Works (24)

2020 journal article

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

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

By: Y. Zhang, C. Lin & M. Chi

Source: Web Of Science
Added: March 30, 2020

2019 article

Hierarchical Reinforcement Learning for Pedagogical Policy Induction

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

By: G. Zhou, H. Azizsoltani, M. Ausin, T. Barnes & M. Chi

Source: Web Of Science
Added: December 2, 2019

2018 chapter

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

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

By: S. Shen, B. Mostafavi, C. Lynch, T. Barnes & M. Chi

Source: Crossref
Added: February 24, 2020

2018 article

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.

By: S. Shen, M. Ausin, B. Mostafavi & M. Chi

Source: Web Of Science
Added: April 2, 2019

2017 conference paper

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

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

By: C. Lin & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

2017 chapter

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

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

By: C. Lin & M. Chi

Source: Crossref
Added: February 24, 2020

2017 conference paper

LSTM for septic shock: Adding unreliable labels to reliable predictions

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

By: Y. Zhang, C. Lin, M. Chi, J. Ivy, M. Capan & J. Huddleston

Source: NC State University Libraries
Added: August 6, 2018

2016 journal article

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

Instructional Science, 44(2), 177–195.

By: D. Chin, M. Chi & D. Schwartz

Source: NC State University Libraries
Added: August 6, 2018

2016 conference paper

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

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

Source: NC State University Libraries
Added: August 6, 2018

2016 conference paper

Evolving augmented graph grammars for argument analysis

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

By: C. Lynch, L. Xue & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

2016 chapter

Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing

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

By: C. Lin & M. Chi

Source: Crossref
Added: February 24, 2020

2016 conference paper

Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing

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

By: C. Lin & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

2015 chapter

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

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

By: B. Mostafavi, G. Zhou, C. Lynch, M. Chi & T. Barnes

Source: Crossref
Added: February 24, 2020

2015 conference paper

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

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

Source: NC State University Libraries
Added: August 6, 2018

2015 chapter

Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis

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

By: E. Choo, T. Yu & M. Chi

Source: Crossref
Added: February 24, 2020

2015 conference paper

Detecting opinion spammer groups through community discovery and sentiment analysis

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

By: E. Choo, T. Yu & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

2014 chapter

Can Diagrams Predict Essay Grades?

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

By: C. Lynch, K. Ashley & M. Chi

Source: Crossref
Added: August 28, 2020

2014 chapter

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

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

By: M. Chi, P. Jordan & K. VanLehn

Source: Crossref
Added: August 28, 2020

2014 conference paper

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

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

By: M. Chi, P. Jordan & K. VanLehn

Source: NC State University Libraries
Added: August 6, 2018

2011 journal article

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.

By: M. Chi, K. VanLehn, D. Litman & P. Jordan

Source: Crossref
Added: August 28, 2020

2010 chapter

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

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

By: M. Chi, K. VanLehn & D. Litman

Source: Crossref
Added: August 28, 2020

2010 chapter

Inducing Effective Pedagogical Strategies Using Learning Context Features

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

By: M. Chi, K. VanLehn, D. Litman & P. Jordan

Source: Crossref
Added: August 28, 2020

2008 chapter

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

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

By: M. Chi & K. VanLehn

Source: Crossref
Added: August 28, 2020

2004 chapter

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

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

By: K. VanLehn, D. Bhembe, M. Chi, C. Lynch, K. Schulze, R. Shelby, L. Taylor, D. Treacy, A. Weinstein, M. Wintersgill

Source: Crossref
Added: August 28, 2020