Articles with public access mandates - Min ChiLearn more
Not available anywhere: 8
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation
Incorporating student response time and tutor instructional interventions into student modeling
C Lin, S Shen, M Chi
Proceedings of the 2016 Conference on user modeling adaptation and …, 2016
Mandates: US National Science Foundation
Mulan: Multilevel language-based representation learning for disease progression modeling
H Sohn, K Park, M Chi
2020 IEEE International Conference on Big Data (Big Data), 1246-1255, 2020
Mandates: US National Science Foundation
Exploring the effect of autoencoder based feature learning for a deep reinforcement learning policy for providing proactive help
N Alam, B Mostafavi, M Chi, T Barnes
International Conference on Artificial Intelligence in Education, 278-283, 2023
Mandates: US National Science Foundation
Impact of Learning a Subgoal-Directed Problem-Solving Strategy Within an Intelligent Logic Tutor
P Shabrina, B Mostafavi, M Chi, T Barnes
International Conference on Artificial Intelligence in Education, 389-400, 2023
Mandates: US National Science Foundation
Multi-temporal abstraction with time-aware deep q-learning for septic shock prevention
YJ Kim, MS Ausin, M Chi
2021 IEEE International Conference on Big Data (Big Data), 1657-1663, 2021
Mandates: US National Science Foundation
A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics
MS Ausin, M Abdelshiheed, T Barnes, M Chi
International Conference on Artificial Intelligence in Education, 599-605, 2023
Mandates: US National Science Foundation
Available somewhere: 70
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
Mandates: US National Science Foundation
ATTAIN: Attention-based time-aware LSTM networks for disease progression modeling.
Y Zhang
In Proceedings of the 28th International Joint Conference on Artificial …, 2019
Mandates: US National Science Foundation
Temporal Belief Memory: Imputing Missing Data during RNN Training.
YJ Kim, M Chi
In Proceedings of the 27th International Joint Conference on Artificial …, 2018
Mandates: US National Science Foundation
Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions.
Y Mao
Journal of educational data mining 10 (2), 2018
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Institute of Education Sciences
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
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation
Intervention-bkt: incorporating instructional interventions into bayesian knowledge tracing
C Lin, M Chi
Intelligent Tutoring Systems: 13th International Conference, ITS 2016 …, 2016
Mandates: US National Science Foundation
A theoretical and evidence-based conceptual design of metadash: An intelligent teacher dashboard to support teachers' decision making and students’ self-regulated learning
MD Wiedbusch, V Kite, X Yang, S Park, M Chi, M Taub, R Azevedo
Frontiers in education 6, 570229, 2021
Mandates: US National Science Foundation
Leveraging deep reinforcement learning for pedagogical policy induction in an intelligent tutoring system
MS Ausin
In: Proceedings of the 12th International Conference on Educational Data …, 2019
Mandates: US National Science Foundation
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