A common challenge for students in heavy proof-based courses is to come up with a long sequence of logical arguments from the problem statement to the final solution. In doing so, they can often skip steps leading to logical leaps or downright incorrect solutions. Ideally the instructor should identify these mis-steps and help students master such proof-based course material. Here we want to take a data-driven approach to address this challenge.

By analyzing student homework solutions, we can potentially discover patterns of mistakes. If many students are making similar types of mistakes then that topic needs to be emphasized by the instructor. To process large amounts of student homework solutions the plan to use machine learning methods to both represent and discover patterns / clusters in student solutions.

As a case study and proof of concept, we plan to use student homeworks that were collected from Discrete Mathematics and Machine Learning courses.

This project is NOT accpeting application.

Faculty Advisor

  • Professor Nakul Verma
  • Department/School: IEOR/SEAS
  • Location: Morningside Campus

Project timeline

  • Earliest starting date: 03/01/2019
  • End date: 05/31/2019
  • Number of hours per week of research expected during Spring 2019: ~10

Candidate requirements

  • Skill sets: Machine Learning, Python programming with tensor flow experience, fluent in proof-based mathematics.
  • Student eligibility (as of Spring 2019): freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible