| 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 | 181 | 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 | 119 | 2011 |
| 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 | 103 | 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 | 102 | 2019 |
| 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 | 99 | 2010 |
| 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 | 93 | 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 | 90 | 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 | 80 | 2018 |
| Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions. Y Mao Journal of educational data mining 10 (2), 2018 | 74 | 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 | 73 | 2015 |
| 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 | 61 | 2019 |
| 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 | 59 | 2016 |
| 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 | 54 | 2018 |
| 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 | 53 | 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 | 52 | 2019 |
| 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 | 52 | 2019 |
| Intervention-bkt: incorporating instructional interventions into bayesian knowledge tracing C Lin, M Chi Intelligent Tutoring Systems: 13th International Conference, ITS 2016 …, 2016 | 50 | 2016 |
| 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 | 45 | 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 | 38 | 2017 |
| 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 | 35 | 2021 |
Kurt VanLehnProfessor of Computer Science, Arizona State UniversityVerified email at asu.edu
