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Article

Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students

by
Ileana A. Callejas
1,*,
Liana Huang
1,
Marisol Cira
1,
Benjamin Croze
1,
Christine M. Lee
2,
Taylor Cason
1,
Elizabeth Schiffler
3,
Carlin Soos
4,
Paul Stainier
5,
Zichan Wang
6,
Shanna Shaked
7,
Moana McClellan
5,
Wei-Cheng Hung
8 and
Jennifer A. Jay
1,*
1
Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, CA 91011, USA
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA
3
Department of Theater, University of California, Los Angeles, Los Angeles, CA 91011, USA
4
Department of Information Studies, University of California, Los Angeles, Los Angeles, CA 91011, USA
5
Institute of the Environment & Sustainability, University of California, Los Angeles, Los Angeles, CA 91011, USA
6
The Cotsen Institute of Archaeology, University of California, Los Angeles, Los Angeles, CA 91011, USA
7
Center for Education Innovation & Learning in the Sciences, University of California, Los Angeles, Los Angeles, CA 91011, USA
8
Department of Chemistry and Biochemistry, Loyola Marymount University, Los Angeles, CA 90045, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(15), 11995; https://doi.org/10.3390/su151511995
Submission received: 2 June 2023 / Revised: 22 July 2023 / Accepted: 27 July 2023 / Published: 4 August 2023
(This article belongs to the Special Issue Technology Enhanced Learning and Resilience in Higher Education Institutions during COVID-19 and Beyond: Challenges and Opportunities)
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Abstract

:
Computational skills are advantageous for teaching students to investigate environmental change using satellite remote sensing. This focus is especially relevant given the disproportionate underrepresentation of minorities and women in STEM fields. This study quantified the effects in both a STEM and a non-STEM class of Earth science remote sensing modules in Google Earth Engine on students’ self-efficacy in coding, understanding remote sensing, and interest in science and a career in environmental research. Additionally, the STEM students engaged in a course-based undergraduate research experience (CURE) on water quality. Satellite imagery was used to visualize water quality changes in coastal areas around the world due to the COVID-19 pandemic shutdown. Pre- and post-surveys reveal statistically significant changes in most students’ confidence to apply coding skills to investigate environmental change and understand remote sensing. The intervention was not sufficient to lead to significant changes in interest in science or a career in environmental research. There is great benefit in incorporating remote sensing labs to teach environmental concepts to STEM and non-STEM students and to bolster the confidence of underrepresented minorities and females in STEM.

1. Introduction

Underrepresented minorities (URM) and women remain at disproportionately low numbers in science, technology, engineering, and math (STEM) and the research workforce despite efforts to increase diversity [1,2,3,4]. In 2016, women earned only 20% of Bachelor’s degrees in engineering and URM students only received 22% of all engineering Bachelor’s degrees in the United States [5]. Intervention efforts seeking to increase diversity in STEM include research experiences, mentoring, financial assistance, and graduate school preparation [6]. However, most first-generation and URM students do not have sufficient resources, time, and knowledge of opportunities to secure an intern position in a STEM laboratory [7]. This issue was heightened during the COVID-19 pandemic as students had limited research opportunities and were not effectively able to explore and learn new skills to inform their future careers [8,9]. Interventions in the classroom setting have proven to increase self-efficacy in women and URM, as well as to increase their feelings of agency, and desire to pursue research. Class modules incorporating research have been shown to improve female students’ self-perception in inquiry skills and attitudes toward science [10]. Teaching undergraduate research skills in an engineering classroom found that 20% of participating students enjoyed research enough to pursue a Ph.D. degree [11].
Course-based undergraduate research experiences (CUREs) are considered to be the next generation of inquiry-based learning, which traditionally has engaged students in research that was not of wide interest to the scientific community. CUREs provide an opportunity for students to engage in research, such as through the collection and analysis of environmental samples, that could lead to important and relevant scientific discoveries [12]. The data from a CURE should either be of interest to the wider scientific community or contribute to a larger database. Notably, CUREs are viewed as an inclusive model for engaging students in research [13,14,15]. When research is included as part of college courses and all students take part in the research project, there is more equitable access to research opportunities, which bring significant and well-documented advantages for the learning process [7,16]. This practice is encouraged by the National Academies of Science, Engineering, and Medicine as a way to increase inclusivity, encourage collaborative work, and expose more students to the research methodology of particular scientific disciplines [17]. Because outcomes are unknown, students can experience the excitement of true discovery. Further, when research embedded in courses contributes to a collective dataset, students feel part of a larger research community [18] and can promote conversations between scientists and students outside of class [19].
Coding, remote sensing, and data visualization are useful skills for undergraduate students to learn. Learning scientific and problem-solving skills should not be limited to only STEM majors, but also to non-STEM students to increase science literacy among all citizens [20,21]. Computational skills are necessary for fields beyond engineering and computer science, and a study has found utility in teaching these skills to non-STEM students [22]. Foundationally, programming encompasses the wider practice of conceptualizing and visualizing abstract data and translating it into other situations which develops one’s critical thinking further [23]. Coding skills can further be used to popularize remote sensing skills which can be utilized to help students understand the human–environment interaction [24,25].
It is important to increase students’ self-efficacy and sense of agency to visualize environmental change and to gauge their interest in a career in STEM research, especially during the COVID-19 pandemic. Studies have yet to investigate the value of teaching remote sensing and computational skills to STEM and non-STEM students. The goal of this study was to integrate remote sensing modules in an engineering, upper-division class (STEM class) and freshman writing course with a focus on the environment (non-STEM class) and understand how doing so influenced the students′ feelings of agency and self-efficacy in using the skills learned and interest in STEM research. The STEM students also partook in a CURE to apply their skills from the remote sensing modules on an original research topic.

2. Materials and Methods

2.1. Background on the Courses

This study was implemented in two different courses at a large public research university in southern California during the winter term of 2022. During this term, the first two weeks of the courses were virtual due to rising COVID-19 cases in the university’s county. The rest of the term was hybrid. The first course is an upper-division engineering course (STEM class) focused on the chemical fate and transport in aquatic environments. This 10-week course usually takes place once a year with around 50 students self-enrolling in the class. The course was taught by one professor with three part-time (25%) teaching assistants. These students are typically STEM majors in their junior or senior year at the university. The second course where the intervention was implemented is a freshman writing cluster course (non-STEM class) focused on food systems through the lens of the environment and sustainability. The class is offered yearlong with approximately 150 mainly non-STEM students self-enrolling to fulfill science and writing general education requirements. In this course, four instructors collaboratively taught different portions of the class with four teaching assistants and the intervention took place during two of the professors’ instructional periods.
To gauge the demographics of the students in both classes, participants self-reported their information in a survey. The frequencies and percentages of student demographic information for the two classes including data for students who fully participated in the study are shown in Table 1. A first-generation student herein was defined as “a student whose parent(s)/guardian(s) have no education experience past high school”, in accordance with a definition from the US Department of Education [26]. Participating students were allowed to select one or multiple racial and ethnic identities and underrepresented minorities (URM) were classified based on three racial and ethnic groups—blacks, Hispanics, and American Indians or Alaska Natives—in accordance with the National Science Foundation classification [27]. In the STEM class (n = 25), 44% female students, 52% male, and 4% non-binary were reported. Those students who were identified as first-generation students represented 24% of the total, and 16% were identified as URM. In the non-STEM class (n = 95), 75%, 22%, 1%, and 1% of participants were identified as female, male, gender-nonconforming, and preferred not to answer, respectively. Among the same participants, approximately 24% identified as first-generation, and 33% were classified as URM. At the institutional level, students are 58% female, 41% male; 31% of them are first-generation students; and around 23% are classified as URM.

2.2. The Intervention

The intervention involved two modules consisting of both in-class instruction and assignments. Students learned the basics of Google Earth Engine (GEE) through the two modules over a three-week time span. GEE is a cloud-based geospatial analysis platform that allows users to visualize and analyze satellite-derived data. GEE may be used with JavaScript and Python, but in this intervention, we used the JavaScript Application Programming Interface (API). Throughout the class instruction, students were introduced to GEE with interactive tutorials with guidance from the instructor. Topics of the tutorials involved climate change effects on sea ice, sea surface temperature, and deforestation. Tutorials consisted of pre-created and tested scripts adapted from existing Geospatial Ecology and Remote Sensing (GEARS) labs (www.gears-lab.com) (accessed on 20 February 2021) that students were able to paste into their code editors in real-time. During in-class instruction, students learned how to change the area of analysis and the time frame within the coded script, create maps and charts for analysis, and learned about different satellite missions and their different properties.

2.3. Assignments

There were two short assignments following in-class instruction for students to explore the basic functionalities of GEE and begin to compare results from different locations and time frames. The objectives of the assignments were for students to learn to modify scripts with a view to answering questions regarding environmental change and to identify potential causes (i.e., climate and/or anthropogenic causes). The students were required to create new maps in addition to their responses.
In Assignment 1, students visualized albedo, sea surface temperature, and sea ice cover around the world using 500 m Daily MODIS Albedo and 4 km NOAA AVHRR Pathfinder images. Students were asked to visualize a part of the world different from the example and explain why some areas have different values and characteristics for the respective parameters.
In Assignment 2, students used true color imagery to visualize the campus. In particular, they used a false-color composite to look at vegetation and calculated the normalized-difference vegetation index (NDVI) using Sentinel-2 MSI images. Students applied the same skills to examine deforestation in Rondonopolis, Brazil, due to the edge effects of agricultural conversion.
The STEM students had an additional assignment before the CURE to prepare them for their respective projects. Since the CURE would involve using remote sensing to look at the effects of the COVID-19 anthropause on water quality during and after the pandemic, students read an article that conducted a similar analysis in Belize [28]. The STEM students completed a reading guide assignment based on this article to indicate an understanding of the appropriate background and methods for the CURE project.

2.4. The CURE

In the second half of the term, the STEM students were introduced to the CURE portion of the class. The project involved using a water quality remote sensing tool to investigate changes to water quality due to the COVID-19 anthropause (global pause in human activity). Students were assigned to randomized groups of three to five students and selected a region of interest for their project. The students used a modified version of the Optical Reef and Coastal Area Assessment (ORCAA) tool created by the Belize and Honduras Water Resources NASA DEVELOP team (https://github.com/NASA-DEVELOP/ORCAA, accessed on 25 August 2020). In this tool, Sentinel-2 MSI and Aqua MODIS satellite imagery are used to visualize water quality changes in coastal areas around the world. Our version was adapted to be shorter and allow for students to modify dates and locations within the code editor as opposed to using the original user interface widgets. Sentinel-2 imagery is used to visualize turbidity, colored dissolved organic matter, chlorophyll-a, and normalized difference chlorophyll index. Sea surface temperature, chlorophyll-a, Kd (490) (a proxy for water clarity), and particulate organic carbon can be assessed with Aqua. The student groups were free to select any place around the world for the CURE. The students had to identify two nearby locations in their study region for comparison: one where they suspected the anthropause would have affected water quality (i.e., port, large city) and a control site. A total of 15 STEM student groups analyzed the following areas: Thailand, Singapore, San Diego (CA, USA), Port of Long Beach (CA, USA), Australia, Nigeria, Maldives, India, Houston (TX, USA), Ho Chi Minh City (Vietnam), Hawaii (USA), and Alaska (USA). The students wrote a final report giving background on how COVID-19 shutdowns affected their area of interest in terms of travel and commerce and attempted to explain water quality findings during and after the COVID-19 shutdown using scientific literature and regional news articles. The report also included their maps and time series plots showing water quality parameters for each location. The students augmented their conclusions through supplemental spreadsheets of their time series data and images of their maps. Templates for the project deliverables are located in the Supplementary Materials.

2.5. Surveys

Confidential pre- and post-surveys were used to assess shifts in the students’ sense of self-efficacy in coding and remote sensing, and in their interest in science and an environmental research career. The surveys were administered through Google Forms which collected responses anonymously. Previously, we had used similar assessment methods which revealed an increased interest in science among K-12 students through service learning research courses [29,30]. The surveys included a five-point Likert scale, free response, and multiple-choice questions. There were five core Likert statements in both surveys for students to rate their ability to modify code and understand remote sensing, and to rate their interest in science and in having an environmental research career (Table 2). These Likert statements were intended to reveal students’ self-efficacy in coding and remote sensing, their perceptions about science, and their career interests—as done in other educational studies [31,32,33]. The first three questions of each survey asked for the students’ favorite number, the name of their first best friend, and the name of their first pet to facilitate pairing the pre- and post-surveys as previously described in Jay et al., 2019 [34]. The pre-survey had additional questions on demographics, major, year, gender, and identification as a first-generation college student. The post-survey included additional multiple-choice and free-response questions on the effect of the pandemic on their ability to engage in a research experience. The surveys were approved by the university’s Institutional Review Board.

2.6. Statistical Analysis

Survey results were processed and analyzed using Excel, RStudio, and Python. The survey data were screened for duplicate entries, typos, and inconsistent response entries in RStudio. The pre- and post-surveys were paired for students using Fuzzy Lookup in Excel using the first three questions. Python was then utilized for statistical processing and visualization of the paired data. Different student identities were grouped for analysis by gender, first-generation, and URM with the Pandas package. Likert statement results were visualized using the Plot_Likert and Pyplot extensions of Matplotlib in Python. The SciPy.Stats sub-package was used to calculate the means for the Likert responses based on each grouping and question and mean differences in the paired data. Each group was tested for normality using the Shapiro–Wilk test. Our data presented non-normal distributions and thus the Wilcoxon signed-rank test was performed in Python through SciPy.Stats library, at a 95% level of significance for each student grouping and Likert statement. The COVID-19 free responses were recorded into more meaningful categories where recurring themes were identified, such as lack of opportunity and financial burden.

3. Results

In the two courses, the non-STEM class had 160 students enrolled and 95 pairable surveys (59% response rate), and the STEM class had 60 students enrolled which resulted in 25 pairable surveys (41% response rate). In reporting summative gains for each goal, students in both courses demonstrated increased confidence values regarding their environmental literacy and ability to modify and apply code for research purposes (Figure 1). Additional Likert plots are in the Supplementary Materials.

3.1. Non-STEM Case Study

In pre-course and post-course surveys, respondents in the non-STEM course in all concerned demographic groups increased their mean response score (on a scale of 1 to 5) regarding skill-based assessments (Q1: coding in environmental research relationship, Q2: coding edits—literacy, and Q3: remote sensing) indicating some gained knowledge and confidence (Table 3). Based on the Wilcoxon signed-rank p-values, all non-STEM students had statistically significant increases for Q1, Q2, and Q3. Most notably, non-STEM females displayed greater differences (p ≤ 0.001) after the modules compared to the non-STEM males (p ≤ 0.01). All first-generation and URM non-STEM students had statistically significant increases (p ≤ 0.001) in their selections for Q1, Q2, and Q3.
The responses for interest-based assessment (Q4: science interest and Q5: environmental research career interest) exhibited marginal improvements. Based on Figure 1, non-STEM students displayed a wide range of selections on the Likert scale for Q4 and Q5. Concerning interest in science (Q4), a greater proportion of students had neutral responses (33–36%) compared to interest in a career in research (25–28%). There were no statistically significant increases in the mean responses for Q4 and Q5. The only identities to see marginal increases in mean response were females, non-first-generation students, and URM in the non-STEM class.

3.2. STEM CURE Case Study

Based on Figure 1, similarly to the non-STEM students, STEM students displayed positive movement in confidence concerning coding (Q1 and Q2) and understanding of remote sensing (Q3). For Q1, there were no STEM students that selected “Disagree” or “Strongly Disagree” before the intervention. Following the modules and CURE, there were no “Neutral” responses for Q1 in the STEM class. For Q2, STEM students had increases in the proportion of students selecting “Agree” and “Strongly Agree” following the intervention. Q3 had the most drastic shift in movement among the Likert results for the STEM students where only 24% of students selected “Agree” and “Strongly Agree” for their understanding of remote sensing at the beginning of the course compared to 84% of students following the intervention. For the differences in mean responses, males, non-first-generation, and non-URM STEM students had statistically significant increases for Q1, as did non-first-generation students for Q2. For Q3, females, males, non-first-generation, and non-URM students had statistically significant differences in their mean responses.
For the STEM students, there was less movement for Q4 and Q5 with most students already having a strong interest in science (92%) and interest in a career in environmental research (60%) at the start of the course. STEM males, first-generation, non-first-generation, and URM students had increases in their mean responses for Q4. For Q5, only females and non-first-generation STEM students had increased mean responses through the course.

CURE Reports

Additionally, students in the STEM course utilized their new skills in accessing remotely-sensed data to compile their group research report on water bodies of their choosing. The students contributed water quality data and analysis for more than fifteen water regions spanning nine countries. Students tested their hypotheses about the anthropogenic effects on water quality by observing differences in indicators, including sea surface temperature, Kd (490), chlorophyll-a, particulate organic carbon, and turbidity. In the reports, some groups reported improvements in water quality while others found deteriorations.
Two exceptional student project reports uncovered improvements in areas with heavy marine traffic. The first group used the ORCAA tool for the Bangkok Port in Thailand and compared water quality during 2018 (1 January 2018–31 December 2018) to that in 2020 (1 January 2020–31 December 2020). The Bangkok Port is the largest in Thailand and is located in the nation’s capital. The students’ most notable finding was the improvement in turbidity in 2020 (Figure 2C) at the port compared to 2018 (Figure 2A) as shown in their created maps. Another group selected Hawaii for their region of interest and similarly noticed an improvement in turbidity in Kahului Bay in Hawaii, USA (Figure 3). The Kahului Harbor was contained in the area of interest and is the main port of Maui for receiving both commercial and tourist-based marine traffic. The students compared the water quality during part of 2018 (1 March 2018–30 November 2018) to 2020 (1 March 2020–30 November 2020) using Sentinel-2 imagery. Their time series plots in particular show a decline in turbidity following the COVID-19 shutdown in Hawaii in March 2020 (Figure 3D).

3.3. Research Opportunities during the COVID-19 Pandemic

Regarding research opportunities, 44% of STEM students and 9.5% of non-STEM students responded that they had experienced difficulties accessing research due to the pandemic. Among 22 free-response answers for both classes, eight students in the STEM class and five students in the non-STEM class stated that there were limited research opportunities on campus and for obtaining internships. A couple of responses involved financial burden and lack of information on how to find research opportunities among first-year non-STEM students. A couple of STEM students mentioned partaking in research activities virtually or in-person despite the pandemic. Post CURE assessments, a couple of STEM students mentioned anecdotally that their applied skills in coding through Google Earth Engine helped them obtain internship positions through their ability to demonstrate competency and enthusiasm for their research projects during the interview stage.

4. Discussion

The study explored outcomes of remote sensing modules in environmental science and engineering courses containing participants from both STEM and non-STEM backgrounds. Implementation of these modules in both STEM- and non-STEM-based courses showed positive outcomes in scientific methodologies, coding skills, and attitudes toward scientific fields for all students. Equitable access to research skills benefits underrepresented demographics in equal if not greater proportions. Here, in our study we saw a significant increase in confidence in coding, particularly among non-STEM females. Similarly, other computer science studies found that though females were less exposed to coding than males, females have a similar or even better aptitude for computing than males [31,35]. Thus, our intervention was potentially able to provide early exposure to coding to undergraduate females. For the duration of the study, positive growth was fostered directly in the STEM course through the modules and research applications that utilized CURE practices and pedagogy. Scaffolding of computational natural science modules leading to independent research has been previously shown to increase self-efficacy in coding and persistence in STEM [33]. Even without the scaffolding through the modules, the non-STEM class participants benefited and exhibited short-term growth from receiving lessons centered around CURE pedagogy. Student agency towards their learning and skills will become the building blocks for further integration of their work with the sciences and the efforts to invite diversity into the field. The results are similar to findings in implementation and observation of earlier completed CURE studies. A recent CURE in a large physics class during the COVID-19 pandemic utilized NOAA GOES satellite data to investigate flare frequency. In this study, students exhibited greater self-efficacy in coding, research skills, and working as a team [36]. Previous CUREs with field and laboratory components in the life sciences helped motivate students to consider or confirm their desire to perform fieldwork and laboratory research while contributing to the Prevalence of Antibiotic Resistance in the Environment (PARE) project [16,37]. Notably, a marine biology CURE increased the science identity in Latinx students [38].
Through the CUREs, the STEM students were able to engage in original research regarding the COVID-19 anthropause and its rebounding effects on water quality. Distinct from usual laboratory and research classes, students had autonomy in selecting the region of interest for their projects and had to investigate region-specific trends for themselves. There are several published and ongoing studies on the impacts of the COVID-19 anthropause on the environment. Having the students engage in this CURE and create a database of their findings may allow for the collection of preliminary results for future investigation. Through our intervention, students were able to identify improvements in turbidity in Hawaii and Bangkok in a short amount of time (10 weeks). A similar study involved senior and graduate students to detect land use changes via Landsat 8 imagery yielded in precise land use change detection in Texas [39]. The scientific data provided by these CUREs has been evaluated to be reliable and of sufficient quality for research use, especially when compared to data collected by a non-experimental (i.e., not in a CURE) group [40], which supports an ideology for the increased inclusion of CUREs to contribute to large environmental investigations. Research groups may use such databases in confidence, and in turn, the students gain new skills through working on an original research topic that can pique their interest in similar fields.
In comparing the differences in mean responses between similar groups (i.e., female vs. male participants, first-generation vs. not, STEM vs. non-STEM, and underrepresented minority vs. not) the results were found to be not statistically significantly different for interest in a career in environmental research. The underrepresented groups, therefore, did not appear to benefit considerably more than their peers from the CUREs, but the process is still conducive to the collective advancement of scientific understanding and personal self-confidence. A recent review of the state of undergraduate research experiences states that CUREs alone do not offer the necessary mentoring to support URM students to persist in STEM [41].
This study shows the viability of GEE as a powerful teaching tool for investigating environmental change, working with remote sensing data, and learning to code in JavaScript. GEE is advantageous in that it houses many datasets and students can get access to imagery without downloading space-intensive images onto their computers. It is also accessible to any student with internet access regardless of their computer’s operating system without the need to download costly software. A recent study found that using Google Earth tools can help students bolster grades and increase critical thinking skills in physical geology courses [42]. In the future, the integration of remote sensing products in teaching will be more important as online learning education continues to grow [43].

5. Conclusions

In this study, we examined the effects of remote sensing modules on confidence in students’ ability to alter code, investigate environmental change, and in their interest in science and a career in environmental research. The study involved both STEM and non-STEM students where the STEM students engaged in a CURE on COVID-19 anthropause effects on water quality. Through the intervention, students’ confidence in their ability to alter code, understand remote sensing, and ability to apply skills on environmental issues significantly increased among all student identities for both classes. There were no significant differences in students’ existing interest in science and career in environmental research through the one-term study. Nonetheless, GEE proved to be an accessible tool for teaching remote sensing to a wide range of students compared to other geospatial tools. A CURE was also an excellent option for making research more accessible to advanced engineering and environmental science students, especially during the COVID-19 pandemic when opportunities were scarce. Educators in environmental sciences and engineering should consider integrating Google Earth Engine as a teaching resource in their courses and use CUREs to capitalize on students’ curiosity and decrease barriers to partaking in original research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su151511995/s1, Figure S1: Likert plots for male and female students; Figure S2: Likert plots for first-generation and non-first-generation students; Figure S3: Likert plots for underrepresented minority (URM) students and non-URM students; Pre and Post Surveys; Remote Sensing Modules: Lab 1: Albedo, Sea Surface Temperature, and Sea Ice, Lab 2: Deforestation; CURE Assignment: ORCAA Project Overview, ORCAA Tool Activity, ORCAA Tool Project Template.

Author Contributions

Conceptualization, I.A.C. and J.A.J.; methodology, I.A.C. and J.A.J.; software, I.A.C. and J.A.J.; formal analysis, I.A.C., L.H. and M.C.; investigation, I.A.C., L.H. and M.C.; resources, I.A.C., M.C., B.C., E.S., C.S., P.S., Z.W. and J.A.J.; data curation, I.A.C., L.H., T.C. and M.C.; writing—original draft preparation, I.A.C. and L.H.; writing—review and editing, I.A.C., L.H., W.-C.H. and J.A.J.; visualization, I.A.C. and L.H.; supervision, S.S., M.M. and J.A.J.; project administration, I.A.C. and J.A.J.; funding acquisition, I.A.C., J.A.J. and C.M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by UCLA’s Center for Diverse Leadership in Science, the Joan Doren Family Foundation, NSF NRT: Graduate Traineeship in Integrated Urban Solutions for Food, Energy, and Water Management-DGE-1735325, and the California NanoSystems Institute. This work was performed in part at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of California, Los Angeles (IRB #21-002104, certified “Exempt” on 17 December 21).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data which supports the conclusions of the current article will be made available on demand.

Acknowledgments

We would like to thank The Center for the Integration of Research, Teaching, and Learning (CIRTL) at UCLA staff for their help and support and all student participants.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Likert plots for pre- and post-survey for non-STEM and STEM participants.
Figure 1. Likert plots for pre- and post-survey for non-STEM and STEM participants.
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Figure 2. Turbidity results from CURE on Bangkok Port in Thailand: (A) map of average turbidity before COVID-19 anthropause; (B) time series of daily average turbidity of Bangkok before COVID-19 anthropause; (C) map of average turbidity during the COVID-19 anthropause; (D) time series of daily average turbidity of Bangkok during the COVID-19 anthropause.
Figure 2. Turbidity results from CURE on Bangkok Port in Thailand: (A) map of average turbidity before COVID-19 anthropause; (B) time series of daily average turbidity of Bangkok before COVID-19 anthropause; (C) map of average turbidity during the COVID-19 anthropause; (D) time series of daily average turbidity of Bangkok during the COVID-19 anthropause.
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Figure 3. Turbidity tesults from CURE on Kahului Bay in Hawaii, USA: (A) map of average turbidity before COVID-19 anthropause; (B) time series of daily average turbidity of Hawaii before COVID-19 anthropause; (C) map of average turbidity during the COVID-19 anthropause; (D) time series of daily average turbidity of Hawaii during the COVID-19 anthropause.
Figure 3. Turbidity tesults from CURE on Kahului Bay in Hawaii, USA: (A) map of average turbidity before COVID-19 anthropause; (B) time series of daily average turbidity of Hawaii before COVID-19 anthropause; (C) map of average turbidity during the COVID-19 anthropause; (D) time series of daily average turbidity of Hawaii during the COVID-19 anthropause.
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Table 1. Frequency and percent of student classification by gender, first-generation student, underrepresented minority, and COVID-19 pandemic research opportunity response per class.
Table 1. Frequency and percent of student classification by gender, first-generation student, underrepresented minority, and COVID-19 pandemic research opportunity response per class.
CourseCategoryFrequencyPercent (%)
Gender
STEMFemale1144
Male1352
Non-binary14
Non-STEMFemale7276
Male2122
Gender-nonconforming11
Prefer not to answer11
First-generation Student
STEMNo1976
Yes624
Non-STEMNo7276
Yes2324
Underrepresented Minority Students (URM)
STEMNon-URM2184
URM416
Non-STEMNon-URM6366
URM3234
Research opportunity impacted by pandemic
STEMMaybe312
No1144
Yes1144
Non-STEMMaybe4143
No4547
Yes910
Table 2. Likert statements included in both pre- and post-surveys.
Table 2. Likert statements included in both pre- and post-surveys.
Core Likert Statements
Q1. I am confident in my ability to leverage current coding skills to investigate environmental change.
Q2. I am confident in my ability to make small edits to code.
Q3. I understand remote sensing for studying the environment.
Q4. I have a strong interest in science.
Q5. I would consider a career in environmental research.
Table 3. Mean differences for the five core Likert statements between pre- and post-surveys for paired student responses per student grouping. Three asterisks denote Wilcoxon signed-rank test p-values ≤ 0.001, two asterisks denote p-values ≤ 0.01, and one asterisk is p-values ≤ 0.05.
Table 3. Mean differences for the five core Likert statements between pre- and post-surveys for paired student responses per student grouping. Three asterisks denote Wilcoxon signed-rank test p-values ≤ 0.001, two asterisks denote p-values ≤ 0.01, and one asterisk is p-values ≤ 0.05.
CategoryClassQ1Q2Q3Q4Q5
FemaleSTEM0.3640.6361.455 **0.0000.182
Non-STEM1.111 ***0.986 ***1.056 ***0.0000.125
MaleSTEM0.462 **0.4621.154 **0.231−0.154
Non-STEM0.857 **0.667 *0.714 ***0.000−0.286
First-GenerationSTEM0.3330.5001.6660.166−0.333
Non-STEM1.217 ***1.217 ***0.957 ***−0.391−0.13
Non-First-GenerationSTEM0.421 *0.579 *1.211 ***0.1050.105
Non-STEM1.000 ***0.819 ***0.972 ***0.1390.083
URMSTEM0.7501.0001.2500.7500.000
Non-STEM0.969 ***1.031 ***0.969 ***0.1250.094
Not URMSTEM0.333 *0.4761.333 ***0.0000.000
Non-STEM1.095 ***0.857 ***0.968 ***−0.0480.000
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Callejas, I.A.; Huang, L.; Cira, M.; Croze, B.; Lee, C.M.; Cason, T.; Schiffler, E.; Soos, C.; Stainier, P.; Wang, Z.; et al. Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students. Sustainability 2023, 15, 11995. https://doi.org/10.3390/su151511995

AMA Style

Callejas IA, Huang L, Cira M, Croze B, Lee CM, Cason T, Schiffler E, Soos C, Stainier P, Wang Z, et al. Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students. Sustainability. 2023; 15(15):11995. https://doi.org/10.3390/su151511995

Chicago/Turabian Style

Callejas, Ileana A., Liana Huang, Marisol Cira, Benjamin Croze, Christine M. Lee, Taylor Cason, Elizabeth Schiffler, Carlin Soos, Paul Stainier, Zichan Wang, and et al. 2023. "Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students" Sustainability 15, no. 15: 11995. https://doi.org/10.3390/su151511995

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