Blended Phenomenography: An Alternative to Investigate Learning

John Mendoza-Garcia; Monica E. Cardella; William C. Oakes

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Conference: 2020 ASEE Virtual Annual Conference Content Access
Location: Virtual On line
Publication Date: June 22, 2020
Start Date: June 22, 2020
End Date: June 26, 2021
Conference Session: Instruments and Methods for Studying Student Experiences and Outcomes
Tagged Division: Educational Research and Methods
Page Count: 13
DOI: 10.18260/1-2--34219
Permanent URL: https://peer.asee.org/34219
Download Count: 463

Paper Authors

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John Mendoza-Garcia University of Florida orcid.org/0000-0002-4943-6222

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John Mendoza Garcia is a Lecturer at the Department of Engineering Education in the Herbert Wertheim College of Engineering at the University of Florida. He received his Ph.D. in Engineering Education at Purdue University, and he has a Master's and a Bachelor's in Systems and Computing Engineering from Universidad de Los Andes, in Colombia, and Universidad Nacional de Colombia respectively. He teaches and investigates the development of professional skills such as problem-solving, systems thinking, and design thinking. He worked in Industry before transitioning to academia.

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Monica E. Cardella Purdue University, West Lafayette orcid.org/0000-0002-4229-6183

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Monica E. Cardella is a Professor of Engineering Education at Purdue University and a Program Director at the National Science Foundation.

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William C. Oakes Purdue University, West Lafayette orcid.org/0000-0002-6183-045X

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William (Bill) Oakes is the Director of the EPICS Program, a 150th Anniversary Professor and one of the founding faculty members of the School of Engineering Education at Purdue University. He has held courtesy appointments in Mechanical, Environmental and Ecological Engineering as well as Curriculum and Instruction in the College of Education. He is a registered professional engineer and on the NSPE board for Professional Engineers in Higher Education. He has been active in ASEE serving in the FPD, CIP and ERM. He is the past chair of the IN/IL section. He is a fellow of the Teaching Academy and listed in the Book of Great Teachers at Purdue University. He was the first engineering faculty member to receive the national Campus Compact Thomas Ehrlich Faculty Award for Service-Learning. He was a co-recipient of the National Academy of Engineering’s Bernard Gordon Prize for Innovation in Engineering and Technology Education and the recipient of the National Society of Professional Engineers’ Educational Excellence Award and the ASEE Chester Carlson Award. He is a fellow of the American Society for Engineering Education and the National Society of Professional Engineers.

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Abstract

Phenomenography is a research approach that has been widely used in Engineering Education to study student’s experiences with various phenomena such as Human-Centered Design, interdisciplinary learning, multiple solutions, conditional and repetition structures, among others. Still, researchers face different issues when working with phenomenography. One of the key ones is the unawareness of the existence of two or more different approaches. The Swedish educational researcher Ference Marton has developed two approaches (first and second phenomenography). John Bowden, an Australian researcher, developed another that is called Developmental phenomenography, and other Nordic researchers have developed other variations. In this paper, we want to contribute to the differentiation of two relevant approaches: Marton’s second and Bowden’s developmental. Each of these phenomenographic approaches has different implications that researchers should follow for valid data collection, data analysis, and outcome spaces. One of these differences can be seen in data collection. Using Marton’s second phenomenography approach implies the collection of data with participants addressing the same task. This is because, according to Marton, that makes visible what the participant knows about an object of learning. On the contrary, a researcher using the developmental phenomenography approach will collect data on interviews with participant’s describing their experiences with an object of learning. Similarly, in data analysis, there are key differences. For example, in the developmental approach, the unit of analysis is the whole transcript, while in the Marton’s first and second, the unit of analysis are quotes from the interviews. Similarly, the outcome spaces are different. Beyond such differentiation, in this paper, we will share our experience on blending these two approaches for studying Engineering Problem-Solving. We used Marton’s second Phenomenography for data collection, and a first data analysis phase to find, according to Variation Theory, the critical aspects and critical features of the object of learning. Accordingly, we will share our process of finding the right tasks, the interview process, and our data analysis process. In addition, we will describe how we used Bowden’s developmental approach for the second phase of our data analysis to discover a developmental path for our object of learning. We believe that blending phenomenography has advantages of the two phenomenographic methods, and it can be a valuable approach for researchers and practitioners when trying to identify and understand the learning trajectory of an engineering object of learning.

Citation
Format

Mendoza-Garcia, J., & Cardella, M. E., & Oakes, W. C. (2020, June), Blended Phenomenography: An Alternative to Investigate Learning Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34219

TY - CPAPER
AB - Phenomenography is a research approach that has been widely used in Engineering Education to study student’s experiences with various phenomena such as Human-Centered Design, interdisciplinary learning, multiple solutions, conditional and repetition structures, among others. Still, researchers face different issues when working with phenomenography. One of the key ones is the unawareness of the existence of two or more different approaches. The Swedish educational researcher Ference Marton has developed two approaches (first and second phenomenography). John Bowden, an Australian researcher, developed another that is called Developmental phenomenography, and other Nordic researchers have developed other variations. In this paper, we want to contribute to the differentiation of two relevant approaches: Marton’s second and Bowden’s developmental. Each of these phenomenographic approaches has different implications that researchers should follow for valid data collection, data analysis, and outcome spaces. One of these differences can be seen in data collection. Using Marton’s second phenomenography approach implies the collection of data with participants addressing the same task. This is because, according to Marton, that makes visible what the participant knows about an object of learning. On the contrary, a researcher using the developmental phenomenography approach will collect data on interviews with participant’s describing their experiences with an object of learning. Similarly, in data analysis, there are key differences. For example, in the developmental approach, the unit of analysis is the whole transcript, while in the Marton’s first and second, the unit of analysis are quotes from the interviews. Similarly, the outcome spaces are different.
Beyond such differentiation, in this paper, we will share our experience on blending these two approaches for studying Engineering Problem-Solving. We used Marton’s second Phenomenography for data collection, and a first data analysis phase to find, according to Variation Theory, the critical aspects and critical features of the object of learning. Accordingly, we will share our process of finding the right tasks, the interview process, and our data analysis process. In addition, we will describe how we used Bowden’s developmental approach for the second phase of our data analysis to discover a developmental path for our object of learning.
We believe that blending phenomenography has advantages of the two phenomenographic methods, and it can be a valuable approach for researchers and practitioners when trying to identify and understand the learning trajectory of an engineering object of learning.
AU - John Mendoza-Garcia
AU - Monica E. Cardella
AU - William C. Oakes
CY - Virtual On line
DA - 2020/06/22
PB - ASEE Conferences
TI - Blended Phenomenography: An Alternative to Investigate Learning
UR - https://peer.asee.org/34219
DO - 10.18260/1-2--34219
ER -