Using Cognitive Task Analysis to Observe the Use of Intuition in Engineering Problem Solving

Natalie Ugenti; Joselyn Elisabeth Busato; Elif Miskioglu; Kaela M. Martin

Download Paper | Permalink

Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Educational Research and Methods Division (ERM) Technical Session 5
Tagged Division: Educational Research and Methods Division (ERM)
Page Count: 7
DOI: 10.18260/1-2--48229
Permanent URL: https://peer.asee.org/48229
Download Count: 2

Paper Authors

biography

Natalie Ugenti Bucknell University

visit author page

Natalie Ugenti is a Senior Chemical Engineering student at Bucknell University

visit author page

biography

Joselyn Elisabeth Busato Bucknell University

visit author page

Joselyn Busato is an undergraduate student at Bucknell University, majoring in creative writing and biology.

visit author page

biography

Elif Miskioglu Bucknell University

visit author page

Dr. Elif Miskioglu is an early-career engineering education scholar and educator. She holds a B.S. ˘
in Chemical Engineering (with Genetics minor) from Iowa State University, and an M.S. and Ph.D. in
Chemical Engineering from Ohio State University. Her early Ph.D. work focused on the development of
bacterial biosensors capable of screening pesticides for specifically targeting the malaria vector mosquito,
Anopheles gambiae. As a result, her diverse background also includes experience in infectious disease
and epidemiology, providing crucial exposure to the broader context of engineering problems and their
subsequent solutions. These diverse experiences and a growing passion for improving engineering education prompted Dr. Miskioglu to change her career path and become a scholar of engineering education. ˘
As an educator, she is committed to challenging her students to uncover new perspectives and dig deeper
into the context of the societal problems engineering is intended to solve. As a scholar, she seeks to not
only contribute original theoretical research to the field, but work to bridge the theory-to-practice gap in
engineering education by serving as an ambassador for empirically driven educational
practices.

visit author page

biography

Kaela M. Martin Embry-Riddle Aeronautical University, Prescott orcid.org/0000-0002-2359-6332

visit author page

Kaela Martin is an Associate Professor and Associate Department Chair of Aerospace Engineering at Embry-Riddle Aeronautical University, Prescott Campus. She graduated from Purdue University with a PhD in Aeronautical and Astronautical Engineering. Her research interests in engineering education include developing classroom interventions that improve student learning, designing experiences to further the development of students from novices to experts, and creating engaging classroom experiences.

visit author page

Download Paper | Permalink

Abstract

This work-in-progress research paper describes the pilot work in a study seeking to gain further insight on the relationships between intuition, expertise, and experience through a better understanding of how intuition is applied in engineering problem solving. Individuals who have attained a high level of expertise, exhibit characteristics of intuitive decision making (Dreyfus & Dreyfus, 1980). The development of expertise (Dreyfus & Dreyfus, 1980; Seifert et al., 1997) and intuition (Authors, 2019; Authors, 2023) are heavily influenced by experience. Engineering intuition can be summarized as a subconscious problem-solving skill that is based on previous experience (Authors, 2023). In this work, we will be using Cognitive Task Analysis (CTA) to examine the use of intuition in engineering problem solving. CTA is a class of observational protocols that surface tacit knowledge through engaging experts with a task (Crandall, 2006). The purpose of CTA is to capture how the mind works through three primary aspects: knowledge elicitation, data analysis, and knowledge representation. As best CTA practices use multiple methods, we will use three methods for this analysis, 1) Simulation Interviews where participants are given a simulated engineering problem and asked to speak out loud to describe their process in approaching the problem, 2) Critical Decision Method (Klein, 1989) where a retrospective interview probes the decisions made during the simulation interview, and 3) Knowledge Audit Method (Taheri et al., 2014) which further guides our probing questions to identify types of knowledge used, or not used, during the simulated problem solving experience. These three techniques are applied to collect data on participants' problem solving. To develop the problems for the Simulation Interviews, we have first conducted pilot work using just the Critical Decision Method and Knowledge Audit Method. As part of the Critical Decision Method, participants will select a non routine problem-solving incident, construct an incident timeline, identify decision points for future probing, and then probe these decisions using the Knowledge Audit Method. This method allows us to determine realistic, practice-based problems for the Simulation Interview, why the participant makes certain decisions, and how their educational background and on the job training influenced their decision making process. The anticipated outcomes of this research are to expand engineering education through a better understanding of engineering intuition and to provide a foundation for the explicit application of intuition in engineering problem solving. These insights can be beneficial for creating educational interventions that promote intuition development and introduce real-world engineering practices in the classroom. This in turn can promote metacognition in engineering students by creating pathways to expertise development, as well as boost confidence and support retention (Metcalfe & Wiebe, 1987; Bolton, 2022; Authors, 2021; Authors, 2023). Additionally, insights into intuition can be beneficial in onboarding new hires who may have more expertise development, agility, and adaptability to the technical landscape in the engineering workforce.

References Authors. (2021). Authors. (2019). Authors. (2023). Bolton, C. S. (2022). What Makes an Expert? Characterizing Perceptions of Expertise and Intuition Among Early-Career Engineers [Undergraduate Honors Thesis, Bucknell University]. Lewisburg, PA. Crandall, B., Klein, G. A., & Hoffman, R. R. (2006). Working minds: A practitioner's guide to cognitive task analysis. MIT Press. Dreyfus, S. E., & Dreyfus, H. L. (1980). A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition. Klein, G. A, Calderwood, R., and Macgregor, D. (1989). Critical decision method for eliciting knowledge, IEEE Transactions on systems, man, and cybernetics, 19(3), 462-472. https://doi.org/10.1109/21.31053 Metcalfe, J., & Wiebe, D. (1987). Intuition in Insight and Noninsight Problem Solving. Memory & Cognition, 15(3), 238-246. https://doi.org/10.3758/BF03197722. Seifert, C. M., Patalano, A. L., Hammond, K. J., & Converse, T. M. (1997). Experience and expertise: The role of memory in planning for opportunities. In P. J. Feltovich, K. M. Ford, & R. R. Hoffmanm (Eds.), Expertise in Context (pp. 101-123). AAAI Press/ MIT Press. Taheri, L., Che Pa, N., Abdullah, R., & Abdullah, S. (2014). Knowledge audit model for requirement elicitation process. International Scholarly and Scientific Research & Innovation, 8(2), 452-456.

Citation
Format

Ugenti, N., & Busato, J. E., & Miskioglu, E., & Martin, K. M. (2024, June), Using Cognitive Task Analysis to Observe the Use of Intuition in Engineering Problem Solving Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--48229

TY  - CPAPER
AB  - This work-in-progress research paper describes the pilot work in a study seeking to gain further insight on the relationships between intuition, expertise, and experience through a better understanding of  how intuition is applied in engineering problem solving. Individuals who have attained a high level of expertise, exhibit characteristics of intuitive decision making (Dreyfus &amp;amp; Dreyfus, 1980). The development of expertise (Dreyfus &amp;amp; Dreyfus, 1980; Seifert et al., 1997) and intuition (Authors, 2019; Authors, 2023) are heavily influenced by experience. Engineering intuition can be summarized as a subconscious problem-solving skill that is based on previous experience (Authors, 2023). In this work, we will be using Cognitive Task Analysis (CTA) to examine the use of intuition in engineering problem solving. CTA is a class of observational protocols that surface tacit knowledge through engaging experts with a task (Crandall, 2006). The purpose of CTA is to capture how the mind works through three primary aspects: knowledge elicitation, data analysis, and knowledge representation. As best CTA practices use multiple methods, we will use three methods for this analysis, 1) Simulation Interviews where participants are given a simulated engineering problem and asked to speak out loud to describe their process in approaching the problem, 2) Critical Decision Method (Klein, 1989) where a retrospective interview probes the decisions made during the simulation interview, and 3) Knowledge Audit Method (Taheri et al., 2014) which further guides our probing questions to identify types of knowledge used, or not used, during the simulated problem solving experience. These three techniques are applied to collect data on participants&#39; problem solving. To develop the problems for the Simulation Interviews, we have first conducted pilot work using just the Critical Decision Method and Knowledge Audit Method. As part of the Critical Decision Method, participants will select a non routine problem-solving incident, construct an incident timeline, identify decision points for future probing, and then probe these decisions using the Knowledge Audit Method. This method allows us to determine realistic, practice-based problems for the Simulation Interview, why the participant makes certain decisions, and how their educational background and on the job training influenced their decision making process. The anticipated outcomes of this research are to expand engineering education through a better understanding of engineering intuition and to provide a foundation for the explicit application of intuition in engineering problem solving. These insights can be beneficial for creating educational interventions that promote intuition development and introduce real-world engineering practices in the classroom. This in turn can promote metacognition in engineering students by creating pathways to expertise development, as well as boost confidence and support retention (Metcalfe &amp;amp; Wiebe, 1987; Bolton, 2022; Authors, 2021; Authors, 2023). Additionally, insights into intuition can be beneficial in onboarding new hires who may have more expertise development, agility, and adaptability to the technical landscape in the engineering workforce.

References
Authors. (2021).
Authors. (2019). 
Authors. (2023). 
Bolton, C. S. (2022). What Makes an Expert? Characterizing Perceptions of Expertise and Intuition Among Early-Career Engineers [Undergraduate Honors Thesis, Bucknell University]. Lewisburg, PA. 
Crandall, B., Klein, G. A., &amp;amp; Hoffman, R. R. (2006). Working minds: A practitioner&#39;s guide to cognitive task analysis. MIT Press.
Dreyfus, S. E., &amp;amp; Dreyfus, H. L. (1980). A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition.
Klein, G. A, Calderwood, R., and Macgregor, D. (1989). Critical decision method for eliciting knowledge, IEEE Transactions on systems, man, and cybernetics, 19(3), 462-472. https://doi.org/10.1109/21.31053
Metcalfe, J., &amp;amp; Wiebe, D. (1987). Intuition in Insight and Noninsight Problem Solving. Memory &amp;amp; Cognition, 15(3), 238-246. https://doi.org/10.3758/BF03197722.
Seifert, C. M., Patalano, A. L., Hammond, K. J., &amp;amp; Converse, T. M. (1997). Experience and expertise: The role of memory in planning for opportunities. In P. J. Feltovich, K. M. Ford, &amp;amp; R. R. Hoffmanm (Eds.), Expertise in Context (pp. 101-123). AAAI Press/ MIT Press.
Taheri, L., Che Pa, N., Abdullah, R., &amp;amp; Abdullah, S. (2014). Knowledge audit model for requirement elicitation process. International Scholarly and Scientific Research &amp;amp; Innovation, 8(2), 452-456.
AU  - Natalie Ugenti
AU  - Joselyn Elisabeth Busato
AU  - Elif Miskioglu
AU  - Kaela M. Martin
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Using Cognitive Task Analysis to Observe the Use of Intuition in Engineering Problem Solving
UR  - https://peer.asee.org/48229
DO  - 10.18260/1-2--48229
ER  -