Brief Biography:

Min Chi is an Assistant Professor in the Department of Computer Science at NC State University. She joined the department in August 2013 as a Chancellor's Faculty Excellence Program cluster hire in the Digital Transformation of Education. She earned her B.E from Xi'an Jiaotong University, China and her M.S. and Ph.D. in Intelligent Systems Program from the University of Pittsburgh (Advisor: Kurt VanLehn and Diane Litman ). She was a Post-Doctoral Fellow in the Machine Learning Department at Carnegie Mellon University (Advisors:Geoff Gordon (Machine Learning Department) and Kenneth R. Koedinger (Human Computer Interaction)), and in the Human Sciences and the Technologies Advanced Research Institute at Stanford University (Advisor:Daniel Schwartz).

Research Interests & Areas:

  • Data-driven decision making using Reinforcement Learning

  • Temporal sequential mining through deep learning

  • Building robust, intelligent real world systems for education, health care, and food bank.


Awards:

  • NSF CAREER Award, National Science Foundation, (2017).

  • Outstanding Paper Award (2016). The 24th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP)

  • Best Paper Award (2015). Data and Applications Security and Privacy XXIX. 29th Annual Working Conference, DBSec2015.

  • Best Paper Award (2010). Tenth International Conference on Intelligent Tutoring Systems (ITS2010).

  • James Chen Best Student Paper Award (2010), Eighteenth International Conference on User Modeling, Adaptation, and Personalization (UMAP2010)

  • Best Student Paper Award (2008). Ninth International Conference on Intelligent Tutoring Systems (ITS2008).

  • Best Poster Award (2008), First International Conference on Educational Data Mining (EDM2008).

  • Mellon Fellowship, 2008-2009, University of Pittsburgh.

Publications (student authors are underlined):

  • Yuan Zhang, Chen Lin, and M. Chi, Julie Ivy, Muge Capan, and Jean Huddleston (2017) LSTM for Septic Shock: Adding Unreliable Labels to Reliable Predictions. In: the IEEE Big Data 2017 conference (BigData17) (Accepted rate: 79/437 = 18%) (pdf)

  • Guojing Zhou, Jianxun Wang, Collin Lynch, and M. Chi (2017) Towards Closing the Loop: Bridging Machine-induced Pedagogical Policies to Learning Theories. In the Proceedings of the 10th International Conference on Educational Data Mining (EDM2017), pp.112-119. (pdf)
    • [Best Paper Final List].

  • Choo, E., T. Yu, M. Chi (2017). Detecting Opinion Spammer Groups and Spam Targets through Community Discovery and Sentiment Analysis. Journal of Computer Security. 25(3) pp.283-318. (pdf)

  • Shitian Shen and M. Chi (2017) Clustering Student Sequential Trajectories Using Dynamic Time Warping. In the Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017), pp.266-271. (pdf)

  • Linting Xue, Collin Lynch, and M. Chi (2017) Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection. In the Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017), pp.296-301. (pdf)

  • Guojing Zhou and M. Chi (2017) The Impact of Decision Agency & Granularity on Aptitude Treatment Interaction in Tutoring. In: the Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci2017). pp.3652-3657. (pdf)

  • Chen Lin and M. Chi (2017) A Comparison of BKT, RNN and LSTM for Learning Gain Prediction. In: the 18th International Conference on Artificial Intelligence in Education (AIED 2017), pp.536-539 (pdf)

  • Doris B. Chin, M. Chi, and Daniel L. Schwartz (2016) A Comparison of Two Methods of Active Learning in Physics: Inventing a General Solution versus Compare and Contrast. Instructional Science. 44, pp.177-195 (pdf)

  • Shitian Shen and M. Chi (2016) Reinforcement Learning: the Sooner the Better or the Later the Better? The 24th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP2016) pp:507-512. (Full Paper, Accepted rate: 21/88 = 23.8%) (pdf)
    • [Outstanding Paper Award].

  • Chen Lin and M. Chi (2016) Incorporating Student Response Time and Tutor Instructional Interventions into Student Modeling The 24th ACM International Conference on User Modeling, Adaptation and Personalization (UMAP2016), pp.157-161. (Accepted rate: 34/123 = 27.6%) (pdf)

  • Zhou, G., Lynch, C., Price, T., Barnes, T., and M. Chi (2016) The Impact of Granularity on the Effectiveness of Students' Pedagogical Decision The 38th Annual Conference of the Cognitive Science Society (CogSci 2016) pp:2801-2806. (Oral Presentation, acceptance rate 222/666 = 34%) (pdf)

  • Linting Xue, Collin Lynch and M. Chi (2016) Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams The 9th International Conference on Educational Data Mining (EDM2016) pp.255-262. (acceptance rate 30/109 = 27.5%) (pdf)

  • Shitian Shen and M. Chi (2016) Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning The 9th International Conference on Educational Data Mining (EDM2016) pp.507-512. (pdf)

  • Yuan Zhang, Rajat Shah and M. Chi (2016) Deep Learning + Student Modeling + Clustering: a Recipe for Effective Automatic Short Answer Grading The 9th International Conference on Educational Data Mining (EDM2016) pp. 562-568. (pdf)

  • Chen Lin and M. Chi (2016) Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing In: Proceedings of the 13th International Conference on 13th International Conference of Intelligent Tutoring Systems (ITS) pp. 208-218. (Oral Presentation, acceptance rate 15%) (pdf)

  • Shitian Shen, Chen Lin, Behrooz Mostafavi, Tiffany Barnes and M. Chi (2016) An Analysis of Feature Selection and Reward Function for Model-Based Reinforcement Learning In: Proceedings of the 13th International Conference on 13th International Conference of Intelligent Tutoring Systems (ITS) pp. 504-505 (pdf)

  • Linting Xue, Collin Lynch, and M. Chi (2016) Evolving Augmented Graph Grammars for Argument Analysis In: The Genetic and Evolutionary Computation Conference (GECCO 2016) pp.65-66. (pdf)

  • Choo, E., T. Yu, M. Chi (2015). Detecting Opinion Spammer Groups through Community Discovery and Sentiment Analysis. Data and Applications Security and Privacy XXIX. Springer International Publishing, 2015. 170-187 (pdf)
    • [Best Paper Award].

  • Zhou, G., Price, T. W., Lynch, C., Barnes, T., & Chi, M. (2015). The Impact of Granularity on Worked Examples and Problem Solving. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015. 2817-2822. (Oral Presentation) (pdf)

  • B. Mostafavi, G. Zhou, C. F. Lynch, M. Chi, and T. Barnes. (2015). Data-driven Worked Examples Improve Retention and Completion in a Logic Tutor In: Proceedings of the 17th International Conference on Artificial Intelligence in Education (AIED 2015) 2015, pp. 726-729 (pdf)

  • T. W. Price, C. F. Lynch, T. Barnes, and M. Chi,. (2015). An Improved Data-Driven Hint Selection Algorithm for Probability Tutors. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015). 2015. pp 610-611 (pdf)

  • C. F. Lynch, T. W. Price, M. Chi, and T. Barnes. (2015). Using the Hint Factory to Analyze Model-Based Tutoring Systems Proceedings of the Second International Workshop on Graph-Based Educational Data Mining (GEDM 2015) (pdf)

  • M. Chi, Daniel Schwartz, Kristen Pilner Blair and Doris B. Chin (2014). Choice-based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores? Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining. 36-43 (pdf) (Acceptance Rate: 16.9%)

  • Choo, E., T. Yu, M. Chi, and Y. L. Sun (2014). Revealing Implicit Communities to Incorporate into Recommender Systems. 15th ACM Conference on Economics and Computation. Palo Alto, CA. (pdf) (Acceptance Rate: 80/290=27.5%)

  • M. Chi P. Jordan, and K. VanLehn (2014). When is Tutorial Dialogue More Effective Than Step-based Tutoring? In Trausan-Matu, Stefan, Boyer, Kristy E., Crosby, Martha, Panourigia, Kitty Intelligent Tutoring Systems, 12th International Conference, ITS 2014, Berlin: Springer, 210-219 (pdf) (Acceptance Rate: 17.5%)

  • Lynch, C., K. D. Ashley, and M. Chi (2014) Can Diagrams Predict Essays? In Trausan-Matu, Stefan, Boyer, Kristy E., Crosby, Martha, Panourigia, Kitty Intelligent Tutoring Systems, 12th International Conference, ITS 2014, Berlin: Springer, 260-265 (pdf)

  • Hallinen, N. R., J. Cheng, M. Chi D. L. Schwartz (2014). Tug of War – What is it Good For? Measuring Student Inquiry Choices in an Online Science Game. Proceedings of International Conference of the Learning Sciences (ICLS), Boulder, CO

  • M. Chi I. Dohmen, J. T. Shemwell, D. B. Chin, C. C. Chase, and D. L. Schwartz (2012). Seeing the Forest from the Trees: A Comparison of Two Instructional Models Using Contrasting Cases. Proceedings of the American Educational Research Association 2012 Annual Meeting (AERA). Vancouver, British Columbia, Canada. (pdf)

  • Hallinen, N.R., M. Chi, Chin, D.B., Prempeh, J., Blair, K.P. & Schwartz, D.L. (2012). Applying Cognitive Developmental Psychology to Middle School Physics Learning: The Rule Assessment Method. Physics Education Research (PER) Conference, Philadelphia, PA.

  • M. Chi, Chin, D.B., Hallinen, N.R, & Schwartz, D.L. (2012). A Comparison of Two Instructional Models Using Contrasting Cases. Physics Education Research (PER) Conference, Philadelphia, PA.

  • M. Chi, VanLehn, K, Litman, D. & Jordan, P. (2011). An evaluation of pedagogical tutorial tactics for a natural language tutoring system: A reinforcement learning approach. International Journal of Artificial Intelligence in Education (IJAIED), 21, 1-2, pp. 83-113. (pdf)

  • M. Chi, VanLehn, K, Litman, D. & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical tactics. User Modeling and User Adapted Instruction (UMUAI), 21, 1-2, pp. 137-180. (pdf)

  • M. Chi, K. R. Koedinger, G. Gordon, P. W. Jordan, and K. VanLehn (2011). Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, July 6-8, 2011. Ed. by M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. C. Stamper. www.educationaldatamining.org, pp. 61-70. ISBN: 978-90-386-2537-9. (pdf)

  • Gowda, S. M., J. P. Rowe, R. S. J. de Baker, M. Chi, and K. R. Koedinger (2011). Improving Models of Slipping, Guessing, and Moment-By-Moment Learning with Estimates of Skill Difficulty” Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, July 6-8, 2011. Ed. by M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, and J. C. Stamper. www.educationaldatamining.org, pp. 199-208. ISBN: 978-90-386-2537-9. (pdf)

  • Chi, M., Vanlehn, K., Litman, D (2010). The More the Merrier? Examining Three Interaction Hypotheses. In S. Ohlsson & R. Catrambone (Eds.) Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp 2870-2875), Austin, TX: Cognitive Science Society. (pdf)

  • Chi, M. VanLehn, K., and Litman, D. (2010). Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics. Proceedings 10th International Conference on Intelligent Tutoring Systems (ITS2010) (pp 224-234). (pdf)
    • [Best Paper Award].

  • Chi, M. VanLehn, K. Litman, D., and Jordan, P. (2010). Inducing Effective Pedagogical Strategies Using Learning Context Features. Proceedings Eighteenth International Conference on User Modeling, Adaptation, and Personalization (UMAP2010). (pp 147-158). (pdf)
    • [James Chen Best Student Paper Award].

  • Chi, M. & VanLehn, K. (2010). Meta-cognitive strategy instruction in intelligent tutoring systems: How, when, and why. Journal of Educational Technology and Society, 13(1), 25-39. (pdf)

  • Chi, M. & Jordan, P. VanLehn, K & Litman, D. (2009). To elicit or to tell: Does it matter? In Vania Dimitrova, Riichiro Mizoguchi,Benedict du Boulay and Arthur C. Graesser (Eds). Proceedings of the 14th International Conference on Artificial Intelligence in Education, (pp 197-204).: IOS Press. (pdf)

  • Chi, M. & VanLehn, K. (2008). Eliminating the gap between the high and low students through meta-cognitive strategy instruction. In B. P. Woolf, E. Aimeur, R. Nkambou & S. Lajoie (Eds). Proceedings of the 9th International Conference on Intelligent Tutoring Systems, (pp 603-613). Amsterdam: IOS Press. (pdf)
    • [Best Student Paper Award].

  • Chi, M., Jordan, P., VanLehn, K., & Hall, M. (2008). Reinforcement learning-based feature selection for developing pedagogically effective tutorial dialogue tactics. In R.S.J.d. Baker, T. Barnes, J.E. Beck (Eds.) Proceedings of the 1st International Conference on Educational Data Mining. (pp 258-265), Montreal, Canada. (pdf)

  • Chi, M. & VanLehn, K. (2007) The impact of explicit strategy instruction on problem solving behaviors across intelligent tutoring systems. In D. McNamara & G. Trafton (Eds.) Proceedings of the 29th Annual Conference of the Cognitive Science Society. (pp.167-172). Mahwah, NJ: Erlbaum. (pdf)

  • Chi, M.& VanLehn, K. (2007) Accelerated future learning via explicit instruction of a problem solving strategy. In R. Luckin, K. R. Koedinger & J. Greer (Eds.) Proceedings of the 13th International Conference on Artificial Intelligence in Education. (pp. 409-416). Amsterdam, Netherlands: IOS Press. (pdf)

  • Chi, M. & VanLehn, K. (2007) Domain-specific and domain-independent interactive behaviors in Andes. In R. Luckin, K. R. Koedinger & J. Greer (Eds.) Proceedings of the 13th International Conference on Artificial Intelligence in Education. (pp. 548-550). Amsterdam, Netherlands: IOS Press. (pdf)

  • Chi, M. & VanLehn, K. (2007) Porting an intelligent tutoring system across domains. In R. Luckin, K. R. Koedinger & J. Greer (Eds.) Proceedings of the 13th International Conference on Artificial Intelligence in Education. (pp. 551-553). Amsterdam, Netherlands: IOS Press. (pdf)

  • VanLehn, K., Bhembe, D., Chi, M., Lynch, C., Schulze, K., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M. (2004). Implicit versus explicit learning of strategies in a non-procedural cognitive skill. In J. C. Lester, R. M. Vicari, & F. Paraguacu, (Eds.), Proceedings of the 7th Conference on Intelligent Tutoring Systems. (pp. 521-530). Berlin: Springer-Verlag Berlin & Heidelberg GmbH & Co. K. (pdf)


Active Projects:

  • Integrated Data-driven Technologies for Individualized Instruction in STEM Learning Environments, National Science Foundation
    PI: Min Chi Co-PI: Tiffany Barnes
    Dates: 03/1/2017 - 02/28/2022
    Summary: This project will develop hierarchical data-driven, interpretable, and robust models that optimize human learning. Moreover, it will investigate whether integrating hierarchical data-driven agent decision-making with user-initiated decisions can help students learn to make better decisions for their learning.

  • FEEED: Flexible, Equitable, Effective and Efficient Distribution, National Science Foundatio
    PI: Lauren Davis (NC A&T State University) , Co-investigator: Julie Ivy (IE, NCSU-PI) and Min Chi (NCSU)
    Dates: 08/1/2017 - 07/31/2020
    Summary: This project develops a smart service system to assist hunger relief organizations, like food banks, in the Flexible, Equitable, Efficient, and Effective Distribution (FEEED) of food to those in need. It will synthesize data from various sources to automatically predict, visualize, learn from decision maker's actions, and identify strategies to advance operational effectiveness of food collection, distribution, and resource management and fundamentally transform the way food banks operate.

  • Using Real-Time Multichannel Self-Regulated Learning Data to Enhance Student Learning and Teachers' Decision-Making with MetaDash, National Science Foundation
    PI: Roger Azevedo (Psychology, NCSU) Co-PI: Soonhye Park (Science Education, NCSU) and Min Chi
    Dates: 04/1/2017 - 03/31/2020
    Summary: The research approach investigates (1) how and when to present the multi-channel input based on human-computer-interaction design principles and informed by teacher usability studies; (2) how to optimize statistical approaches to handle unstructured data from multiple sources; and (3) how to create behavioral signatures for constructs such as self-regulation, motivation and frustration using multi-modal measures such as eye-tracking and facial expression.

  • CAREER: Improving Adaptive Decision Making in Interactive Learning Environments, National Science Foundation
    PI: Min Chi
    Dates: 03/1/2017 - 02/28/2022
    Summary: This project will 1) advance research on Reinforcement Learning by adapting it to make hierarchical decisions similar to those of human experts; 2) advance the understanding Reinforcement Learning algorithms by inducing compact policies that highlight key decisions; and 3) close the loop by supporting hybrid human-machine interactive decision making.

  • EXP: Data-driven support for novice programmers, National Science Foundation,
    PI: Tiffany Barnes, Co-PI: Min Chi
    Dates: 09/1/2016 - 08/31/2019
    Summary: Open-ended, media-rich visual programming environments such as Scratch and Snap represent the next-generation genre for engaging and inspiring students to learn programming. However, it is a particularly challenging area for data-driven support due to the extremely large potential solution spaces and the very creative embellishments that make them attractive. This project integrates intelligent student support that is derived from data into the media-rich, open-ended problem solving in the Snap programming environment.

  • SCH:INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System, National Science Foundation
    PI: Julie Ivy (IE), Co-PIs: Maria Mayorga, Osman Ozaltin, Min Chi
    Dates: 10/1/2015 - 09/30/2018
    Summary: The goal of this research is to integrate electronic health records (EHR), machine learning, data mining, and clinical expertise to provide an evidence-based framework to diagnose and accurately risk-stratify patients within the sepsis spectrum, and develop and validate intervention policies that inform sepsis treatment decisions.

  • Educational Data Mining for Individualized Instruction in STEM Learning Environments, National Science Foundation
    PI: Min Chi> Co-PIs: Tiffany M. Barnes
    Dates: 09/1/2014 - 08/31/2018
    Summary: This project will augment three existing e-learning interactive environments; more specifically, it will add data-driven techniques for the automatic generation of next-step hints and for the automatic selection of learning activities.

  • Recent Professional Activities:

    Executive Committee Member of the International Artificial Intelligence in Education (IJAIED) Society
    JEDM track Co-Chair, 11th International Conference on Educational Data Mining (EDM 2017)
    Program Co-Chair, 9th International Conference on Educational Data Mining (EDM 2016)
    Tutorials and Workshops Co-Chair, 12th International Conference on Intelligent Tutoring Systems (ITS 2014)
    Poster & Demo Co-Chair, 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014)
    Program Committee, International Conference on Intelligent Tutoring Systems (2010-present)
    Program Committee, International Conference on Artificial Intelligence in Education (2011-present)
    Program Committee, International Conference on User Modeling, Adaptation and Personalization (UMAP 2011-present)
    Program Committee, International Conference on Educational Data Mining (EDM 2011-present)
    Program Committee, International Workshop on Graph-based Educational Datamining (2014-present)



Contact:

Office: EB3 2407
North Carolina State University
Raleigh, North Carolina,
27695 USA

email: mchi AT ncsu dot edu
Phone: (919) 515-7825
Fax: 919-515-7896
Postal Mail: Department of Computer Science
EB2, Rm 3260, Box 8206
North Carolina State University
Raleigh, North Carolina,
27695-8206, USA