Conference Tracks

Teaching Practices (Poster Only) – Analysis, synthesis, reflection, and discussion

Abstract

The scholarship of teaching and learning (SoTL) has been developed by cross-disciplinary research
and practice-based teaching inquiries. The objective of this study is to investigate how a cross-
disciplinary project can increase student engagement and learning outcomes. The cross-disciplinary
project in this study is infusing data science into digital photography through data visualization using a
programming language, Python. It is important to choose a single shared subject to overlap across
disciplines. The shared subject is color as data of a digital image. Color is a crucial element of
photography because it directly affects viewers’ emotions and attention. Data visualization can be a
useful tool for the color analysis of photographs. This project is fully developed as a three-week
module on Canvas, but it will be taught in January of 2023, and all data will be collected and analyzed
then. As a hands-on project, students will create 3 color analysis reports, which include photographs,
pie charts, color wheels, and written descriptions. First, students will take 3 photographs that show
monochromatic, analogous, and complementary colors followed by a color theory. Then, they will
generate pie charts using Python, and color wheels using Adobe Color. They will write 3 color analysis
reports by analyzing data visualization and suggesting color improvement for photographs. Through
the project, students can understand and apply color theory to their photographs and analyze colors in
photographs through data visualization. Students will take pre- and post-surveys which consist of
questions to assess students’ engagement with subjects, understanding of subjects, and analytical
skills. The expected result from the pre-and post-survey data and color analysis reports is that the
cross-disciplinary project can increase student engagement in both visual arts and data science, and
improve student learning outcomes. I hope this study can contribute to the SoTL by encouraging other
instructors to develop more cross-disciplinary projects by understanding the benefits. This study was
funded by UNC Research Opportunities Initiative (ROI) award "Winston-Salem State University Center
for Applied Data Science", FY 2020 – 2023.

Session Format

Poster

1

Location

Poster Presentations (Ballroom East)

Publication Type and Release Option

Image (Open Access)

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Data visualization as a useful tool for the color analysis of photographs: The benefits of a cross-disciplinary project

Poster Presentations (Ballroom East)

The scholarship of teaching and learning (SoTL) has been developed by cross-disciplinary research
and practice-based teaching inquiries. The objective of this study is to investigate how a cross-
disciplinary project can increase student engagement and learning outcomes. The cross-disciplinary
project in this study is infusing data science into digital photography through data visualization using a
programming language, Python. It is important to choose a single shared subject to overlap across
disciplines. The shared subject is color as data of a digital image. Color is a crucial element of
photography because it directly affects viewers’ emotions and attention. Data visualization can be a
useful tool for the color analysis of photographs. This project is fully developed as a three-week
module on Canvas, but it will be taught in January of 2023, and all data will be collected and analyzed
then. As a hands-on project, students will create 3 color analysis reports, which include photographs,
pie charts, color wheels, and written descriptions. First, students will take 3 photographs that show
monochromatic, analogous, and complementary colors followed by a color theory. Then, they will
generate pie charts using Python, and color wheels using Adobe Color. They will write 3 color analysis
reports by analyzing data visualization and suggesting color improvement for photographs. Through
the project, students can understand and apply color theory to their photographs and analyze colors in
photographs through data visualization. Students will take pre- and post-surveys which consist of
questions to assess students’ engagement with subjects, understanding of subjects, and analytical
skills. The expected result from the pre-and post-survey data and color analysis reports is that the
cross-disciplinary project can increase student engagement in both visual arts and data science, and
improve student learning outcomes. I hope this study can contribute to the SoTL by encouraging other
instructors to develop more cross-disciplinary projects by understanding the benefits. This study was
funded by UNC Research Opportunities Initiative (ROI) award "Winston-Salem State University Center
for Applied Data Science", FY 2020 – 2023.