default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAndrew S. Lan
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c89]Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan:
SyllabusQA: A Course Logistics Question Answering Dataset. ACL (1) 2024: 10344-10369- [c88]Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. AIED (1) 2024: 280-294 - [c87]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: 409-417 - [c86]Nischal Ashok Kumar, Andrew S. Lan:
Improving Socratic Question Generation using Data Augmentation and Preference Optimization. BEA 2024: 108-118 - [c85]Alexander Scarlatos, Wanyong Feng, Andrew S. Lan, Simon Woodhead, Digory Smith:
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank. BEA 2024: 222-231 - [c84]Yang Shi, Peter Brusilovsky, Bita Akram, Thomas W. Price, Juho Leinonen, Kenneth R. Koedinger, Andrew S. Lan:
8th Educational Data Mining in Computer Science Education (CSEDM) Workshop. EDM 2024 - [c83]Jaewook Lee, Digory Smith, Simon Woodhead, Andrew S. Lan:
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. EDM 2024 - [c82]Nigel Fernandez, Andrew S. Lan:
Interpreting Latent Student Knowledge Representations in Programming Assignments. EDM 2024 - [c81]Hunter McNichols, Jaewook Lee, Stephen Fancsali, Steve Ritter, Andrew S. Lan:
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? EDM 2024 - [c80]Andres Felipe Zambrano, Ryan S. Baker, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
From Reaction to Anticipation: Predicting Future Affect. EDM 2024 - [c79]Jaewook Lee, Hunter McNichols, Andrew S. Lan:
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank. EMNLP (Findings) 2024: 5521-5542 - [c78]Nigel Fernandez, Alexander Scarlatos, Wanyong Feng, Simon Woodhead, Andrew S. Lan:
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. EMNLP 2024: 9063-9081 - [c77]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. NAACL-HLT (Findings) 2024: 3067-3082 - [e6]Yang Shi, Peter Brusilovsky, Bita Akram, Thomas W. Price, Juho Leinonen, Ken Koedinger, Andrew S. Lan:
Proceedings of 8th Educational Data Mining in Computer Science Education Workshop (CSEDM 2024) co-located with the 17th Educational Data Mining Conference (EDM 2024), Atlanta, GA, USA, July 14, 2024. CEUR Workshop Proceedings 3796, CEUR-WS.org 2024 [contents] - [i56]Nischal Ashok Kumar, Andrew S. Lan:
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education. CoRR abs/2402.07081 (2024) - [i55]Nischal Ashok Kumar, Andrew S. Lan:
Improving Socratic Question Generation using Data Augmentation and Preference Optimization. CoRR abs/2403.00199 (2024) - [i54]Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. CoRR abs/2403.01304 (2024) - [i53]Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan:
SyllabusQA: A Course Logistics Question Answering Dataset. CoRR abs/2403.14666 (2024) - [i52]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 abs/2404.02124 (2024) - [i51]Jaewook Lee, Digory Smith, Simon Woodhead, Andrew S. Lan:
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. CoRR abs/2405.00864 (2024) - [i50]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 abs/2405.05144 (2024) - [i49]Hunter McNichols, Jaewook Lee, Stephen Fancsali, Steven Ritter, Andrew S. Lan:
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? CoRR abs/2405.06414 (2024) - [i48]Nigel Fernandez, Andrew S. Lan:
Interpreting Latent Student Knowledge Representations in Programming Assignments. CoRR abs/2405.08213 (2024) - [i47]Nigel Fernandez, Alexander Scarlatos, Simon Woodhead, Andrew S. Lan:
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. CoRR abs/2406.19356 (2024) - [i46]Jaewook Lee, Hunter McNichols, Andrew S. Lan:
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank. CoRR abs/2409.13952 (2024) - [i45]Alexander Scarlatos, Andrew S. Lan:
Exploring Knowledge Tracing in Tutor-Student Dialogues. CoRR abs/2409.16490 (2024) - 2023
- [c76]Aritra Ghosh, Andrew S. Lan:
DiFA: Differentiable Feature Acquisition. AAAI 2023: 7705-7713 - [c75]Andres Felipe Zambrano, Ryan S. Baker, Andrew S. Lan:
Active Learning for a Classroom Observer who Can't Time Travel. ACIIW 2023: 1-8 - [c74]Alexander Scarlatos, Andrew S. Lan:
Tree-Based Representation and Generation of Natural and Mathematical Language. ACL (1) 2023: 3714-3730 - [c73]Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan:
Interpretable Math Word Problem Solution Generation via Step-by-step Planning. ACL (1) 2023: 6858-6877 - [c72]Jaewook Lee, Andrew S. Lan:
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. AIED 2023: 16-27 - [c71]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Intelligent Textbooks: The Fifth International Workshop. AIED (Posters/Late Breaking Results/...) 2023: 97-102 - [c70]Hunter McNichols, Mengxue Zhang, Andrew S. Lan:
Algebra Error Classification with Large Language Models. AIED 2023: 365-376 - [c69]Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan:
Balancing Test Accuracy and Security in Computerized Adaptive Testing. AIED 2023: 708-713 - [c68]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: 247-259 - [c67]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 - [c66]Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan:
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. EDM 2023 - [e5]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Proceedings of the Fifth International Workshop on Intelligent Textbooks 2023 co-located with the 24th International Conference on Artificial Intelligence in Education (AIED 2023), Tokyo, Japan, July 3, 2023. CEUR Workshop Proceedings 3444, CEUR-WS.org 2023 [contents] - [i44]Alexander Scarlatos, Andrew S. Lan:
Tree-Based Representation and Generation of Natural and Mathematical Language. CoRR abs/2302.07974 (2023) - [i43]Hunter McNichols, Mengxue Zhang, Andrew S. Lan:
Algebra Error Classification with Large Language Models. CoRR abs/2305.06163 (2023) - [i42]Jaewook Lee, Andrew S. Lan:
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. CoRR abs/2305.10436 (2023) - [i41]Alexander Scarlatos, Andrew S. Lan:
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning. CoRR abs/2305.14502 (2023) - [i40]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. CoRR abs/2305.16165 (2023) - [i39]Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan:
Balancing Test Accuracy and Security in Computerized Adaptive Testing. CoRR abs/2305.18312 (2023) - [i38]Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan:
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning. CoRR abs/2306.00784 (2023) - [i37]Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan:
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. CoRR abs/2306.00791 (2023) - [i36]Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew S. Lan:
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. CoRR abs/2306.08847 (2023) - [i35]Hunter McNichols, Wanyong Feng, Jaewook Lee, Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning. CoRR abs/2308.03234 (2023) - 2022
- [c65]Aritra Ghosh, Saayan Mitra, Andrew S. Lan:
DiPS: Differentiable Policy for Sketching in Recommender Systems. AAAI 2022: 6703-6712 - [c64]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Intelligent Textbooks: Themes and Topics. AIED (2) 2022: 111-114 - [c63]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: 691-697 - [c62]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. CIKM 2022: 3033-3042 - [c61]Alexander Scarlatos, Christopher Brinton, Andrew S. Lan:
Process-BERT: A Framework for Representation Learning on Educational Process Data. EDM 2022 - [c60]Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
Automatic Short Math Answer Grading via In-context Meta-learning. EDM 2022 - [c59]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-ended Knowledge Tracing for Computer Science Education. EMNLP 2022: 3849-3862 - [e4]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Proceedings of the Fourth International Workshop on Intelligent Textbooks 2022 co-located with 23d International Conference on Artificial Intelligence in Education (AIED 2022), Durham, UK, July 27, 2022. CEUR Workshop Proceedings 3192, CEUR-WS.org 2022 [contents] - [i34]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-Ended Knowledge Tracing. CoRR abs/2203.03716 (2022) - [i33]Alexander Scarlatos, Christopher G. Brinton, Andrew S. Lan:
Process-BERT: A Framework for Representation Learning on Educational Process Data. CoRR abs/2204.13607 (2022) - [i32]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 abs/2205.09864 (2022) - [i31]Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
Automatic Short Math Answer Grading via In-context Meta-learning. CoRR abs/2205.15219 (2022) - [i30]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 abs/2208.01182 (2022) - [i29]Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics. CoRR abs/2212.02985 (2022) - 2021
- [j8]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): 14110-14126 (2021) - [j7]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): 37-50 (2021) - [c58]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Mathematical Formula Representation via Tree Embeddings. iTextbooks@AIED 2021: 121-133 - [c57]Aritra Ghosh, Jay Raspat, Andrew S. Lan:
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. AIED (1) 2021: 137-149 - [c56]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: 1389-1398 - [c55]Zichao Wang, Mengxue Zhang, Richard G. Baraniuk, Andrew S. Lan:
Scientific Formula Retrieval via Tree Embeddings. IEEE BigData 2021: 1493-1503 - [c54]Aritra Ghosh, Andrew S. Lan:
Contrastive Learning Improves Model Robustness Under Label Noise. CVPR Workshops 2021: 2703-2708 - [c53]Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. EDM 2021 - [c52]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. EMNLP (1) 2021: 5986-5999 - [c51]Aritra Ghosh, Andrew S. Lan:
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. IJCAI 2021: 2410-2417 - [c50]Brian Zylich, Andrew S. Lan:
Linguistic Skill Modeling for Second Language Acquisition. LAK 2021: 141-150 - [c49]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: 151-160 - [c48]Aritra Ghosh, Andrew S. Lan:
Do We Really Need Gold Samples for Sample Weighting under Label Noise? WACV 2021: 3921-3930 - [e3]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Andrew S. Lan:
Proceedings of the Third International Workshop on Inteligent Textbooks 2021 Co-located with 22nd International Conference on Artificial Intelligence in Education (AIED 2021), Online, June 15, 2021. CEUR Workshop Proceedings 2895, CEUR-WS.org 2021 [contents] - [i28]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOCThread Recommendation. CoRR abs/2101.05625 (2021) - [i27]Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar:
Personalized Education in the AI Era: What to Expect Next? CoRR abs/2101.10074 (2021) - [i26]Aritra Ghosh, Andrew S. Lan:
Contrastive Learning Improves Model Robustness Under Label Noise. CoRR abs/2104.08984 (2021) - [i25]Aritra Ghosh, Jay Raspat, Andrew S. Lan:
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. CoRR abs/2104.09043 (2021) - [i24]Aritra Ghosh, Andrew S. Lan:
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? CoRR abs/2104.09045 (2021) - [i23]Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. CoRR abs/2104.12047 (2021) - [i22]Aritra Ghosh, Andrew S. Lan:
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. CoRR abs/2108.07386 (2021) - [i21]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. CoRR abs/2109.04546 (2021) - [i20]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 abs/2111.00901 (2021) - [i19]Aritra Ghosh, Saayan Mitra, Andrew S. Lan:
DiPS: Differentiable Policy for Sketching in Recommender Systems. CoRR abs/2112.07616 (2021) - 2020
- [j6]Adam Winchell, Andrew S. Lan, Michael Mozer:
Highlights as an Early Predictor of Student Comprehension and Interests. Cogn. Sci. 44(11) (2020) - [j5]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: 106290 (2020) - [c47]Brian Zylich, Adam Viola, Brokk Toggerson, Lara Al-Hariri, Andrew S. Lan:
Exploring Automated Question Answering Methods for Teaching Assistance. AIED (1) 2020: 610-622 - [c46]Aritra Ghosh, Beverly P. Woolf, Shlomo Zilberstein, Andrew S. Lan:
Skill-based Career Path Modeling and Recommendation. IEEE BigData 2020: 1156-1165 - [c45]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CISS 2020: 1-6 - [c44]Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. EDM 2020 - [c43]Shashank Sonkar, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. EDM 2020 - [c42]Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan:
Robust and Interpretable Grounding of Spatial References with Relation Networks. EMNLP (Findings) 2020: 1908-1923 - [c41]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOC Thread Recommendation. ICDM (Workshops) 2020: 400-407 - [c40]Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan:
Context-Aware Attentive Knowledge Tracing. KDD 2020: 2330-2339 - [e2]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Andrew S. Lan:
Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), Online, July 06, 2020. CEUR Workshop Proceedings 2674, CEUR-WS.org 2020 [contents] - [i18]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CoRR abs/2001.10509 (2020) - [i17]Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. CoRR abs/2005.12442 (2020) - [i16]Jack Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. CoRR abs/2005.13107 (2020) - [i15]Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan:
Context-Aware Attentive Knowledge Tracing. CoRR abs/2007.12324 (2020)
2010 – 2019
- 2019
- [c39]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: 274-281 - [c38]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction with Neural Collaborative Filtering. DSAA 2019: 1-10 - [c37]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. EDM 2019 - [c36]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 - [c35]Tsung-Yen Yang, Ryan S. Baker, Christoph Studer, Neil T. Heffernan, Andrew S. Lan:
Active Learning for Student Affect Detection. EDM 2019 - [c34]Yuwei Tu, Christopher G. Brinton, Andrew S. Lan, Mung Chiang:
Adaptive Remediation with Multi-modal Content. HCI (32) 2019: 455-468 - [c33]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: 1931-1940 - [c32]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: 622-630 - [e1]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Rakesh Agrawal, Andrew S. Lan:
Proceedings of the First Workshop on Intelligent Textbooks co-located with 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, IL, USA, June 25, 2019. CEUR Workshop Proceedings 2384, CEUR-WS.org 2019 [contents] - [i14]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 abs/1905.08831 (2019) - 2018
- [j4]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent sensor selection for sparse signals. Signal Process. 150: 57-65 (2018) - [j3]Christopher G. Brinton, Swapna Buccapatnam, Liang Zheng, Da Cao, Andrew S. Lan, Felix Ming Fai Wong, Sangtae Ha, Mung Chiang, H. Vincent Poor:
On the Efficiency of Online Social Learning Networks. IEEE/ACM Trans. Netw. 26(5): 2076-2089 (2018) - [c31]Da Cao, Andrew S. Lan, Weiyu Chen, Christopher G. Brinton, Mung Chiang:
Learner Behavioral Feature Refinement and Augmentation Using GANs. AIED (2) 2018: 41-46 - [c30]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: 1534-1543 - [c29]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear phase retrieval. CISS 2018: 1-6 - [c28]Andrew S. Lan, Mung Chiang, Christoph Studer:
Linearized binary regression. CISS 2018: 1-6 - [c27]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 - [c26]Adam Winchell, Michael Mozer, Andrew S. Lan, Phillip Grimaldi, Harold Pashler:
Textbook annotations as an early predictor of student learning. EDM 2018 - [c25]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent Sensor Selection for Sparse Signals. ICASSP 2018: 4689-4693 - [c24]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. ICML 2018: 1729-1738 - [c23]Andrew S. Lan, Mung Chiang, Christoph Studer:
An Estimation and Analysis Framework for the Rasch Model. ICML 2018: 2889-2897 - [c22]Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang:
Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM 2018: 2762-2770 - [c21]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: 7:1-7:10 - [c20]Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang:
Personalized Thread Recommendation for MOOC Discussion Forums. ECML/PKDD (2) 2018: 725-740 - [i13]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear Phase Retrieval. CoRR abs/1802.00432 (2018) - [i12]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. CoRR abs/1806.03547 (2018) - [i11]Andrew S. Lan, Mung Chiang, Christoph Studer:
An Estimation and Analysis Framework for the Rasch Model. CoRR abs/1806.03551 (2018) - [i10]Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang:
Personalized Thread Recommendation for MOOC Discussion Forums. CoRR abs/1806.08468 (2018) - 2017
- [j2]Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
BLAh: Boolean Logic Analysis for Graded Student Response Data. IEEE J. Sel. Top. Signal Process. 11(5): 754-764 (2017) - [c19]Andrew S. Lan, Christopher G. Brinton, Tsung-Yen Yang, Mung Chiang:
Behavior-Based Latent Variable Model for Learner Engagement. EDM 2017 - [c18]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks. EDM 2017 - [c17]Joshua J. Michalenko, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
Data-Mining Textual Responses to Uncover Misconception Patterns. EDM 2017 - [c16]Jack Z. Wang, Andrew S. Lan, Phillip Grimaldi, Richard G. Baraniuk:
A Latent Factor Model For Instructor Content Preference Analysis. EDM 2017 - [c15]Andrew E. Waters, Phillip Grimaldi, Andrew S. Lan, Richard G. Baraniuk:
Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning. EDM 2017 - [c14]Indu Manickam, Andrew S. Lan, Richard G. Baraniuk:
Contextual multi-armed bandit algorithms for personalized learning action selection. ICASSP 2017: 6344-6348 - [c13]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
RHash: Robust Hashing via L_infinity-norm Distortion. IJCAI 2017: 1386-1394 - [c12]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
D.TRUMP: Data-mining Textual Responses to Uncover Misconception Patterns. L@S 2017: 245-248 - [i9]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent Sensor Selection for Sparse Signals. CoRR abs/1702.07670 (2017) - [i8]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
Data-Mining Textual Responses to Uncover Misconception Patterns. CoRR abs/1703.08544 (2017) - 2016
- [c11]Divyanshu Vats, Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Optimal ranking of test items using the Rasch model. Allerton 2016: 467-473 - [c10]Andrew S. Lan, Richard G. Baraniuk:
A Contextual Bandits Framework for Personalized Learning Action Selection. EDM 2016: 424-429 - [c9]Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer:
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. ICML 2016: 266-275 - [i7]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
Near-Isometric Binary Hashing for Large-scale Datasets. CoRR abs/1603.03836 (2016) - 2015
- [c8]Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard G. Baraniuk:
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions. L@S 2015: 167-176 - [i6]Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard G. Baraniuk:
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions. CoRR abs/1501.04346 (2015) - 2014
- [j1]Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
Sparse factor analysis for learning and content analytics. J. Mach. Learn. Res. 15(1): 1959-2008 (2014) - [c7]Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Quantized Matrix Completion for Personalized Learning. EDM 2014: 280-283 - [c6]Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Matrix recovery from quantized and corrupted measurements. ICASSP 2014: 4973-4977 - [c5]Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Time-varying learning and content analytics via sparse factor analysis. KDD 2014: 452-461 - [i5]Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk:
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics. CoRR abs/1412.5967 (2014) - [i4]Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Quantized Matrix Completion for Personalized Learning. CoRR abs/1412.5968 (2014) - 2013
- [c4]Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk:
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics. EDM 2013: 90-97 - [c3]Divyanshu Vats, Christoph Studer, Andrew S. Lan, Lawrence Carin, Richard G. Baraniuk:
Test-size Reduction for Concept Estimation. EDM 2013: 292-295 - [c2]Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk:
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data. EDM 2013: 324-325 - [c1]Andrew E. Waters, Andrew S. Lan, Christoph Studer:
Sparse probit factor analysis for learning analytics. ICASSP 2013: 8776-8780 - [i3]Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
Sparse Factor Analysis for Learning and Content Analytics. CoRR abs/1303.5685 (2013) - [i2]Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk:
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data. CoRR abs/1305.1956 (2013) - [i1]Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Time-varying Learning and Content Analytics via Sparse Factor Analysis. CoRR abs/1312.5734 (2013)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-15 19:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
