Improving STEM Education via NLP, Visualization, and Mobile Interfaces


The degree and quality of interaction between students and instructors are critical factors for students’ engagement, retention, and learning outcomes across domains (National Research Council, 2012). Although many suggestions and innovations have been proposed, interactive engagement is still very limited between students and instructors. This is especially true for the introductory Science, Technology, Engineering and Math (STEM) courses at the undergraduate level since these courses are generally taught in lecture halls due to a large number of students enrolled (Mervis, 2013). The larger the class size, the less likely instructors are to employ best teaching practices that foster robust learning such as timely feedback and interactive learning activities (Cuseo, 2007). Recent developments in educational technology (i.e. Massive Open Online Courses, blended learning environments) and financial troubles in universities (i.e. budget cuts by states) make it safe to predict that the class size problem will only get worse both in traditional face-to-face and online classes. So how can we modify the passive nature of lectures and increase the interaction while actively involving both students and instructors in the learning process in these circumstances?

In order to address this problem, we present CourseMIRROR (Mobile In-situ Reflections and Review with Optimized Rubrics), a system that integrates Natural Language Processing (NLP) with a mobile application that prompts students to reflect as well as provide immediate and continuous feedback to instructors about the difficulties that their students encounter. By enhancing the student reflection and instructor feedback cycle with technological tools, this project will incorporate three lines of research: 1) role of students’ reflection and instructor’s feedback on students’ retention and learning outcomes; 2) effectiveness and reliability of NLP to summarize written responses in a meaningful way; and 3) value and design of mobile technologies to improve retention and learning in STEM domains.

Classroom Adoptions

  • CS2001, University of Pittsburgh, Fall 2014
  • CS2610, University of Pittsburgh, Fall 2014
  • PHYS0175, University of Pittsburgh, Spring 2015
  • IE256, Bogazici University, Spring 2015
  • IE312, Bogazici University, Fall 2015
  • CS0401, University of Pittsburgh, Spring 2016
  • CS1635, University of Pittsburgh, Spring 2016
  • IE256, Bogazici University, Spring 2016
  • MATH125, Thiel College, Fall 2016
  • CS2610, University of Pittsburgh, Fall 2016
  • ENGR132, Purdue University, Spring 2017
  • PSY0422, University of Pittsburgh, Spring 2017


  • Fan, X., Scalable Teaching and Learning via Intelligent User Interfaces, Doctoral Dissertation, University of Pittsburgh, 2017 ( pdf )
  • Luo, W., Automatic Summarization for Student Reflective Responses, Doctoral Dissertation, University of Pittsburgh, 2017 ( pdf )
  • Fan, X., Luo, W., Menekse, M., Litman, D., Wang, J., Scaling Reflection Prompts in Large Classrooms via Mobile Interfaces and Natural Language Processing, Proceedings of 22nd ACM Conference on Intelligent User Interfaces (IUI 2017), Limassol, Cyprus, March 13 – 16, 2017 ( pdf ) Recommended
  • Luo, W., Liu, F., Liu, Z., Litman, D., Automatic Summarization of Student Course Feedback, In Proceedings of the 15th Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT 2016), San Diego, CA, June 2016 ( pdf )
  • Luo, W., Litman, D., Determining the Quality of a Student Reflective Response, In Proceedings 29th International FLAIRS Conference (Flairs 2016) . Key Largo, FL, May, 2016 ( pdf )
  • Luo, W., Litman, D., Summarizing Student Responses to Reflection Prompts, In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2015) . Lisboa, Portugal, September 17 – 21, 2015 ( pdf )
  • Luo, W., Fan, X., Menekse, M., Wang, J., Litman, D., Enhancing Instructor-Student and Student-Student Interactions with Mobile Interfaces and Summarization, Demo Paper, Conference of North American Association for Computational Linguistics (NAACL HLT 2015), Denver, CO, May 30 – June 5, 2015 ( pdf )
  • Fan, X., Luo, W., Menekse, M., Litman, D., Wang, J., CourseMIRROR: Enhancing Large Classroom Instructor-Student Interactions via Mobile Interfaces and Natural Language Processing, Works-In-Progress, ACM Conference on Human Factors in Computing Systems (CHI 2015), Seoul, Korea, April 18 – 23, 2015 ( pdf )