TY - JOUR
T1 - (Work in Progress) Associations of Problem-Solving Strategy Use, Metacognition, Confidence, and Performance in a Senior Engineering Review Course
AU - Taraban, Roman
AU - Khatib, Sheima
AU - Vaughn, Jacob
N1 - Publisher Copyright:
© American Society for Engineering Education, 2022.
PY - 2022/8/23
Y1 - 2022/8/23
N2 - Engineers are problem solvers. Models of successful problem solving in engineering include steps of choosing equations, monitoring, and evaluating problem solutions, and the combination of these steps into more complex problem-solving strategies. These are metacognitive processes because they require the solver to think about anticipated, ongoing, and final problem-solving steps. Although research has identified characteristic differences between novice and expert problem solvers, less is known about the more detailed ways in which students develop their problem-solving methods through experience. In this research project, we asked 1) Which problem-solving strategies do students report using, 2) Is strategy use correlated with level of metacognitive reflection and problem-solving confidence, 3) Is strategy use correlated with objective measures of problem-solving ability, and 4) Do problem-solving steps cluster into more complex strategies. Participants in the present study were 101 chemical engineering seniors who completed a 3-credit course that reviewed eight major Fundamentals of Engineering (FE) Chemical topics (i.e., material balances, energy balances, mass transfer, heat transfer, fluid mechanics, reaction engineering, material sciences, and process control) in order to prepare for taking the FE exam. At the conclusion of each topic, students responded to a number of survey questions, including 1) Briefly describe the problem-solving strategies you employed to solve the FE practice problems, 2) On a scale of 1-5, how reflective (metacognitive) were you when solving this assignment, and 3) On a scale of 1-5, indicate how confident you were after solving the [practice] problems. Additionally, students completed a mock FE examination as a measure of problem-solving ability. Qualitative data-analytic methods were used to code responses, which included: understanding the problem, choosing a method, constructing a mental model, checking work, and drawing on supplemental resources. The paper reports frequencies of specific strategy steps and strategy clusters, and correlations of problem-solving steps and more complex strategies with metacognitive reflection, confidence, and the FE exam. This study provides a detailed description of student cognitions and the ways in which those cognitions might cluster into more complicated networks of problem-solving steps. It takes a first step in understanding the relationships between metacognitive reflection, confidence, strategies, and problem-solving performance, in training engineering students.
AB - Engineers are problem solvers. Models of successful problem solving in engineering include steps of choosing equations, monitoring, and evaluating problem solutions, and the combination of these steps into more complex problem-solving strategies. These are metacognitive processes because they require the solver to think about anticipated, ongoing, and final problem-solving steps. Although research has identified characteristic differences between novice and expert problem solvers, less is known about the more detailed ways in which students develop their problem-solving methods through experience. In this research project, we asked 1) Which problem-solving strategies do students report using, 2) Is strategy use correlated with level of metacognitive reflection and problem-solving confidence, 3) Is strategy use correlated with objective measures of problem-solving ability, and 4) Do problem-solving steps cluster into more complex strategies. Participants in the present study were 101 chemical engineering seniors who completed a 3-credit course that reviewed eight major Fundamentals of Engineering (FE) Chemical topics (i.e., material balances, energy balances, mass transfer, heat transfer, fluid mechanics, reaction engineering, material sciences, and process control) in order to prepare for taking the FE exam. At the conclusion of each topic, students responded to a number of survey questions, including 1) Briefly describe the problem-solving strategies you employed to solve the FE practice problems, 2) On a scale of 1-5, how reflective (metacognitive) were you when solving this assignment, and 3) On a scale of 1-5, indicate how confident you were after solving the [practice] problems. Additionally, students completed a mock FE examination as a measure of problem-solving ability. Qualitative data-analytic methods were used to code responses, which included: understanding the problem, choosing a method, constructing a mental model, checking work, and drawing on supplemental resources. The paper reports frequencies of specific strategy steps and strategy clusters, and correlations of problem-solving steps and more complex strategies with metacognitive reflection, confidence, and the FE exam. This study provides a detailed description of student cognitions and the ways in which those cognitions might cluster into more complicated networks of problem-solving steps. It takes a first step in understanding the relationships between metacognitive reflection, confidence, strategies, and problem-solving performance, in training engineering students.
KW - Confidence
KW - Metacognition
KW - Problem-Solving Strategies
KW - Qualitative Data
KW - Skill Development
UR - http://www.scopus.com/inward/record.url?scp=85138233612&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85138233612
SN - 2153-5965
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
T2 - 129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022
Y2 - 26 June 2022 through 29 June 2022
ER -