2023 ASEE Annual Conference & Exposition

Work in Progress: Quantification of Problem-Complexity and Problem-Solving Skills with Directed Networks in a Sophomore Course in Mechanics of Materials

Presented at Work-in-Progress Session: Exploring Learning and Development in Engineering Courses

Assessing learners’ problem-solving skills, such as in a sophomore course in Mechanics of Materials (MoM), is critical to course and program accreditation related assessments. Assessments in a MoM course typically involve problems structured as a sequence of steps, each of which transforms data in a directed fashion toward numerical solutions, analysis inferences, or design decisions. Designing assessments to measure learners’ competency is another crucial and essential part of instructional design. From an instructional design perspective, there are challenges in quantifying the complexity of problems, while from the learners’ perspective, the difficulty experienced is not easily quantifiable. In this work-in-progress (WIP) paper, we will demonstrate the feasibility and utility of a quantifiable directed network representation of the sequence of steps in engineering problems in a MoM course. The network representation visually and numerically captures two aspects of problem-solving: concept knowledge and process knowledge. We report quantification of the complexity of an example problem and learners’ problem-solving competency by computing metrics for the directed network representations. Future work will focus on assessing the evolution of learners’ problem-solving competency, utility of the directed network representations in designing course assessments, supporting program assessment and accreditation, and its application in measuring learner’s metacognition.

Authors
  1. Dr. Radheshyam Tewari Michigan Technological University [biography]
  2. Dr. Aneet Dharmavaram Narendranath Michigan Technological University [biography]
  3. Dr. Jaclyn Johnson Michigan Technological University [biography]
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