Min Chi
Cited by
Cited by
Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies
M Chi, K VanLehn, D Litman, P Jordan
User Modeling and User-Adapted Interaction 21, 137-180, 2011
Instructional factors analysis: A cognitive model for multiple instructional interventions
M Chi, KR Koedinger, GJ Gordon, P Jordon, K VanLahn
Carnegie Mellon University, 2011
Do micro-level tutorial decisions matter: Applying reinforcement learning to induce pedagogical tutorial tactics
M Chi, K VanLehn, D Litman
Intelligent Tutoring Systems: 10th International Conference, ITS 2010 …, 2010
Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM
C Lin, Y Zhang, J Ivy, M Capan, R Arnold, JM Huddleston, M Chi
2018 IEEE international conference on healthcare informatics (ICHI), 219-228, 2018
ATTAIN: Attention-based time-aware LSTM networks for disease progression modeling.
Y Zhang
In Proceedings of the 28th International Joint Conference on Artificial …, 2019
Meta-cognitive strategy instruction in intelligent tutoring systems: how, when, and why
M Chi, K VanLehn
Journal of Educational Technology & Society 13 (1), 25-39, 2010
An evaluation of pedagogical tutorial tactics for a natural language tutoring system: A reinforcement learning approach
M Chi, K VanLehn, D Litman, P Jordan
International Journal of Artificial Intelligence in Education 21 (1-2), 83-113, 2011
Temporal Belief Memory: Imputing Missing Data during RNN Training.
YJ Kim, M Chi
In Proceedings of the 27th International Joint Conference on Artificial …, 2018
Detecting opinion spammer groups through community discovery and sentiment analysis
E Choo, T Yu, M Chi
Data and Applications Security and Privacy XXIX: 29th Annual IFIP WG 11.3 …, 2015
Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions.
Y Mao
Journal of educational data mining 10 (2), 2018
Reinforcement learning: the sooner the better, or the later the better?
S Shen, M Chi
Proceedings of the 2016 conference on user modeling adaptation and …, 2016
Exploring the impact of worked examples in a novice programming environment
R Zhi, TW Price, S Marwan, A Milliken, T Barnes, M Chi
Proceedings of the 50th acm technical symposium on computer science …, 2019
Recent temporal pattern mining for septic shock early prediction
F Khoshnevisan, J Ivy, M Capan, R Arnold, J Huddleston, M Chi
2018 IEEE international conference on healthcare informatics (ICHI), 229-240, 2018
One minute is enough: Early prediction of student success and event-level difficulty during novice programming tasks
Y Mao
In: Proceedings of the 12th International Conference on Educational Data …, 2019
A comparison of two methods of active learning in physics: Inventing a general solution versus compare and contrast
DB Chin, M Chi, DL Schwartz
Instructional Science 44, 177-195, 2016
Intervention-bkt: incorporating instructional interventions into bayesian knowledge tracing
C Lin, M Chi
Intelligent Tutoring Systems: 13th International Conference, ITS 2016 …, 2016
Evaluating the effectiveness of parsons problems for block-based programming
R Zhi, M Chi, T Barnes, TW Price
Proceedings of the 2019 ACM Conference on International Computing Education …, 2019
Hierarchical reinforcement learning for pedagogical policy induction
G Zhou, H Azizsoltani, MS Ausin, T Barnes, M Chi
Artificial Intelligence in Education: 20th International Conference, AIED …, 2019
Lstm for septic shock: Adding unreliable labels to reliable predictions
Y Zhang, C Lin, M Chi, J Ivy, M Capan, JM Huddleston
2017 IEEE International Conference on Big Data (Big Data), 1233-1242, 2017
Towards Closing the Loop: Bridging Machine-Induced Pedagogical Policies to Learning Theories.
G Zhou, J Wang, CF Lynch, M Chi
International Educational Data Mining Society, 2017
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