Triangulation of Three Different Research Methods when Capturing Participant Data During Engineering Education

Jani Kalasniemi; Joona Kurikka; Lauri Repokari

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Conference: 2017 ASEE Annual Conference & Exposition
Location: Columbus, Ohio
Publication Date: June 24, 2017
Start Date: June 24, 2017
End Date: June 28, 2017
Conference Session: Electrical and Computer Division Technical Session 5
Tagged Division: Electrical and Computer
Page Count: 10
DOI: 10.18260/1-2--29045
Permanent URL: https://peer.asee.org/29045
Download Count: 495

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Paper Authors

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Jani Kalasniemi Aalto University orcid.org/0000-0002-0979-5415

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Mechanical Engineer, Master of Science in Technology
Done several international and multidisciplinary university projects during studies, including ME310 with Stanford.
Entrepreneur and CEO of a Finnish startup ZeroG Oy
Alumni from Aalto University targeting to be a Ph.D. candidate

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Joona Kurikka Aalto University

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Joona Kurikka is a PhD Researcher at Aalto University and Associate at CERN, working at the innovation experiment IdeaSquare. As part of his work at CERN, he is coordinating and teaching student project like Challenge Based Innovation and various smaller innovation workshops, hackathons and other projects. His current research focus is on processes and ICT tools for distributed collaboration and learning.

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Lauri Repokari Politecnico do Porto

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Consulting professor at Politecnico do Porto. Previous Positions: Research Manager at Aalto University, Invited Professor at Kyoto Institute of Technology, Consulting Assistant Professor at Stanford University. Several positions in industry. Hundreds Industrial projects conducted in academia-industry collaboration.

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Abstract

Designing new products and further developing existing products has become increasingly important for today’s industry. Therefore, engineering education has expanded from theoretical science education towards practical and challenging project-based education to teach students real-life problem-solving skills along with communication and teamwork skills, which are also essential in the future working environment after graduation. Effectively tracking this learning experience is one of the critical steps to improve it. And as commercial product development and R&D are expensive, risky, and time-consuming, also industry around the world is increasingly interested in measuring the effectiveness of the design process and the design team In our experiment conducted in January 2016, the participants were from four different European countries and from several different study backgrounds. Most of them were also participating the experiment as a voluntary part of their postgraduate studies. The teams were challenged with a task to build a robot which communicates with the user, is easy to use, moves independently, and has to be able to express four distinct emotions. The challenge lasted for 4 days and 4 hours. Data was collected and analyzed by using three different research methods; time-lapse images, time-tracking software operated by the coaches individually, and written coach notes. The teams were tracked with eight time-lapse cameras and time tracker data was collected with software installed to coaches’ mobile phones. Coaches also made handwritten notes after each student interaction to elaborate details about the encountered issues. To synchronize with other methods, the notes also included a timestamp when the coach had helped the team. Time-tracking data from coaches showed in details how much time the coaches had spent with the teams. There were only a few times when a coach made a mistake by forgetting to start or stop the timer. Without the alternative methods, this information would be quite hard to analyze since one could only see the duration of the session and time when it happened. Handwritten notes, on the other hand, did give an enormous amount of additional qualitative information about what kind of problems the teams were facing in their challenge. The outcome of the study is that none of the three methods proved to be superior, but each one of them brings up useful data for future studies when combined. The paper will introduce detailed recommendations in building and updating such a measurement setup in a dedicated working space and analyze the gathered data in more detail.

Citation
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Kalasniemi, J., & Kurikka, J., & Repokari, L. (2017, June), Triangulation of Three Different Research Methods when Capturing Participant Data During Engineering Education Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--29045

TY - CPAPER
AB - Designing new products and further developing existing products has become increasingly important for today’s industry. Therefore, engineering education has expanded from theoretical science education towards practical and challenging project-based education to teach students real-life problem-solving skills along with communication and teamwork skills, which are also essential in the future working environment after graduation. Effectively tracking this learning experience is one of the critical steps to improve it. And as commercial product development and R&amp;D are expensive, risky, and time-consuming, also industry around the world is increasingly interested in measuring the effectiveness of the design process and the design team
In our experiment conducted in January 2016, the participants were from four different European countries and from several different study backgrounds. Most of them were also participating the experiment as a voluntary part of their postgraduate studies.
The teams were challenged with a task to build a robot which communicates with the user, is easy to use, moves independently, and has to be able to express four distinct emotions. The challenge lasted for 4 days and 4 hours. Data was collected and analyzed by using three different research methods; time-lapse images, time-tracking software operated by the coaches individually, and written coach notes.
The teams were tracked with eight time-lapse cameras and time tracker data was collected with software installed to coaches’ mobile phones. Coaches also made handwritten notes after each student interaction to elaborate details about the encountered issues. To synchronize with other methods, the notes also included a timestamp when the coach had helped the team.
Time-tracking data from coaches showed in details how much time the coaches had spent with the teams. There were only a few times when a coach made a mistake by forgetting to start or stop the timer. Without the alternative methods, this information would be quite hard to analyze since one could only see the duration of the session and time when it happened. Handwritten notes, on the other hand, did give an enormous amount of additional qualitative information about what kind of problems the teams were facing in their challenge.
The outcome of the study is that none of the three methods proved to be superior, but each one of them brings up useful data for future studies when combined. The paper will introduce detailed recommendations in building and updating such a measurement setup in a dedicated working space and analyze the gathered data in more detail.
AU - Jani Kalasniemi
AU - Joona Kurikka
AU - Lauri Repokari
CY - Columbus, Ohio
DA - 2017/06/24
PB - ASEE Conferences
TI - Triangulation of Three Different Research Methods when Capturing Participant Data During Engineering Education
UR - https://peer.asee.org/29045
DO - 10.18260/1-2--29045
ER -