Min Chi

Healthful Connected Living: Vision and Challenges for the Case of Obesity

IEEE INTERNET COMPUTING, 27(3), 7–14.

How and When: The Impact of Metacognitive Knowledge Instruction and Motivation on Transfer Across Intelligent Tutoring Systems

Abdelshiheed, M., Barnes, T., & Chi, M. (2023, September 27). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.

Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning Towards Improved Problem Solving

Shabrina, P., Mostafavi, B., Abdelshiheed, M., Chi, M., & Barnes, T. (2023, August 21). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.

TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering

INFORMATION SCIENCES, 621, 611–626.

Time-aware deep reinforcement learning with multi-temporal abstraction

Kim, Y. J., & Chi, M. (2023, March 25). APPLIED INTELLIGENCE.

Enhancing a student productivitymodel for adaptive problem-solving assistance

Maniktala, M., Chi, M., & Barnes, T. (2022, August 3). USER MODELING AND USER-ADAPTED INTERACTION.

Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer

ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I, Vol. 13355, pp. 546–552.

Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction

2022 IEEE 10TH INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2022), pp. 151–162.

Student-Tutor Mixed-Initiative Decision-Making Supported by Deep Reinforcement Learning

ARTIFICIAL INTELLIGENCE IN EDUCATION, PT I, Vol. 13355, pp. 440–452.

The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.

A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students’ Self-Regulated Learning

Frontiers in Education, 6.

Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor (September, 10.1007/s40593-020-00213-3, 2020)

Maniktala, M., Cody, C., Barnes, T., & Chi, M. (2021, March). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 31, pp. 154–155.

Data to Donations: Towards In-Kind Food Donation Prediction across Two Coasts

2021 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC), pp. 281–288.

Evaluating Critical Reinforcement Learning Framework in the Field

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 215–227.

InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1337–1348.

Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8.

Multi-Temporal Abstraction with Time-Aware Deep Q-Learning for Septic Shock Prevention

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1657–1663.

Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 356–368.

The Impact of Looking Further Ahead: A Comparison of Two Data-driven Unsolicited Hint Types on Performance in an Intelligent Data-driven Logic Tutor

Cody, C., Maniktala, M., Lytle, N., Chi, M., & Barnes, T. (2021, May 21). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION.

To Reduce Healthcare Workload: Identify Critical Sepsis Progression Moments through Deep Reinforcement Learning

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1640–1646.

Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, Vol. 12975, pp. 521–537.

An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 64–73.

An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis

ADVANCED ANALYTICS AND LEARNING ON TEMPORAL DATA, AALTD 2019, Vol. 11986, pp. 213–228.

Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor

INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 30(4), 637–667.

Going deeper: Automatic short-answer grading by combining student and question models

USER MODELING AND USER-ADAPTED INTERACTION, 30(1), 51–80.

MuLan: Multilevel Language-based Representation Learning for Disease Progression Modeling

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), pp. 1246–1255.

PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems

MULTIMEDIA MODELING (MMM 2020), PT II, Vol. 11962, pp. 63–75.

Hierarchical Reinforcement Learning for Pedagogical Policy Induction

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, Vol. 11625, pp. 544–556.

Empirically Evaluating the Effectiveness of POMDP vs. MDP Towards the Pedagogical Strategies Induction

In Lecture Notes in Computer Science (pp. 327–331).

Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach

Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 43–51.

A Comparisons of BKT, RNN and LSTM for Learning Gain Prediction

In Lecture Notes in Computer Science (pp. 536–539).

A Comparisons of BKT, RNN and LSTM for Learning Gain Prediction

Artificial intelligence in education, aied 2017, 10331, 536–539.

LSTM for septic shock: Adding unreliable labels to reliable predictions

2017 IEEE International Conference on Big Data (Big Data), 1233–1242.

A comparison of two methods of active learning in physics: inventing a general solution versus compare and contrast

INSTRUCTIONAL SCIENCE, 44(2), 177–195.

An analysis of feature selection and reward function for model-based reinforcement learning

Intelligent tutoring systems, its 2016, 0684, 504–505.

Evolving augmented graph grammars for argument analysis

Proceedings of the 2016 Genetic and Evolutionary Computation Conference (GECCO'16 Companion), 65–66.

Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing

In Intelligent Tutoring Systems (pp. 208–218).

Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing

Intelligent tutoring systems, its 2016, 0684, 208–218.

Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor

In Lecture Notes in Computer Science (pp. 726–729).

Data-driven worked examples improve retention and completion in a logic tutor

Artificial intelligence in education, aied 2015, 9112, 726–729.

Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis

In Data and Applications Security and Privacy XXIX (pp. 170–187).

Detecting opinion spammer groups through community discovery and sentiment analysis

Data and applications security and privacy xxix, 9149, 170–187.

Can Diagrams Predict Essay Grades?

In Intelligent Tutoring Systems (pp. 260–265).

When Is Tutorial Dialogue More Effective Than Step-Based Tutoring?

In Intelligent Tutoring Systems (pp. 210–219).

When is tutorial dialogue more effective than step-based tutoring?

Intelligent tutoring systems, its 2014, 8474, 210–219.

Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies

User Modeling and User-Adapted Interaction, 21(1-2), 137–180.

Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics

In Intelligent Tutoring Systems (pp. 224–234).

Inducing Effective Pedagogical Strategies Using Learning Context Features

In User Modeling, Adaptation, and Personalization (pp. 147–158).

Eliminating the Gap between the High and Low Students through Meta-cognitive Strategy Instruction

In Intelligent Tutoring Systems (pp. 603–613).

Implicit Versus Explicit Learning of Strategies in a Non-procedural Cognitive Skill

In Intelligent Tutoring Systems (pp. 521–530).