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2020 – today
- 2024
[j17]Preya Shabrina
, Behrooz Mostafavi, Mark Abdelshiheed, Min Chi, Tiffany Barnes:
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving. Int. J. Artif. Intell. Educ. 34(3): 825-861 (2024)- [j16]Mark Abdelshiheed, Tiffany Barnes, Min Chi:
How and When: The Impact of Metacognitive Knowledge Instruction and Motivation on Transfer Across Intelligent Tutoring Systems. Int. J. Artif. Intell. Educ. 34(3): 974-1007 (2024) - [j15]Mark Abdelshiheed, Robert Moulder, John Wesley Hostetter, Tiffany Barnes, Min Chi:
Example, nudge, or practice? Assessing metacognitive knowledge transfer of factual and procedural learners. User Model. User Adapt. Interact. 34(5): 1745-1775 (2024) - [c117]Ge Gao, Xi Yang, Min Chi:
Get a Head Start: On-Demand Pedagogical Policy Selection in Intelligent Tutoring. AAAI 2024: 12136-12144 - [c116]Yang Shi, Min Chi, Tiffany Barnes, Thomas W. Price:
Evaluating Multi-Knowledge Component Interpretability of Deep Knowledge Tracing Models in Programming. EDM 2024 - [c115]Nazia Alam, Behrooz Mostafavi, Sutapa Dey Tithi, Min Chi, Tiffany Barnes:
How Much Training is Needed? Reducing Training Time using Deep Reinforcement Learning in an Intelligent Tutor. EDM 2024 - [c114]Md Mirajul Islam, Xi Yang, John Wesley Hostetter, Adittya Soukarjya Saha, Min Chi:
A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategies. EDM 2024 - [c113]Gyuhun Jung, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
More, May not the Better: Insights from Applying Deep Reinforcement Learning for Pedagogical Policy Induction. EDM 2024 - [c112]Ge Gao, Qitong Gao, Xi Yang, Song Ju, Miroslav Pajic, Min Chi:
On Trajectory Augmentations for Off-Policy Evaluation. ICLR 2024 - [c111]Hyunwoo Sohn, Kyungjin Park, Baekkwan Park, Min Chi:
Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction. IJCAI 2024: 6035-6043 - [i26]Md Mirajul Islam, Xi Yang, John Wesley Hostetter, Adittya Soukarjya Saha, Min Chi:
A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategies. CoRR abs/2406.02450 (2024) - [i25]Ge Gao, Xi Yang, Qitong Gao, Song Ju, Miroslav Pajic, Min Chi:
Off-Policy Selection for Initiating Human-Centric Experimental Design. CoRR abs/2410.20017 (2024) - 2023
- [j14]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way. Int. J. Artif. Intell. Educ. 33(4): 1031-1056 (2023) - [j13]Yeo Jin Kim, Min Chi:
Time-aware deep reinforcement learning with multi-temporal abstraction. Appl. Intell. 53(17): 20007-20033 (2023) - [j12]Munindar P. Singh, Min Chi, Veena Misra:
Healthful Connected Living: Vision and Challenges for the Case of Obesity. IEEE Internet Comput. 27(3): 7-14 (2023) - [j11]Daniel S. Shen, Min Chi:
TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering. Inf. Sci. 621: 611-626 (2023) - [j10]Mehak Maniktala, Min Chi, Tiffany Barnes:
Enhancing a student productivity model for adaptive problem-solving assistance. User Model. User Adapt. Interact. 33(1): 159-188 (2023) - [c110]Nazia Alam, Mehak Maniktala, Behrooz Mostafavi, Min Chi, Tiffany Barnes:
Does Knowing When Help Is Needed Improve Subgoal Hint Performance in an Intelligent Data-Driven Logic Tutor? AAAI 2023: 15895-15902 - [c109]Nazia Alam, Behrooz Mostafavi, Min Chi, Tiffany Barnes:
Exploring the Effect of Autoencoder Based Feature Learning for a Deep Reinforcement Learning Policy for Providing Proactive Help. AIED (Posters/Late Breaking Results/...) 2023: 278-283 - [c108]Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi:
Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems. AIED 2023: 291-303 - [c107]Preya Shabrina, Behrooz Mostafavi, Min Chi, Tiffany Barnes:
Impact of Learning a Subgoal-Directed Problem-Solving Strategy Within an Intelligent Logic Tutor. AIED 2023: 389-400 - [c106]Markel Sanz Ausin, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics. AIED (Posters/Late Breaking Results/...) 2023: 599-605 - [c105]John Wesley Hostetter, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
A Self-Organizing Neuro-Fuzzy Q-Network: Systematic Design with Offline Hybrid Learning. AAMAS 2023: 1248-1257 - [c104]Ge Gao, Song Ju, Markel Sanz Ausin, Min Chi:
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare. AAMAS 2023: 1504-1513 - [c103]Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi:
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning. CogSci 2023 - [c102]Yang Shi, Robin Schmucker, Min Chi, Tiffany Barnes, Thomas W. Price:
KC-Finder: Automated Knowledge Component Discovery for Programming Problems. EDM 2023 - [c101]Preya Shabrina, Behrooz Mostafavi, Sutapa Dey Tithi, Min Chi, Tiffany Barnes:
Learning Problem Decomposition-Recomposition with Data-driven Chunky Parsons Problems within an Intelligent Logic Tutor. EDM 2023 - [c100]John Wesley Hostetter, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
Leveraging Fuzzy Logic Towards More Explainable Reinforcement Learning-Induced Pedagogical Policies on Intelligent Tutoring Systems. FUZZ 2023: 1-7 - [c99]John Wesley Hostetter, Min Chi:
Latent Space Encoding for Interpretable Fuzzy Logic Rules in Continuous and Noisy High-Dimensional Spaces. FUZZ 2023: 1-6 - [c98]Ge Gao, Min Chi:
Trace Augmentation with Missing EHRs for Sepsis Treatments. ICHI 2023: 480 - [c97]Qitong Gao, Ge Gao, Min Chi, Miroslav Pajic:
Variational Latent Branching Model for Off-Policy Evaluation. ICLR 2023 - [c96]Xi Yang, Ge Gao, Min Chi:
Hierarchical Apprenticeship Learning for Disease Progression Modeling. IJCAI 2023: 2388-2396 - [c95]John Wesley Hostetter, Cristina Conati, Xi Yang, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
XAI to Increase the Effectiveness of an Intelligent Pedagogical Agent. IVA 2023: 28:1-28:9 - [c94]Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh, Min Chi, Miroslav Pajic:
Off-Policy Evaluation for Human Feedback. NeurIPS 2023 - [i24]Qitong Gao, Ge Gao, Min Chi, Miroslav Pajic:
Variational Latent Branching Model for Off-Policy Evaluation. CoRR abs/2301.12056 (2023) - [i23]Ge Gao, Song Ju, Markel Sanz Ausin, Min Chi:
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare. CoRR abs/2302.09212 (2023) - [i22]Mark Abdelshiheed, Mehak Maniktala, Song Ju, Ayush Jain, Tiffany Barnes, Min Chi:
Preparing Unprepared Students For Future Learning. CoRR abs/2303.11960 (2023) - [i21]Mark Abdelshiheed, John Wesley Hostetter, Preya Shabrina, Tiffany Barnes, Min Chi:
The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems. CoRR abs/2303.11965 (2023) - [i20]Mark Abdelshiheed, John Wesley Hostetter, Xi Yang, Tiffany Barnes, Min Chi:
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer. CoRR abs/2303.12223 (2023) - [i19]Mark Abdelshiheed, Guojing Zhou, Mehak Maniktala, Tiffany Barnes, Min Chi:
Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning. CoRR abs/2303.13541 (2023) - [i18]Mark Abdelshiheed, Mehak Maniktala, Tiffany Barnes, Min Chi:
Assessing Competency Using Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning. CoRR abs/2303.14609 (2023) - [i17]Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi:
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems. CoRR abs/2304.09821 (2023) - [i16]Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi:
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning. CoRR abs/2304.11739 (2023) - [i15]Xi Yang, Ge Gao, Min Chi:
An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions. CoRR abs/2305.09070 (2023) - [i14]Qitong Gao, Ge Gao, Juncheng Dong, Vahid Tarokh, Min Chi, Miroslav Pajic:
Off-Policy Evaluation for Human Feedback. CoRR abs/2310.07123 (2023) - 2022
- [j9]Christa Cody, Mehak Maniktala, Nicholas Lytle, Min Chi, Tiffany Barnes:
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor. Int. J. Artif. Intell. Educ. 32(2): 263-296 (2022) - [j8]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. Int. J. Artif. Intell. Educ. 32(2): 454-500 (2022) - [c93]Ye Mao, Farzaneh Khoshnevisan, Thomas W. Price, Tiffany Barnes, Min Chi:
Cross-Lingual Adversarial Domain Adaptation for Novice Programming. AAAI 2022: 7682-7690 - [c92]Song Ju, Xi Yang, Tiffany Barnes, Min Chi:
Student-Tutor Mixed-Initiative Decision-Making Supported by Deep Reinforcement Learning. AIED (1) 2022: 440-452 - [c91]Mark Abdelshiheed, John Wesley Hostetter, Xi Yang, Tiffany Barnes, Min Chi:
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer. AIED (1) 2022: 546-552 - [c90]Mark Abdelshiheed, John Wesley Hostetter, Preya Shabrina, Tiffany Barnes, Min Chi:
The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems. CogSci 2022 - [c89]Yang Shi, Min Chi, Tiffany Barnes, Thomas W. Price:
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. EDM 2022 - [c88]Ge Gao, Farzaneh Khoshnevisan, Min Chi:
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction. ICHI 2022: 151-162 - [c87]Ge Gao, Qitong Gao, Xi Yang, Miroslav Pajic, Min Chi:
A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification. IJCAI 2022: 2994-3000 - [i13]Mitchell Plyler, Michael Green, Min Chi:
Making a (Counterfactual) Difference One Rationale at a Time. CoRR abs/2201.05177 (2022) - [i12]Ge Gao, Farzaneh Khoshnevisan, Min Chi:
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction. CoRR abs/2203.08245 (2022) - [i11]Yang Shi, Min Chi, Tiffany Barnes, Thomas W. Price:
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks. CoRR abs/2206.03545 (2022) - [i10]Mehak Maniktala, Min Chi, Tiffany Barnes:
Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance. CoRR abs/2207.03025 (2022) - [i9]Preya Shabrina, Behrooz Mostafavi, Mark Abdelshiheed, Min Chi, Tiffany Barnes:
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving. CoRR abs/2208.04696 (2022) - [i8]Preya Shabrina, Samiha Marwan, Andrew Bennison, Min Chi, Thomas W. Price, Tiffany Barnes:
A Multicriteria Evaluation for Data-Driven Programming Feedback Systems: Accuracy, Effectiveness, Fallibility, and Students' Response. CoRR abs/2208.05326 (2022) - 2021
- [j7]Mehak Maniktala, Christa Cody, Tiffany Barnes, Min Chi:
Correction to: Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor. Int. J. Artif. Intell. Educ. 31(1): 154-155 (2021) - [c86]Song Ju, Guojing Zhou, Mark Abdelshiheed, Tiffany Barnes, Min Chi:
Evaluating Critical Reinforcement Learning Framework in the Field. AIED (1) 2021: 215-227 - [c85]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. AIED (1) 2021: 356-368 - [c84]Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo-Jin Kim, Min Chi:
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. IEEE BigData 2021: 1337-1348 - [c83]Song Ju, Yeo Jin Kim, Markel Sanz Ausin, Maria E. Mayorga, Min Chi:
To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning. IEEE BigData 2021: 1640-1646 - [c82]Yeo Jin Kim, Markel Sanz Ausin, Min Chi:
Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention. IEEE BigData 2021: 1657-1663 - [c81]Mark Abdelshiheed, Mehak Maniktala, Song Ju, Ayush Jain, Tiffany Barnes, Min Chi:
Preparing Unprepared Students For Future Learning. CogSci 2021 - [c80]Yang Shi, Ye Mao, Tiffany Barnes, Min Chi, Thomas W. Price:
More With Less: Exploring How to Use Deep Learning Effectively through Semi-supervised Learning for Automatic Bug Detection in Student Code. EDM 2021 - [c79]Ye Mao, Yang Shi, Samiha Marwan, Thomas W. Price, Tiffany Barnes, Min Chi:
Knowing both when and where: Temporal-ASTNN for Early Prediction of Student Success in Novice Programming Tasks. EDM 2021 - [c78]Samiha Marwan, Yang Shi, Ian Menezes, Min Chi, Tiffany Barnes, Thomas W. Price:
Just a Few Expert Constraints Can Help: Humanizing Data-Driven Subgoal Detection for Novice Programming. EDM 2021 - [c77]Esha Sharma, Lauren B. Davis, Julie S. Ivy, Min Chi:
Data to Donations: Towards In-Kind Food Donation Prediction across Two Coasts. GHTC 2021: 281-288 - [c76]Xi Yang, Yuan Zhang, Min Chi:
Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling. IJCAI 2021: 3581-3587 - [c75]Mitchell Plyler, Michael Green, Min Chi:
Making a (Counterfactual) Difference One Rationale at a Time. NeurIPS 2021: 28701-28713 - [c74]Farzaneh Khoshnevisan, Min Chi:
Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups. ECML/PKDD (1) 2021: 521-537 - [i7]Daniel Shen, Min Chi:
TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering. CoRR abs/2101.07731 (2021) - [i6]Christa Cody, Mehak Maniktala, Nicholas Lytle, Min Chi, Tiffany Barnes:
The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor. CoRR abs/2102.05741 (2021) - [i5]Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo-Jin Kim, Min Chi:
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem. CoRR abs/2105.00568 (2021) - [i4]Yeo-Jin Kim, Min Chi:
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning. CoRR abs/2105.02580 (2021) - 2020
- [j6]Mehak Maniktala, Christa Cody, Tiffany Barnes, Min Chi:
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor. Int. J. Artif. Intell. Educ. 30(4): 637-667 (2020) - [j5]Yuan Zhang, Chen Lin, Min Chi:
Going deeper: Automatic short-answer grading by combining student and question models. User Model. User Adapt. Interact. 30(1): 51-80 (2020) - [c73]Markel Sanz Ausin, Mehak Maniktala, Tiffany Barnes, Min Chi:
Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. AIED (1) 2020: 472-485 - [c72]Farzaneh Khoshnevisan, Min Chi:
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems. IEEE BigData 2020: 64-73 - [c71]Hyunwoo Sohn, Kyungjin Park, Min Chi:
MuLan: Multilevel Language-based Representation Learning for Disease Progression Modeling. IEEE BigData 2020: 1246-1255 - [c70]Mark Abdelshiheed, Min Chi:
Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning. CogSci 2020 - [c69]Christa Cody, Mehak Maniktala, David Warren, Min Chi, Tiffany Barnes:
Does autonomy help Help? The impact of unsolicited hints and choice on help avoidance and learning. EDM 2020 - [c68]Song Ju, Min Chi, Guojing Zhou:
Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards. EDM 2020 - [c67]Mehak Maniktala, Tiffany Barnes, Min Chi:
Extending the Hint Factory: Towards Modelling Productivity for Open-ended Problem-solving. EDM 2020 - [c66]Ye Mao, Samiha Marwan, Thomas W. Price, Tiffany Barnes, Min Chi:
What Time is It? Student Modeling Needs to Know. EDM 2020 - [c65]Samiha Marwan, Thomas W. Price, Min Chi, Tiffany Barnes:
Immediate Data-Driven Positive Feedback Increases Engagement on Programming Homework for Novices. CSEDM@EDM 2020 - [c64]Preya Shabrina, Samiha Marwan, Min Chi, Thomas W. Price, Tiffany Barnes:
The Impact of Data-driven Positive Programming Feedback: When it Helps, What Happens when it Goes Wrong, and How Students Respond. CSEDM@EDM 2020 - [c63]Xi Yang, Guojing Zhou, Michelle Taub, Roger Azevedo, Min Chi:
Student Subtyping via EM-Inverse Reinforcement Learning. EDM 2020 - [c62]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract). IJCAI 2020: 4691-4695 - [c61]Xi Yang, Yeo-Jin Kim, Michelle Taub, Roger Azevedo, Min Chi:
PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems. MMM (2) 2020: 63-75 - [c60]Guojing Zhou, Xi Yang, Hamoon Azizsoltani, Tiffany Barnes, Min Chi:
Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies. UMAP 2020: 284-292 - [i3]Mehak Maniktala, Christa Cody, Tiffany Barnes, Min Chi:
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor. CoRR abs/2009.13371 (2020) - [i2]Mehak Maniktala, Christa Cody, Amy Isvik, Nicholas Lytle, Min Chi, Tiffany Barnes:
Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help. CoRR abs/2010.04124 (2020) - [i1]Farzaneh Khoshnevisan, Min Chi:
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems. CoRR abs/2010.13952 (2020)
2010 – 2019
- 2019
- [c59]Guojing Zhou, Hamoon Azizsoltani, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Hierarchical Reinforcement Learning for Pedagogical Policy Induction. AIED (1) 2019: 544-556 - [c58]Ali Jazayeri, Muge Capan, Christopher C. Yang, Farzaneh Khoshnevisan, Min Chi, Ryan Arnold:
Network-Based Modeling of Sepsis: Quantification and Evaluation of Simultaneity of Organ Dysfunctions. BCB 2019: 87-96 - [c57]Chen Lin, Julie S. Ivy, Min Chi:
Multi-layer Facial Representation Learning for Early Prediction of Septic Shock. IEEE BigData 2019: 840-849 - [c56]Shuai Yang, Xipeng Shen, Min Chi:
Streamline Density Peak Clustering for Practical Adoptions. CIKM 2019: 49-58 - [c55]Guojing Zhou, Xi Yang, Min Chi:
Big, Little, or Both? Exploring the Impact of Granularity on Learning for Students with Different Incoming Competence. CogSci 2019: 3206-3212 - [c54]Markel Sanz Ausin, Hamoon Azizsoltani, Tiffany Barnes, Min Chi:
Leveraging Deep Reinforcement Learning for Pedagogical Policy Induction in an Intelligent Tutoring System. EDM 2019 - [c53]Song Ju, Shitian Shen, Hamoon Azizsoltani, Tiffany Barnes, Min Chi:
Importance Sampling to Identify Empirically Valid Policies and their Critical Decisions. EDM (Workshops) 2019: 69-78 - [c52]Song Ju, Guojing Zhou, Hamoon Azizsoltani, Tiffany Barnes, Min Chi:
Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning. EDM 2019 - [c51]Ye Mao, Rui Zhi, Farzaneh Khoshnevisan, Thomas W. Price, Tiffany Barnes, Min Chi:
One minute is enough: Early Prediction of Student Success and Event-level Difficulty during Novice Programming Tasks. EDM 2019 - [c50]Rui Zhi, Min Chi, Tiffany Barnes, Thomas W. Price:
Evaluating the Effectiveness of Parsons Problems for Block-based Programming. ICER 2019: 51-59 - [c49]Xi Yang, Yeo-Jin Kim, Farzaneh Khoshnevisan, Yuan Zhang, Min Chi:
Missing Data Imputation for MIMIC-III using Matrix Decomposition. ICHI 2019: 1-3 - [c48]Yuan Zhang, Xi Yang, Julie S. Ivy, Min Chi:
Time-aware Adversarial Networks for Adapting Disease Progression Modeling. ICHI 2019: 1-11 - [c47]Hamoon Azizsoltani, Yeo-Jin Kim, Markel Sanz Ausin, Tiffany Barnes, Min Chi:
Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts. IJCAI 2019: 1974-1980 - [c46]Yuan Zhang, Xi Yang, Julie S. Ivy, Min Chi:
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling. IJCAI 2019: 4369-4375 - [c45]Daniel Shen, Min Chi:
An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis. AALTD@PKDD/ECML 2019: 213-228 - [c44]Rui Zhi, Thomas W. Price, Samiha Marwan, Alexandra Milliken, Tiffany Barnes, Min Chi:
Exploring the Impact of Worked Examples in a Novice Programming Environment. SIGCSE 2019: 98-104 - 2018
- [c43]Shitian Shen, Behrooz Mostafavi, Collin F. Lynch, Tiffany Barnes, Min Chi:
Empirically Evaluating the Effectiveness of POMDP vs. MDP Towards the Pedagogical Strategies Induction. AIED (2) 2018: 327-331 - [c42]Xi Yang, Yuan Zhang, Min Chi:
Time-aware Subgroup Matrix Decomposition: Imputing Missing Data Using Forecasting Events. IEEE BigData 2018: 1524-1533 - [c41]Chen Lin, Yuan Zhang, Julie S. Ivy, Muge Capan, Ryan Arnold, Jeanne M. Huddleston, Min Chi:
Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM. ICHI 2018: 219-228 - [c40]Farzaneh Khoshnevisan, Julie S. Ivy, Muge Capan, Ryan Arnold, Jeanne Huddleston, Min Chi:
Recent Temporal Pattern Mining for Septic Shock Early Prediction. ICHI 2018: 229-240 - [c39]Yeo-Jin Kim, Min Chi:
Temporal Belief Memory: Imputing Missing Data during RNN Training. IJCAI 2018: 2326-2332 - [c38]Shitian Shen, Markel Sanz Ausin, Behrooz Mostafavi, Min Chi:
Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. UMAP 2018: 43-51 - 2017
- [j4]Euijin Choo, Ting Yu, Min Chi:
Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis. J. Comput. Secur. 25(3): 283-318 (2017) - [c37]Chen Lin, Min Chi:
A Comparisons of BKT, RNN and LSTM for Learning Gain Prediction. AIED 2017: 536-539 - [c36]Yuan Zhang, Chen Lin, Min Chi, Julie S. Ivy, Muge Capan, Jeanne M. Huddleston:
LSTM for septic shock: Adding unreliable labels to reliable predictions. IEEE BigData 2017: 1233-1242 - [c35]Guojing Zhou, Min Chi:
The Impact of Decision Agency & Granularity on Aptitude Treatment Interaction in Tutoring. CogSci 2017 - [c34]Joseph Beck, Min Chi, Ryan S. Baker:
Workshop proposal: deep learning for educational data mining. EDM 2017 - [c33]Shitian Shen, Min Chi:
Clustering Student Sequential Trajectories Using Dynamic Time Wrapping. EDM 2017 - [c32]Linting Xue, Collin F. Lynch, Min Chi:
Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection. EDM 2017 - [c31]Guojing Zhou, Jianxun Wang, Collin F. Lynch, Min Chi:
Towards Closing the Loop: Bridging Machine-induced Pedagogical Policies to Learning Theories. EDM 2017 - 2016
- [c30]Guojing Zhou, Collin F. Lynch, Thomas W. Price, Tiffany Barnes, Min Chi:
The Impact of Granularity on the Effectiveness of Students' Pedagogical Decisions. CogSci 2016 - [c29]Linting Xue, Collin F. Lynch, Min Chi:
Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams. EDM 2016: 255-262 - [c28]Shitian Shen, Min Chi:
Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning. EDM 2016: 507-512 - [c27]Yuan Zhang, Rajat Shah, Min Chi:
Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading. EDM 2016: 562-567 - [c26]Collin F. Lynch, Linting Xue, Min Chi:
Evolving Augmented Graph Grammars for Argument Analysis. GECCO (Companion) 2016: 65-66 - [c25]Chen Lin, Min Chi:
Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing. ITS 2016: 208-218 - [c24]Shitian Shen, Min Chi:
Reinforcement Learning: the Sooner the Better, or the Later the Better? UMAP 2016: 37-44 - [c23]Chen Lin, Shitian Shen, Min Chi:
Incorporating Student Response Time and Tutor Instructional Interventions into Student Modeling. UMAP 2016: 157-161 - [e2]Tiffany Barnes, Min Chi, Mingyu Feng:
Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, Raleigh, North Carolina, USA, June 29 - July 2, 2016. International Educational Data Mining Society (IEDMS) 2016 [contents] - 2015
- [c22]Behrooz Mostafavi, Guojing Zhou, Collin F. Lynch, Min Chi, Tiffany Barnes:
Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor. AIED 2015: 726-729 - [c21]Guojing Zhou, Thomas W. Price, Collin F. Lynch, Tiffany Barnes, Min Chi:
The Impact of Granularity on Worked Examples and Problem Solving. CogSci 2015 - [c20]Euijin Choo, Ting Yu, Min Chi:
Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis. DBSec 2015: 170-187 - [c19]Collin F. Lynch, Thomas W. Price, Min Chi, Tiffany Barnes:
Using the Hint Factory to Compare Model-Based Tutoring Systems. EDM (Workshops) 2015 - [c18]Thomas W. Price, Collin F. Lynch, Tiffany Barnes, Min Chi:
An Improved Data-Driven Hint Selection Algorithm for Probability Tutors. EDM 2015: 610-611 - [c17]Linting Xue, Collin F. Lynch, Min Chi:
Graph Grammar Induction via Evolutionary Computation. EDM (Workshops) 2015 - 2014
- [c16]Min Chi, Daniel L. Schwartz, Kristen Pilner Blair, Doris B. Chin:
Choice-based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores? EDM 2014: 36-43 - [c15]Nicole R. Hallinen, Julius Cheng, Min Chi, Daniel L. Schwartz:
Tug of War: What is it Good For? Measuring Student Inquiry Choices in an Online Science Game. ICLS 2014 - [c14]Min Chi, Pamela W. Jordan, Kurt VanLehn:
When Is Tutorial Dialogue More Effective Than Step-Based Tutoring? Intelligent Tutoring Systems 2014: 210-219 - [c13]Collin F. Lynch, Kevin D. Ashley, Min Chi:
Can Diagrams Predict Essay Grades? Intelligent Tutoring Systems 2014: 260-265 - [c12]Euijin Choo, Ting Yu, Min Chi, Yan Sun:
Revealing and incorporating implicit communities to improve recommender systems. EC 2014: 489-506 - [e1]Iván Cantador, Min Chi, Rosta Farzan, Robert Jäschke:
Posters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014), Aalborg, Denmark, July 7-11, 2014. CEUR Workshop Proceedings 1181, CEUR-WS.org 2014 [contents] - 2011
- [j3]Min Chi, Kurt VanLehn, Diane J. Litman, Pamela W. Jordan:
An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning Approach. Int. J. Artif. Intell. Educ. 21(1-2): 83-113 (2011) - [j2]Min Chi, Kurt VanLehn, Diane J. Litman, Pamela W. Jordan:
Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Model. User Adapt. Interact. 21(1-2): 137-180 (2011) - [c11]Min Chi, Kenneth R. Koedinger, Geoffrey J. Gordon, Pamela W. Jordan, Kurt VanLehn:
Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions. EDM 2011: 61-70 - [c10]Sujith M. Gowda, Jonathan P. Rowe, Ryan Shaun Joazeiro de Baker, Min Chi, Kenneth R. Koedinger:
Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty. EDM 2011: 199-208 - 2010
- [j1]Min Chi, Kurt VanLehn:
Meta-Cognitive Strategy Instruction in Intelligent Tutoring Systems: How, When, and Why. J. Educ. Technol. Soc. 13(1): 25-39 (2010) - [c9]Min Chi, Kurt VanLehn, Diane J. Litman:
Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics. Intelligent Tutoring Systems (1) 2010: 224-234 - [c8]Min Chi, Kurt VanLehn, Diane J. Litman, Pamela W. Jordan:
Inducing Effective Pedagogical Strategies Using Learning Context Features. UMAP 2010: 147-158
2000 – 2009
- 2009
- [c7]Min Chi, Pamela W. Jordan, Kurt VanLehn, Diane J. Litman:
To Elicit Or To Tell: Does It Matter? AIED 2009: 197-204 - 2008
- [c6]Min Chi, Pamela W. Jordan, Kurt VanLehn, Moses Hall:
Reinforcement Learning-based Feature Seleciton For Developing Pedagogically Effective Tutorial Dialogue Tactics. EDM 2008: 258-265 - [c5]Min Chi, Kurt VanLehn:
Eliminating the Gap between the High and Low Students through Meta-cognitive Strategy Instruction. Intelligent Tutoring Systems 2008: 603-613 - 2007
- [c4]Min Chi, Kurt VanLehn:
Accelerated Future Learning via Explicit Instruction of a Problem Solving Strategy. AIED 2007: 409-416 - [c3]Min Chi, Kurt VanLehn:
Domain-Specific and Domain-Independent Interactive Behaviors in Andes. AIED 2007: 548-550 - [c2]Min Chi, Kurt VanLehn:
Porting an Intelligent Tutoring System across Domains. AIED 2007: 551-553 - 2004
- [c1]Kurt VanLehn, Dumiszewe Bhembe, Min Chi, Collin F. Lynch, Kay G. Schulze, Robert Shelby, Linwood Taylor, Donald Treacy, Anders Weinstein, Mary Wintersgill:
Implicit Versus Explicit Learning of Strategies in a Non-procedural Cognitive Skill. Intelligent Tutoring Systems 2004: 521-530
Coauthor Index
[j17] [j16] [j15] [c116] [c115] [c113] [j14] [j10] [c110] [c109] [c108] [c107] [c106] [c105] [c103] [c102] [c101] [c100] [c95] [i22] [i21] [i20] [i19] [i18] [i17] [i16] [j9] [j8] [c93] [c92] [c91] [c90] [c89] [i11] [i10] [i9] [i8] [j7] [c86] [c85] [c81] [c80] [c79] [c78] [i6] [j6] [c73] [c69] [c67] [c66] [c65] [c64] [c62] [c60] [i3] [i2] [c59] [c54] [c53] [c52] [c51] [c50] [c47] [c44] [c43] [c30] [e2] [c22] [c21] [c19] [c18]
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