Innovative Pedagogy for Teaching and Learning Data Visualization

Vetria L. Byrd

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: Multidisciplinary Endeavors: Mechatronics, Robotics, and Technology
Tagged Division: Multidisciplinary Engineering
Page Count: 20
DOI: 10.18260/1-2--37342
Permanent URL: https://strategy.asee.org/37342
Download Count: 745

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Paper Authors

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Vetria L. Byrd Ph.D. Purdue University at West Lafayette (COE) orcid.org/0000-0002-0733-2062

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Dr. Vetria Byrd is an assistant professor in the Department of Computer Graphics Technology in the Polytechnic Institute at Purdue University in West Lafayette, Indiana. Dr. Byrd is the founder and organizer of BPViz: Broadening Participation in Visualization (BPViz) Workshops. Dr. Byrd has given numerous invited talks on visualization, and given numerous workshops nationally and internationally on visualizaiton. Dr. Byrd received her graduate and undergraduate degrees at the University of Alabama at Birmingham, in Birmingham, Alabama which includes: Ph.D. in Computer and Information Sciences, Master’s degrees in Computer Science and Biomedical Engineering and a Bachelor’s degree in Computer Science. Dr. Byrd’s research interests include: data visualization, data visualization capacity building, high performance visualization, big data, and collaborative visualization.

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Abstract

This paper describes a process for teaching and learning data visualization with the use of a series of worksheets for each stage of the data visualization process. The class meets twice a week, for 16-weeks with lecture on the first day of the week and in-person labs on the second day. The course consists of an online-lecture, and use of activity worksheets for in-class and homework assignments to foster student participation and engagement, and in-person labs.

Data visualization is a complex, intricate process, that fosters higher-order thinking and critical thinking in each stage of the process. Ordinarily, the class is in-person which allows for interactive engagement and organic dialog between faculty and students, which makes is easier to see when students are struggling with a concept. In the absence of these key elements, worksheets are used to introduce each stage of the data visualization process, student self-assessments are used to gauge self-efficacy and quizzes are used to assess competency. What is innovative about the approach is the automation of the process for students and faculty. Students complete a worksheet online and receive a copy of their responses by email with the option to generate a PDF version of their responses. Subsequently, the student submits the PDF version to Brightspace for review by the instructor. After submitting the worksheet, students complete a self-assessment survey to assess student’s self-efficacy with content covered in class and reinforced in the worksheet. Worksheets coupled with self-assessments provide insight on student’s data visualization capacity levels.

The goals of the worksheets are to enable students to understand essential elements of data visualization while fostering critical thinking throughout the process. Skills identified and showcased in each worksheet align with capabilities characteristic of higher-order thinking skills across the knowledge and cognitive dimensions of Bloom’s Taxonomy hierarchy of learning. Worksheets are introduced in a linear manner for the novice; however, each worksheet is designed to stand alone to facilitate the iterative, dynamic nature of the data visualization process. Overall response to the worksheet method was positive. With 97% of Likert responses being agree total, 65% were strongly agree in response to capturing students’ self-assessment of what was learned from the data visualization course. Three percent of students disagreed. The activity worksheets are used to inform pedagogy of data visualization. In this work, we describe the online-process, the worksheets, assignments and the ways in which faculty and students navigate the pandemic inspired teaching and learning environment to support higher-order thinking, and critical thinking skills, crucial to computing and engineering curriculums, among undergraduates.

Citation
Format

Byrd, V. L. (2021, July), Innovative Pedagogy for Teaching and Learning Data Visualization Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37342

TY  - CPAPER
AB  - This paper describes a process for teaching and learning data visualization with the use of a series of worksheets for each stage of the data visualization process. The class meets twice a week, for 16-weeks with lecture on the first day of the week and in-person labs on the second day. The course consists of an online-lecture, and use of activity worksheets for in-class and homework assignments to foster student participation and engagement, and in-person labs.

Data visualization is a complex, intricate process, that fosters higher-order thinking and critical thinking in each stage of the process. Ordinarily, the class is in-person which allows for interactive engagement and organic dialog between faculty and students, which makes is easier to see when students are struggling with a concept. In the absence of these key elements, worksheets are used to introduce each stage of the data visualization process, student self-assessments are used to gauge self-efficacy and quizzes are used to assess competency. What is innovative about the approach is the automation of the process for students and faculty. Students complete a worksheet online and receive a copy of their responses by email with the option to generate a PDF version of their responses. Subsequently, the student submits the PDF version to Brightspace for review by the instructor. After submitting the worksheet, students complete a self-assessment survey to assess student’s self-efficacy with content covered in class and reinforced in the worksheet. Worksheets coupled with self-assessments provide insight on student’s data visualization capacity levels. 



The goals of the worksheets are to enable students to understand essential elements of data visualization while fostering critical thinking throughout the process. Skills identified and showcased in each worksheet align with capabilities characteristic of higher-order thinking skills across the knowledge and cognitive dimensions of Bloom’s Taxonomy hierarchy of learning. Worksheets are introduced in a linear manner for the novice; however, each worksheet is designed to stand alone to facilitate the iterative, dynamic nature of the data visualization process. Overall response to the worksheet method was positive. With 97% of Likert responses being agree total, 65% were strongly agree in response to capturing students’ self-assessment of what was learned from the data visualization course. Three percent of students disagreed. The activity worksheets are used to inform pedagogy of data visualization. In this work, we describe the online-process, the worksheets, assignments and the ways in which faculty and students navigate the pandemic inspired teaching and learning environment to support higher-order thinking, and critical thinking skills, crucial to computing and engineering curriculums, among undergraduates. 

AU  - Vetria L. Byrd Ph.D.
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Innovative Pedagogy for Teaching and Learning Data Visualization
UR  - https://strategy.asee.org/37342
DO  - 10.18260/1-2--37342
ER  -