Min Chi

Zhang, Y., Lin, C., & Chi, M. (2020). Going deeper: Automatic short-answer grading by combining student and question models. USER MODELING AND USER-ADAPTED INTERACTION, 30(1), 51–80. https://doi.org/10.1007/s11257-019-09251-6

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2019). Hierarchical Reinforcement Learning for Pedagogical Policy Induction. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, pp. 544–556. https://doi.org/10.1007/978-3-030-23204-7_45

Shen, S., Mostafavi, B., Lynch, C., Barnes, T., & Chi, M. (2018). Empirically Evaluating the Effectiveness of POMDP vs. MDP Towards the Pedagogical Strategies Induction. In Lecture Notes in Computer Science (pp. 327–331). https://doi.org/10.1007/978-3-319-93846-2_61

Shen, S., Ausin, M. S., Mostafavi, B., & Chi, M. (2018). 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. https://doi.org/10.1145/3209219.3209232

Lin, C., & Chi, M. (2017). A Comparisons of BKT, RNN and LSTM for Learning Gain Prediction. Artificial intelligence in education, aied 2017, 10331, 536–539.

Lin, C., & Chi, M. (2017). A Comparisons of BKT, RNN and LSTM for Learning Gain Prediction. In Lecture Notes in Computer Science (pp. 536–539). https://doi.org/10.1007/978-3-319-61425-0_58

Zhang, Y., Lin, C., Chi, M., Ivy, J., Capan, M., & Huddleston, J. M. (2017). LSTM for septic shock: Adding unreliable labels to reliable predictions. 2017 IEEE International Conference on Big Data (Big Data), 1233–1242. https://doi.org/10.1109/bigdata.2017.8258049

Chin, D. B., Chi, M., & Schwartz, D. L. (2016). A comparison of two methods of active learning in physics: Inventing a general solution versus compare and contrast. Instructional Science, 44(2), 177–195. https://doi.org/10.1007/s11251-016-9374-0

Shen, S. T., Lin, C., Mostafavi, B., Barnes, T., & Chi, M. (2016). An analysis of feature selection and reward function for model-based reinforcement learning. Intelligent tutoring systems, its 2016, 0684, 504–505.

Lynch, C. F., Xue, L. T., & Chi, M. (2016). Evolving augmented graph grammars for argument analysis. Proceedings of the 2016 Genetic and Evolutionary Computation Conference (GECCO'16 Companion), 65–66. https://doi.org/10.1145/2908961.2908994

Lin, C., & Chi, M. (2016). Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing. In Intelligent Tutoring Systems (pp. 208–218). https://doi.org/10.1007/978-3-319-39583-8_20

Lin, C., & Chi, M. (2016). Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing. Intelligent tutoring systems, its 2016, 0684, 208–218.

Mostafavi, B., Zhou, G., Lynch, C., Chi, M., & Barnes, T. (2015). Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor. In Lecture Notes in Computer Science (pp. 726–729). https://doi.org/10.1007/978-3-319-19773-9_102

Mostafavi, B., Zhou, G. J., Lynch, C., Chi, M., & Barnes, T. (2015). Data-driven worked examples improve retention and completion in a logic tutor. Artificial intelligence in education, aied 2015, 9112, 726–729.

Choo, E., Yu, T., & Chi, M. (2015). Detecting Opinion Spammer Groups Through Community Discovery and Sentiment Analysis. In Data and Applications Security and Privacy XXIX (pp. 170–187). https://doi.org/10.1007/978-3-319-20810-7_11

Choo, E., Yu, T., & Chi, M. (2015). Detecting opinion spammer groups through community discovery and sentiment analysis. Data and applications security and privacy xxix, 9149, 170–187.

Lynch, C. F., Ashley, K. D., & Chi, M. (2014). Can Diagrams Predict Essay Grades? In Intelligent Tutoring Systems (pp. 260–265). https://doi.org/10.1007/978-3-319-07221-0_32

Chi, M., Jordan, P., & VanLehn, K. (2014). When Is Tutorial Dialogue More Effective Than Step-Based Tutoring? In Intelligent Tutoring Systems (pp. 210–219). https://doi.org/10.1007/978-3-319-07221-0_25

Chi, M., Jordan, P., & VanLehn, K. (2014). When is tutorial dialogue more effective than step-based tutoring? Intelligent tutoring systems, its 2014, 8474, 210–219.

Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). 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. https://doi.org/10.1007/S11257-010-9093-1

Chi, M., VanLehn, K., & Litman, D. (2010). Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics. In Intelligent Tutoring Systems (pp. 224–234). https://doi.org/10.1007/978-3-642-13388-6_27

Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2010). Inducing Effective Pedagogical Strategies Using Learning Context Features. In User Modeling, Adaptation, and Personalization (pp. 147–158). https://doi.org/10.1007/978-3-642-13470-8_15

Chi, M., & VanLehn, K. (2008). Eliminating the Gap between the High and Low Students through Meta-cognitive Strategy Instruction. In Intelligent Tutoring Systems (pp. 603–613). https://doi.org/10.1007/978-3-540-69132-7_63

VanLehn, K., Bhembe, D., Chi, M., Lynch, C., Schulze, K., Shelby, R., … Wintersgill, M. (2004). Implicit Versus Explicit Learning of Strategies in a Non-procedural Cognitive Skill. In Intelligent Tutoring Systems (pp. 521–530). https://doi.org/10.1007/978-3-540-30139-4_49