Plumb, C., & Marra, R. M., & Hacker, D. J. (2020, June), The Sequential Nature of Engineering Problem Solving  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35372

\bibitem{asee_peer_35372}
  Plumb, C., \& Marra, R. M., \& Hacker, D. J. (2020, June), \emph{The Sequential Nature of Engineering Problem Solving}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--35372

Carolyn Plumb, Rose M. Marra, and Douglas J. Hacker.  "The Sequential Nature of Engineering Problem Solving".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/35372 Internet.  26 Apr, 2024

\bibitem{asee_peer_35372}
  Carolyn Plumb, Rose M. Marra, and Douglas J. Hacker.
  "The Sequential Nature of Engineering Problem Solving".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/35372 Internet.  26 Apr, 2024

@INPROCEEDINGS{asee_peer_35372
author = "Carolyn Plumb, Rose M. Marra, and Douglas J. Hacker"
title = "The Sequential Nature of Engineering Problem Solving"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/35372}
number = {10.18260/1-2--35372}
}

TY  - CPAPER
AB  - The Sequential Nature of Engineering Problem Solving
Iron Range Engineering (IRE) is an innovative, problem-based-learning program in Virginia, Minnesota.  Part of its innovation comes from the program’s strong emphasis on developing metacognitive skills necessary for students to become self-directed learners of the knowledge and skills required for professional engineers.  In our NSF IUSE project, we have been investigating the cognitive processes involved in engineering problem solving, focusing specifically on the role of metacognition.  Using verbal protocol analysis, we recorded students’ utterances as they solved two engineering design problems, a pre-problem at the beginning of their engineering program and a post-problem at the end.  We identified categories of utterances, some metacognitive and some non-metacognitive, and measured the frequency of those utterance categories.  However, because problem solving does not reside in a single utterance nor in the frequency of utterances but rather in the sequence of the utterance categories, we examined the sequences of students’ utterances as they solved the two problems.  This poster will address the sequential nature of the cognitive processes revealed in students’ utterances as they solved engineering design problems and to identify the role that metacognition plays in that sequencing.  We hypothesized that as students acquired greater engineering knowledge and were exposed to greater use of metacognitive thinking and strategies that focused on that knowledge across their education at IRE, the sequencing of their utterances would indicate the following differences from the pre- to the post-problem:  
  
1)  greater sustained use of engineering knowledge when solving the post-problem; 
2)  increased metacognitive monitoring occurring before and after the use of engineering knowledge on the post-problem;
3)  greater elaboration of solutions on the post-problem;   
4)  increased metacognitive monitoring before and after providing solutions on the post-problem; 
5)  greater use of metacognitive knowledge of strategies on the post-problem.   
A lag-one sequential analysis (i.e., an utterance [lag 0] directly followed by another utterance [lag 1]) was conducted by producing three matrices for each of the 11 participants for the pre- and post-problems:  a frequency matrix showing the frequency of lag-one transitions; a transitional matrix showing the probability of a particular category of utterance occurring given that a specific category of utterance had occurred; and a z-score matrix produced from each frequency matrix showing what sequence transitions significantly differed from chance.  We averaged the z-score matrices across students’ pre- and post-problems and computed a χ2 statistic to analyze differences between the two.  Our results are preliminary, but the χ2 was significant.  We found support for hypotheses 1, 3, and 5, partial support for hypothesis 2, and no support for hypothesis 4.  From pre- to post-problems, students increased their use of engineering knowledge, elaborated their solutions, made greater use of their metacognitive knowledge of strategies, and made greater use of metacognitive monitoring before but not after the use of their engineering knowledge.  Metacognitive monitoring remained stable before a solution but decreased after a solution.  The IRE program showed positive growth in both students’ engineering knowledge and in their metacognitive use of that knowledge.        

AU  - Carolyn Plumb
AU  - Rose M. Marra
AU  - Douglas J. Hacker
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - The Sequential Nature of Engineering Problem Solving
UR  - https://peer.asee.org/35372
DO  - 10.18260/1-2--35372
ER  -