Studying the Resource Networks of First-year Engineering Students: Establishing a Data Collection Method

Alexis Rae Walsh; Emily Diehl; Courtney June Faber

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Conference: 2019 ASEE Annual Conference & Exposition
Location: Tampa, Florida
Publication Date: June 15, 2019
Start Date: June 15, 2019
End Date: June 19, 2019
Conference Session: Student Division Technical Session 1
Tagged Division: Student
Page Count: 8
DOI: 10.18260/1-2--33321
Permanent URL: https://strategy.asee.org/33321
Download Count: 433

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Paper Authors

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Alexis Rae Walsh University of Tennessee, Knoxville

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I am a second year Industrial & Systems Engineering student at the University of Tennessee in Knoxville.

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Emily Diehl The University of Tennessee, Knoxville

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Emily is a senior in the Industrial and Systems Engineering with an Honors concentration at the University of Tennessee, Knoxville. She studies Engineering Education under the direction of Dr. Courtney Faber as part of the ENLITE research group. Emily is also a member of the Haslam Scholars Program and the Cook Grand Challenge Scholars program.

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Courtney June Faber University of Tennessee, Knoxville

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Courtney is a Research Assistant Professor and Lecturer in the Cook Grand Challenge Engineering Honors Program at the University of Tennessee. She completed her Ph.D. in Engineering & Science Education at Clemson University. Prior to her Ph.D. work, she received her B.S. in Bioengineering at Clemson University and her M.S. in Biomedical Engineering at Cornell University. Courtney’s research interests include epistemic cognition in the context of problem solving, and researcher identity.

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Abstract

This paper describes our process for developing a data collection method to study the resource networks students use to complete engineering homework problems through network analysis. Network analysis is the study of connections between objects or people. Networks are composed of nodes and edges, where nodes are objects or people and edges represent the connections between nodes. There have been multiple education studies within and outside of engineering that use network analysis to understand peer connections within a course. However, there are few studies that have applied this method to study how students use resources within a course.

The goal of our larger work is to examine the use of resources by first year engineering students to complete homework to understand what resources they use, when they use particular resources, and how they use the resources. To begin addressing this goal we needed to establish a method to collect data that allows us to create and analyze time-series networks. Our time-series networks include information to capture the resources students use (e.g. instructors, teaching assistants, peers, class notes, online sources, textbooks, and discussion boards) and the order in which they use these resources.

The data collection method that we developed was informed by previous studies in the field of engineering education including our prior research. Given the nature of the data that we are collecting and number of responses we want to collect, we decided to create a survey with both descriptive (open-ended) and multiple-selection questions. For this study, we asked students in a first-year engineering course to complete the survey with a specific assignment in mind.

At the beginning of the survey, we included an open-ended item asking students to describe the process that they used to complete the assignment. Students’ responses to this question included information about what resources were selected and the order in which they used the resources. This qualitative data was used to generate a matrix that was imported into the statistical software R to generate a visualization of the network and analyze the network’s structure.

In order to assess the survey’s validity and robustness, we needed to collect redundant data, so we included multiple-selection items asking students to identify the resources they used. These items were followed by open-ended questions asking students to describe how they used these resources. We used these responses to generate additional resource networks for 20 randomly selected students (27% of our sample). These resource networks for each student were then compared to their time-series networks to assess the number of connections and the resources included in each network. Through this comparison, one limitation we identified is that some of the student did not provide sufficient detail in their description of the process they used to complete the homework. Because of this, the resource networks included more connections than the time-series networks. Future work will seek to modify the item asking about the students’ processes in order to capture more detailed data from students.

Citation
Format

Walsh, A. R., & Diehl, E., & Faber, C. J. (2019, June), Studying the Resource Networks of First-year Engineering Students: Establishing a Data Collection Method Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33321

TY - CPAPER
AB - This paper describes our process for developing a data collection method to study the resource networks students use to complete engineering homework problems through network analysis. Network analysis is the study of connections between objects or people. Networks are composed of nodes and edges, where nodes are objects or people and edges represent the connections between nodes. There have been multiple education studies within and outside of engineering that use network analysis to understand peer connections within a course. However, there are few studies that have applied this method to study how students use resources within a course.

The goal of our larger work is to examine the use of resources by first year engineering students to complete homework to understand what resources they use, when they use particular resources, and how they use the resources. To begin addressing this goal we needed to establish a method to collect data that allows us to create and analyze time-series networks. Our time-series networks include information to capture the resources students use (e.g. instructors, teaching assistants, peers, class notes, online sources, textbooks, and discussion boards) and the order in which they use these resources.

The data collection method that we developed was informed by previous studies in the field of engineering education including our prior research. Given the nature of the data that we are collecting and number of responses we want to collect, we decided to create a survey with both descriptive (open-ended) and multiple-selection questions. For this study, we asked students in a first-year engineering course to complete the survey with a specific assignment in mind.

At the beginning of the survey, we included an open-ended item asking students to describe the process that they used to complete the assignment. Students’ responses to this question included information about what resources were selected and the order in which they used the resources. This qualitative data was used to generate a matrix that was imported into the statistical software R to generate a visualization of the network and analyze the network’s structure.

In order to assess the survey’s validity and robustness, we needed to collect redundant data, so we included multiple-selection items asking students to identify the resources they used. These items were followed by open-ended questions asking students to describe how they used these resources. We used these responses to generate additional resource networks for 20 randomly selected students (27% of our sample). These resource networks for each student were then compared to their time-series networks to assess the number of connections and the resources included in each network. Through this comparison, one limitation we identified is that some of the student did not provide sufficient detail in their description of the process they used to complete the homework. Because of this, the resource networks included more connections than the time-series networks. Future work will seek to modify the item asking about the students’ processes in order to capture more detailed data from students.
AU - Alexis Rae Walsh
AU - Emily Diehl
AU - Courtney June Faber
CY - Tampa, Florida
DA - 2019/06/15
PB - ASEE Conferences
TI - Studying the Resource Networks of First-year Engineering Students: Establishing a Data Collection Method
UR - https://strategy.asee.org/33321
DO - 10.18260/1-2--33321
ER -