Brief Biography:

Min Chi is an Assistant Professor in the Department of Computer Science at NC State University. Dr. Chi earned her Ph.D. and M.S. in the Intelligent System Program from the University of Pittsburgh. She was a Post Doctoral Fellow in the Machine Learning Department at Carnegie Mellon University, and Human Sciences and the Technologies Advanced Research Institute at Stanford University. Her research has focus on applying and developing various machine learning and data mining algorithms especially reinforcement learning to improve and understand human learning. It was mainly done mining various human-computer interactive sequential log files collected from when they interacting educational software such as Intelligent Tutoring Systems, Natural Language Dialogue tutoring systems. In recent years, she also expanded her research areas to other datasets including e-commerce (like Amazon) and healthcare data. Her research has been mainly funded by National Science Foundation(NSF).

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


Education & Professional Preparation:


Announcements:




  • I'm the Program Co-Chair for the 9th International Conference on Educational Data Mining (EDM 2016). Please consider submitting you paper to it.


Awards:

  • 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:

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

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

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

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

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

  • Shitian Shen and M. Chi (Accepted) Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning The 9th International Conference on Educational Data Mining (EDM2016) (Short Paper)

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

  • Chen Lin and M. Chi (Accepted) 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) (Full Paper)

  • Shitian Shen, Chen Lin, Behrooz Mostafavi, Tiffany Barnes and M. Chi (Accepted) 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) (Poster)

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

  • 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)


Recent Professional Activities:

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, 12th International Conference on Intelligent Tutoring Systems (ITS 2014)
Program Committee, 22nd Conference on User Modeling, Adaptation and Personalization (UMAP 2014)
Program Committee, 7th International Conference on Educational Data Mining (EDM 2014)
Program Committee, 1st International Workshop on Graph-based Educational Datamining (G-EDM 2014)
Program Committee, 6th International Conference on Educational Data Mining (EDM 2013)
Program Committee, 5th International Conference on Educational Data Mining (EDM 2012)
Program Committee, 4th International Conference on Educational Data Mining (EDM 2011)