policy for Sketching in Recommender Systems. CoRR (2021)
Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar
Personalized Education in the AI Era: What to Expect Next? CoRR (2021)
Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. CoRR (2021)
Brian Zylich, Andrew S. Lan
Linguistic Skill Modeling for Second Language Acquisition. LAK (2021)
Aritra Ghosh, Jay Raspat, Andrew S. Lan
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. AIED (1) (2021)
Adam Winchell, Andrew S. Lan, Michael Mozer
Highlights as an Early Predictor of Student Comprehension and Interests. Cogn. Sci. 44 (11) (2020)
Aritra Ghosh, Beverly Park Woolf, Shlomo Zilberstein, Andrew S. Lan
Skill-based Career Path Modeling and Recommendation. IEEE BigData (2020)
Yupei Zhang, Huan Dai, Yue Yun, Shuhui Liu, Andrew S. Lan, Xuequn Shang
Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl. Based Syst. 205 (2020)
Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. EDM (2020)
Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk
qDKT: Question-centric Deep Knowledge Tracing. CoRR (2020)
Brian Zylich, Adam Viola, Brokk Toggerson, Lara Al-Hariri, Andrew S. Lan
Exploring Automated Question Answering Methods for Teaching Assistance. AIED (1) (2020)
Jack Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. CoRR (2020)
Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan
Robust and Interpretable Grounding of Spatial References with Relation Networks. EMNLP (Findings) (2020)
Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan
Context-Aware Attentive Knowledge Tracing. KDD (2020)
Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan
Context-Aware Attentive Knowledge Tracing. CoRR (2020)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
MSE-Optimal Neural Network Initialization via Layer Fusion. CoRR (2020)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
MSE-Optimal Neural Network Initialization via Layer Fusion. CISS (2020)
Shashank Sonkar, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk
qDKT: Question-centric Deep Knowledge Tracing. EDM (2020)
Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava
Learning Student Interest Trajectory for MOOC Thread Recommendation. ICDM (Workshops) (2020)
Yuwei Tu, Christopher G. Brinton, Andrew S. Lan, Mung Chiang
Adaptive Remediation with Multi-modal Content. HCI (32) (2019)
Parinaz Naghizadeh, Maria Gorlatova, Andrew S. Lan, Mung Chiang
Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning. INFOCOM (2019)
Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. EDM (2019)
Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election. CoRR (2019)
Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala
Grade Prediction with Neural Collaborative Filtering. DSAA (2019)
Patrick Hansen, Richard Junior Bustamante, Tsung-Yen Yang, Elizabeth Tenorio, Christopher G. Brinton, Mung Chiang, Andrew S. Lan
Predicting the Timing and Quality of Responses in Online Discussion Forums. ICDCS (2019)
Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk
IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election. ASONAM (2019)
Zichao Wang, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk
A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading. EDM (2019)
Tsung-Yen Yang, Ryan S. Baker, Christoph Studer, Neil T. Heffernan, Andrew S. Lan
Active Learning for Student Affect Detection. EDM (2019)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
Linear Spectral Estimators and an Application to Phase Retrieval. ICML (2018)
Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang
Personalized Thread Recommendation for MOOC Discussion Forums. ECML/PKDD (2) (2018)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
PhaseLin: Linear Phase Retrieval. CoRR (2018)
Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, Richard G. Baraniuk
QG-net: a data-driven question generation model for educational content. L@S (2018)
Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk
Insense: Incoherent Sensor Selection for Sparse Signals. ICASSP (2018)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
PhaseLin: Linear phase retrieval. CISS (2018)
Weiyu Chen, Andrew S. Lan, Da Cao, Christopher G. Brinton, Mung Chiang
Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. EDM (2018)
Da Cao, Andrew S. Lan, Weiyu Chen, Christopher G. Brinton, Mung Chiang
Learner Behavioral Feature Refinement and Augmentation Using GANs. AIED (2) (2018)
Andrew S. Lan, Mung Chiang, Christoph Studer
Linearized binary regression. CISS (2018)
Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang
Personalized Thread Recommendation for MOOC Discussion Forums. CoRR (2018)
Adam Winchell, Michael Mozer, Andrew S. Lan, Phillip Grimaldi, Harold Pashler
Textbook annotations as an early predictor of student learning. EDM (2018)
Tsung-Yen Yang, Christopher G. Brinton, Prateek Mittal, Mung Chiang, Andrew S. Lan
Learning Informative and Private Representations via Generative Adversarial Networks. IEEE BigData (2018)
Andrew S. Lan, Mung Chiang, Christoph Studer
An Estimation and Analysis Framework for the Rasch Model. CoRR (2018)
Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer
Linear Spectral Estimators and an Application to Phase Retrieval. CoRR (2018)
Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk
Insense: Incoherent sensor selection for sparse signals. Signal Process. 150 (2018)
Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang
Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM (2018)

Andrew S. Lan.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Andrew S. Lan