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Climate change is a major concern to undergraduate students. Understanding climate change relies on an understanding of polar regions. However, courses on polar regions are rare at undergraduate institutions. Polar ENgagement through GUided INquiry (PENGUIN) modules were designed to give students experience with polar research in a variety of standard courses, including physics, computer science, physical chemistry, and economics, through using course-specific and computational tools to analyze polar data. Here, we present a new PENGUIN module taught in a statistics class, in which students apply statistical tools to ice core data to reconstruct past temperature records. Quantitative student responses on pre- and post-surveys were collected in a quasi-experimental context to assess student knowledge gains for a test group of 91 students and a control group of 73 students (who did not complete the module). Test-group students made statistically significant increases of 25 to 46% on all six statistics questions, with a normalized gain of 56%. By contrast, control group statistics knowledge gains ranged from −4 to 25%, with statistically significant increases for only three questions and a normalized gain of 22%. For polar research questions, the test group demonstrated increases in correct responses to polar research questions (11 to 31%), with statistically significant improvements (p < .05) of 22-31% on 3 of 6 polar research questions. These findings support the conclusion that PENGUIN modules can successfully teach course concepts while increasing polar literacy.
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Teaching image processing in an upper level CS undergraduate class using compuational guided inquiry and polar data
This paper describes a method of teaching image processing in a computer science (CS) course in which students obtain and analyze polar data through a computational guided inquiry (CGI) module. In CGI, the instructor guides the students in the process of learning, through the use of a computational tool: for this course, a Jupyter Notebook is used, consisting of alternating text and blocks of Python code that the students can modify as needed and execute. The students obtain images of polar ice and use them to learn about image processing while increasing their climate literacy. Students demonstrated learning of course disciplinary objectives through assessments built into the CGI module. Pre- and post-module surveys indicate increases in student self-reporting of comfort with Python and exposure to polar data. Over half of students indicated increased interest in learning more about polar research, and students overall rated the CGI modules positively. Improvements in climate literacy were tested through asking students to ask a question about a visual representation of polar data; results of this assessment were inconclusive. Future work will focus on strengthening the connection between goals, activities, and assessment, in order to better understand whether the goal of improved climate literacy was achieved.
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- Award ID(s):
- 1712354
- PAR ID:
- 10089305
- Date Published:
- Journal Name:
- Journal of computing sciences in colleges
- Volume:
- 34
- Issue:
- 1
- ISSN:
- 1937-4763
- Page Range / eLocation ID:
- 171-179
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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