An Approach to Understanding Problem Solving Using Multiple Solution Methods

Hao Li; Anette Hosoi

Download Paper | Permalink

Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Student Division (STDT) Technical Session 2: Student Success and Resources
Tagged Division: Student Division (STDT)
Page Count: 16
DOI: 10.18260/1-2--42612
Permanent URL: https://peer.asee.org/42612
Download Count: 162

Request a correction

Paper Authors

biography

Hao Li Massachusetts Institute of Technology

visit author page

Hao Li is currently a PhD student studying Mechanical Engineering at MIT. He earned his Bachelor's degree from Rice University.

visit author page

biography

Anette Hosoi Massachusetts Institute of Technology

visit author page

Anette (Peko) Hosoi is Associate Dean of Engineering and the Neil and Jane Pappalardo Professor of Mechanical Engineering, at MIT. She received her PhD in Physics from the University of Chicago and went on to become an NSF Postdoctoral Fellow in the MIT Department of Mathematics and at the Courant Institute, NYU. She is a leader in the study of the hydrodynamics of thin fluid films and in the nonlinear physical interaction of viscous fluids and deformable interfaces. Her work spans multiple disciplines including physics, biology and applied mathematics, and is being used, in collaboration with Schlumberger-Doll Research, Bluefin Robotics, and Boston Dynamics to guide the engineering design of robotic crawlers and other mechanisms.

Prof. Hosoi is an exceptional, innovative teacher and an inspiring mentor for women in engineering. She was awarded the Bose Award for Excellence in Teaching, and a MacVicar Fellowship. She is a recipient of the 3M Innovation Award and has held the Doherty Chair in Ocean Utilization at MIT. She is a Radcliffe Institute Fellow and a Fellow of the American Physical Society. Her research interests include fluid mechanics, bioinspired design and locomotion, with a focus on optimization of crawling gastropods, digging bivalves, swimming microorganisms and soft robotics. Prof. Hosoi is also an avid mountain biker and her passion for sports has led her to create MIT Sports Lab, a program that is designed to build an interconnected community of faculty, students, industry partners, alums and athletes who are dedicated to applying their technical expertise to advance the state-of-the-art in sports.

visit author page

Download Paper | Permalink

Abstract

One of the key challenges of Engineering Education is developing students’ ability to navigate and solve moderately- or ill-structured problems with multiple solution paths. Existing theoretical and conceptual frameworks can provide a basis for understanding this challenge. In order to solve ill-structured problems, students may need to improve their ability to carry out self-regulated learning in order to plan, execute, and reflect on their problem solving efforts. For example, they might need to develop metacognition in order to direct their thinking. In addition, a student may progress via development stages: at first, they might attempt only one method to solve a problem; next, a student may resort to a simpler method if their first attempt failed; finally, a student might first start with an estimation to get a general idea of the answer before moving to a more detailed solution plan.

An experiment was performed to illustrate the possibilities of student problem solving activity on such a problem. The subject population consisted of 72 undergraduate and graduate students recruited from an engineering-focused university on the U.S. East Coast. The participants were given a problem with a well-defined goal but no well-defined method. The results were coded to extract the method used and the approximate time used for each method. Student performance was compared against school year, the choice of method, and the number of methods used. No significant differences in performance were found between students in different years. However, it was found that students who either 1) used simpler methods (methods with lower solve time) or 2) used more than one method tended to perform better than average, though the results are not statistically significant.

After performing the experiment, we realized that there could exist an “optimal” number of solution methods (more than one but not infinitely large) that would maximize problem solving success. In order to better understand this, we performed a simulation to better understand the interplay between multiple method use and problem solving effectiveness in a time-limited scenario. Each solution method was modeled with a different solve time. It was found that whenever there existed sufficiently short methods for solving a problem, using multiple methods (i.e. switching methods) can improve the problem solving outcome. The optimal number of methods is dependent on the methods’ solve times. This result is consistent with the outcome of the experiment and suggests several teaching strategies an instructor can use: 1) teach students low solve time methods and 2) teach students to switch methods when needed and 3) teach students how to select favorable (low solve time) starting methods.

Citation
Format

Li, H., & Hosoi, A. (2023, June), An Approach to Understanding Problem Solving Using Multiple Solution Methods Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42612

TY - CPAPER
AB - One of the key challenges of Engineering Education is developing students’ ability to navigate and solve moderately- or ill-structured problems with multiple solution paths. Existing theoretical and conceptual frameworks can provide a basis for understanding this challenge. In order to solve ill-structured problems, students may need to improve their ability to carry out self-regulated learning in order to plan, execute, and reflect on their problem solving efforts. For example, they might need to develop metacognition in order to direct their thinking. In addition, a student may progress via development stages: at first, they might attempt only one method to solve a problem; next, a student may resort to a simpler method if their first attempt failed; finally, a student might first start with an estimation to get a general idea of the answer before moving to a more detailed solution plan.

An experiment was performed to illustrate the possibilities of student problem solving activity on such a problem. The subject population consisted of 72 undergraduate and graduate students recruited from an engineering-focused university on the U.S. East Coast. The participants were given a problem with a well-defined goal but no well-defined method. The results were coded to extract the method used and the approximate time used for each method. Student performance was compared against school year, the choice of method, and the number of methods used. No significant differences in performance were found between students in different years. However, it was found that students who either 1) used simpler methods (methods with lower solve time) or 2) used more than one method tended to perform better than average, though the results are not statistically significant.

After performing the experiment, we realized that there could exist an “optimal” number of solution methods (more than one but not infinitely large) that would maximize problem solving success. In order to better understand this, we performed a simulation to better understand the interplay between multiple method use and problem solving effectiveness in a time-limited scenario. Each solution method was modeled with a different solve time. It was found that whenever there existed sufficiently short methods for solving a problem, using multiple methods (i.e. switching methods) can improve the problem solving outcome. The optimal number of methods is dependent on the methods’ solve times. This result is consistent with the outcome of the experiment and suggests several teaching strategies an instructor can use: 1) teach students low solve time methods and 2) teach students to switch methods when needed and 3) teach students how to select favorable (low solve time) starting methods.

AU - Hao Li
AU - Anette Hosoi
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - An Approach to Understanding Problem Solving Using Multiple Solution Methods
UR - https://peer.asee.org/42612
DO - 10.18260/1-2--42612
ER -