Work in Progress: Qualitative Content Analysis of Quantitative Literacy in First-Year Engineering Courses

Kerrie A. Douglas; Elliot P. Douglas; Raenita A. Fenner; Peggy O'Neill

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Work-in-Progress Session: Exploring Learning and Development in Engineering Courses
Tagged Division: Educational Research and Methods Division (ERM)
Page Count: 8
DOI: 10.18260/1-2--44342
Permanent URL: https://peer.asee.org/44342
Download Count: 61

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Paper Authors

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Raenita A. Fenner Loyola University, Maryland

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Dr. Raenita Fenner is an Associate Professor of Engineering in the Department of Engineering at Loyola University Maryland.

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Peggy O'Neill Loyola University, Maryland

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Peggy O'Neill, PhD, is a professor of writing at Loyola University Maryland where she has served as director of composition, department chair, and associate dean. Her primary research is in writing pedagogy and assessment, and she has taught a wide variety of writing courses including first year composition, professional writing, and rhetoric. She has been collaborating with Professor Raenita Fenner on ways to improve student learning in Engineering for several years.

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Kerrie A. Douglas Purdue University, West Lafayette orcid.org/0000-0002-2693-5272

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Dr. Douglas is an Associate Professor in the Purdue School of Engineering Education. Her research is focused on improving methods of assessment in engineering learning environments and supporting engineering students.

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Elliot P. Douglas University of Florida

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Elliot P. Douglas is Professor of Environmental Engineering Sciences and Engineering Education, and Distinguished Teaching Scholar at the University of Florida. His research interests are in the areas of problem-solving, cultures of inclusion in engineeri

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Abstract

This paper is a Work in Progress (WIP). Quantitative Literacy (QL) encompasses many of the competencies professional engineers need. QL is the ability to engage in context-specific quantitative activities for problem-solving by collecting, understanding, processing, interpreting, synthesizing, and displaying numerical information for effective communication. QL also includes numerical skills, communication of quantitative information, and dispositions and beliefs in quantitative activities. Thus, QL is multi-dimensional with the dimensions of disposition, cognition, and beliefs. Within this framework, cognition comprises sub-dimensions of content, reasoning, and communication. While engineers and engineering educators agree that QL is critical for success as an engineering student and professional, little is known about the expectations of QL for engineering students as they begin their college engineering studies. As a result, existing instruments for measuring QL are designed for a general population and do not assess engineering-specific QL needs.

This work-in-progress paper reports on part of a larger study that aims to develop an evidence-based Student Model for future assessment instruments intended to measure first-year engineering students' QL. The Student Model refers to the specific knowledge, skills, and abilities that are desired targets in the assessment instrument. To obtain the evidence for the Student Model, materials from first-year engineering curricula (i.e., learning objectives, homework problems, and assessment questions) are being collected from a variety of engineering programs for analysis. Qualitative Content Analysis (QCA) and an a-priori coding frame developed from a QL framework are being used to answer the research question, What QL skills and knowledge are expected of first-year engineering students? While materials are still being collected and coded, preliminary results indicate that first-year engineering courses focus on the cognitive aspect of QL with little attention to disposition and beliefs, the two other aspects of QL identified in the literature. In the full paper, we will share the results of the qualitative content analysis of the course materials, comprising instances of QL found in the first-year course materials and the specific aspects of QL students are expected to know (e.g., disposition, content, etc.).

Determining the expectations for QL in first-year courses is the first step in developing a QL instrument that is specific to engineering. Understanding what students are expected to learn in their first-year engineering classes will also help educators better prepare students for success in college during secondary education.

Keywords: quantitative literacy, QL, qualitative content analysis, QCA, first-year, student model, assessment

Citation
Format

Fenner, R. A., & O'Neill, P., & Douglas, K. A., & Douglas, E. P. (2023, June), Work in Progress: Qualitative Content Analysis of Quantitative Literacy in First-Year Engineering Courses Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44342

TY - CPAPER
AB - This paper is a Work in Progress (WIP). Quantitative Literacy (QL) encompasses many of the competencies professional engineers need. QL is the ability to engage in context-specific quantitative activities for problem-solving by collecting, understanding, processing, interpreting, synthesizing, and displaying numerical information for effective communication. QL also includes numerical skills, communication of quantitative information, and dispositions and beliefs in quantitative activities. Thus, QL is multi-dimensional with the dimensions of disposition, cognition, and beliefs. Within this framework, cognition comprises sub-dimensions of content, reasoning, and communication. While engineers and engineering educators agree that QL is critical for success as an engineering student and professional, little is known about the expectations of QL for engineering students as they begin their college engineering studies. As a result, existing instruments for measuring QL are designed for a general population and do not assess engineering-specific QL needs.

This work-in-progress paper reports on part of a larger study that aims to develop an evidence-based Student Model for future assessment instruments intended to measure first-year engineering students&#39; QL. The Student Model refers to the specific knowledge, skills, and abilities that are desired targets in the assessment instrument. To obtain the evidence for the Student Model, materials from first-year engineering curricula (i.e., learning objectives, homework problems, and assessment questions) are being collected from a variety of engineering programs for analysis. Qualitative Content Analysis (QCA) and an a-priori coding frame developed from a QL framework are being used to answer the research question, What QL skills and knowledge are expected of first-year engineering students? While materials are still being collected and coded, preliminary results indicate that first-year engineering courses focus on the cognitive aspect of QL with little attention to disposition and beliefs, the two other aspects of QL identified in the literature. In the full paper, we will share the results of the qualitative content analysis of the course materials, comprising instances of QL found in the first-year course materials and the specific aspects of QL students are expected to know (e.g., disposition, content, etc.).

Determining the expectations for QL in first-year courses is the first step in developing a QL instrument that is specific to engineering. Understanding what students are expected to learn in their first-year engineering classes will also help educators better prepare students for success in college during secondary education.

Keywords: quantitative literacy, QL, qualitative content analysis, QCA, first-year, student model, assessment
AU - Raenita A. Fenner
AU - Peggy O'Neill
AU - Kerrie A. Douglas
AU - Elliot P. Douglas
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Work in Progress: Qualitative Content Analysis of Quantitative Literacy in First-Year Engineering Courses
UR - https://peer.asee.org/44342
DO - 10.18260/1-2--44342
ER -