Chakraborty, Sabyasachi (2018-12). A Novel Methodology for Creating Auto Generated Spring-Based Truss Problems Through Mechanix. Master's Thesis. Thesis uri icon

abstract

  • Software that provides automated teaching assistance and instantaneous feedback for students has revolutionized the modern classroom. In addition to helping instructors manage large classes, the interactive experience can also benefit students. For instance, several existing systems incorporate recognition of student's hand-drawn solutions to problems. In these cases, the instructor sketches the solution to the problem and the student's sketches are expected to match this template. While this framework provides immediate feedback to students, it is still a constraint on instructors' time; additionally, it can be difficult to test conceptual understanding through only a small number of problems. There remains a strong need to generate questions automatically based on templates drawn by instructors so as to promote greater customization and variation in problems for students. The focus of our research is to develop a novel method that can automatically generate new valid problems from a given reference problem. We have chosen linear spring-based truss systems as our domain. Another outcome of our research is to develop a method for recognizing a spring network sketched naturally by the user with commonly used symbols. We also generate different types of questions and boundary conditions from the recognized and auto-generated truss structures using the finite element method (FEM) in a novel way. Our system has been integrated with Mechanix, a tool developed at Texas A&M University which supports free body diagrams (FBDs) and the creative design of truss structures. Mechanix supports engineering learning by providing intelligent and immediate feedback on hand-drawn sketches, and it has already been actively deployed in a number of university classrooms. We build a problem generator on top of Mechanix to leverage its capabilities for instantaneous, personalized feedback while enabling more thorough testing of student abilities and providing them a limitless pool of practice problems.
  • Software that provides automated teaching assistance and instantaneous feedback for
    students has revolutionized the modern classroom. In addition to helping instructors manage
    large classes, the interactive experience can also benefit students. For instance, several
    existing systems incorporate recognition of student's hand-drawn solutions to problems.
    In these cases, the instructor sketches the solution to the problem and the student's sketches
    are expected to match this template. While this framework provides immediate feedback
    to students, it is still a constraint on instructors' time; additionally, it can be difficult to
    test conceptual understanding through only a small number of problems. There remains a
    strong need to generate questions automatically based on templates drawn by instructors
    so as to promote greater customization and variation in problems for students.
    The focus of our research is to develop a novel method that can automatically generate
    new valid problems from a given reference problem. We have chosen linear spring-based
    truss systems as our domain. Another outcome of our research is to develop a method for
    recognizing a spring network sketched naturally by the user with commonly used symbols.
    We also generate different types of questions and boundary conditions from the recognized
    and auto-generated truss structures using the finite element method (FEM) in a novel way.
    Our system has been integrated with Mechanix, a tool developed at Texas A&M University
    which supports free body diagrams (FBDs) and the creative design of truss structures.
    Mechanix supports engineering learning by providing intelligent and immediate
    feedback on hand-drawn sketches, and it has already been actively deployed in a number
    of university classrooms. We build a problem generator on top of Mechanix to leverage
    its capabilities for instantaneous, personalized feedback while enabling more thorough
    testing of student abilities and providing them a limitless pool of practice problems.

publication date

  • December 2018