default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMin Chi
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
[j16]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)- [j15]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) - [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 - [i25]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) - 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
[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]
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 2024-11-11 21:25 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:
