This research paper elaborates on the process used by a team of researchers to create a codebook from interviews of Civil Engineers who included students, professors, and professionals, solving ill-structured problems. The participants solved two ill-structured problems while speaking aloud their thought process. In addition to recording the participant verbalization, the solution to their problems were also collected with the use of a smart pen. Creating a codebook from interviews is a key element of qualitative analysis forming the basis for coding. While individuals can create codebooks for analysis, a team-based approach is advantageous especially when dealing with large amounts of data. A team-based approach involves an iterative process of inter-rater reliability essential to the trustworthiness of the data obtained by coding. In addition to coding the transcripts as a team, which consisted of novice, intermediate, and experts in the engineering education field, the audio and written solution to the problems were also coded. The use of multiple data sources to obtain data, and not just the verbatim transcripts, is lesser studied in engineering education literature and provides opportunities for a more detailed qualitative analysis.
Initial codes were created from existing literature, which were refined through an iterative process. This process consisted of coding data, team consensus on coded data, codebook refinement, and recoding data with the refined codes. Results show that coding verbatim transcripts might not provide an accurate representation of the problem-solving processes participants used to solve the ill-structured problem. Benefits, challenges and recommendations regarding the use of multiple sources to obtain data are discussed while considering the amount of time required to conduct such analysis.
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Developing a Program to Assist in Qualitative Data Analysis: How Engineering Students’ Discuss Model Types
This Research paper discusses the opportunities that utilizing a computer program can present in analyzing large amounts of qualitative data collected through a survey tool. When working with longitudinal qualitative data, there are many challenges that researchers face. The coding scheme may evolve over time requiring re-coding of early data. There may be long periods of time between data analysis. Typically, multiple researchers will participate in the coding, but this may introduce bias or inconsistencies. Ideally the same researchers would be analyzing the data, but often there is some turnover in the team, particularly when students assist with the coding. Computer programs can enable automated or semi-automated coding helping to reduce errors and inconsistencies in the coded data. In this study, a modeling survey was developed to assess student awareness of model types and administered in four first-year engineering courses across the three universities over the span of three years. The data collected from this survey consists of over 4,000 students’ open-ended responses to three questions about types of models in science, technology, engineering, and mathematics (STEM) fields. A coding scheme was developed to identify and categorize model types in student responses. Over two years, two undergraduate researchers analyzed a total of 1,829 students’ survey responses after ensuring intercoder reliability was greater than 80% for each model category. However, with much data remaining to be coded, the research team developed a MATLAB program to automatically implement the coding scheme and identify the types of models students discussed in their responses. MATLAB coded results were compared to human-coded results (n = 1,829) to assess reliability; results matched between 81%-99% for the different model categories. Furthermore, the reliability of the MATLAB coded results are within the range of the interrater reliability measured between the 2 undergraduate researchers (86-100% for the five model categories). With good reliability of the program, all 4,358 survey responses were coded; results showing the number and types of models identified by students are presented in the paper.
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- Award ID(s):
- 1827600
- NSF-PAR ID:
- 10392774
- Date Published:
- Journal Name:
- 2022 ASEE Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This research paper elaborates on the process used by a team of researchers to create a codebook from interviews of Civil Engineers, which included students, professors, and professionals, solving ill-structured problems. The participants solved two ill-structured problems while speaking aloud their thought process. In addition to recording the participant verbalization, the solution to their problems were also collected with the use of a smart pen. Creating a codebook from interviews is a key element of qualitative analysis forming the basis for coding. While individuals can create codebooks for analysis, a team-based approach is advantageous especially when dealing with large amounts of data. A team-based approach involves an iterative process of inter-rater reliability essential to the trustworthiness of the data obtained by coding. In addition to coding the transcripts as a team, which consisted of novice, intermediate, and experts in the engineering education field, the audio and written solution to the problems were also coded. The use of multiple data sources to obtain data, and not just the verbatim transcripts, is lesser studied in engineering education literature and provides opportunities for a more detailed qualitative analysis. Initial codes were created from existing literature, which were refined through an iterative process. This process consisted of coding data, team consensus on coded data, codebook refinement, and recoding data with the refined codes. Results show that coding verbatim transcripts might not provide an accurate representation of the problem-solving processes participants used to solve the ill-structured problem. Benefits, challenges and recommendations regarding the use of multiple sources to obtain data are discussed while considering the amount of time required to conduct such analysis.more » « less
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Aim/Purpose: The research reported here aims to demonstrate a method by which novel applications of qualitative data in quantitative research can resolve ceiling effect tensions for educational and psychological research.Background: Self-report surveys and scales are essential to graduate education and social science research. Ceiling effects reflect the clustering of responses at the highest response categories resulting in non-linearity, a lack of variability which inhibits and distorts statistical analyses. Ceiling effects in stress reported by students can negatively impact the accuracy and utility of the resulting data.Methodology: A longitudinal sample example from graduate engineering students’ stress, open-ended critical events, and their early departure from doctoral study considerations demonstrate the utility and improved accuracy of adjusted stress measures to include open-ended critical event responses. Descriptive statistics are used to describe the ceiling effects in stress data and adjusted stress data. The longitudinal stress ratings were used to predict departure considerations in multilevel modeling ANCOVA analyses and demonstrate improved model predictiveness.Contribution: Combining qualitative data from open-ended responses with quantitative survey responses provides an opportunity to reduce ceiling effects and improve model performance in predicting graduate student persistence. Here, we present a method for adjusting stress scale responses by incorporating coded critical events based on the Taxonomy of Life Events, the application of this method in the analysis of stress responses in a longitudinal data set, and potential applications.Findings: The resulting process more effectively represents the doctoral student experience within statistical analyses. Stress and major life events significantly impact engineering doctoral students’ departure considerations.Recommendations for Practitioners: Graduate educators should be aware of students’ life events and assist students in managing graduate school expectations while maintaining progress toward their degree. Recommendation for Researchers: Integrating coded open-ended qualitative data into statistical models can increase the accuracy and representation of the lived student experience. The new approach improves the accuracy and presentation of students’ lived experiences by incorporating qualitative data into longitudinal analyses. The improvement assists researchers in correcting data with ceiling effects for use in longitudinal analyses.Impact on Society: The method described here provides a framework to systematically include open-ended qualitative data in which ceiling effects are present.Future Research: Future research should validate the coding process in similar samples and in samples of doctoral students in different fields and master’s students.more » « less
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null (Ed.)Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students’ understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students’ responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students’ responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students’ understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students’ awareness of models and their ability to build and apply models.more » « less
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