Progress 01/01/19 to 12/31/23
Outputs
Target Audience:The target audience was undergraduate students in assorted majors from assorted institutions who have interest in data science for agriculture. Some had data for research interests and some had data for agricultural management interests. Background knowledge in agricultural sciences was varied. Changes/Problems:Recruting and getting enough participants to accept offers to participate is a challenge. It is difficult to compete with highpaying internships. Weincreased the level of support for 2023 by diverting funds intended for a "cohort manager". This did result in us meeting a total student count goal as initially proposed. Due to the relatively short (10 week program), students often expressed interest in starting the independent projects sooner. However, due to the nature of our formal instruction during the first 8 weeks, it is difficult to have students commence the project until we had convered prerequisite knowledge and skills. In the end it worked well, but students were sometimes stressed due to the back-loading of the independent work. What opportunities for training and professional development has the project provided?47 students participating in Experiential Learning with Data Tools for Agriscience at Purdue University during the summers of 2021-2023 increased their levels of awareness of data-focused careers, self-efficacy for working with data, and understanding of how to use data to answer questions and solve problems. Students were highly satisfied with their experiences at Purdue and were significantly more likely to aspire to graduate study after participating in the program. The evaluation suggests that the program was successful in developing cohorts of students prepared for, and interested in further study of, data-focused careers in agriscience. In addition to the participating students, several graduate students from ABE, ECE, and STAT departments were involved providing some instruction and interaction with the undergraduates. They were mentors with regard to graduate studies and as a result, of the participants who have graduated since the program began (26), 11 or 42% did pursue advanced degrees. In 2022 and 2023, several (about 6) graduate students not involved with instruction who were interested in the program content were invited to participate in the formal instructionm portion of the program. They did not get exposure to all aspects (spreadsheets, GIS, Python, and R), but joined for sessions of interest. How have the results been disseminated to communities of interest?Structure of this experiential educational program and results with respect to student learning were shared with: ASABE organization through technical committee meetings ASABE elibrary website via a teaching module document (in review) with other Purdue and IoT4Ag research community via meetings regarding summer programs. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
The overall goal of the project is to equip students with data science knowledge with specific applications to agriculture and food systems. The program objectives and student learning goals are: • understand application program interfaces (APIs) well enough to collect data and wrangle data into informed decisions • perform proper statistical analysis and be able to communicate process and results • understand the industry opportunities in agri-business data science These students gained firsthand experience in a laboratory classroom setting as well as through an independent project. This experience included close mentoring by faculty in assorted disciplines (Agricultural & Biological Engineering, Statistics, and Electrical and Computer Engineering) and some engagement with graduate students in these fields. An assortment of activities and tours enabled them to see and experience data collection and agricultural operations and also served as social networking opportunities. The Evaluation and Learning Research Center (ELRC) partnered with the program team to support evaluation of the development, implementation, and efficacy of this program. In 2019, the ELRC met with the program team approximately quarterly to: develop and review their program objectives, identify, and adapt existing survey instruments to measure the program's progress toward its objectives, and review initial data from the first cohort of students. The project was active for five years and planned on enrolling five cohorts. COVID-19 caused cancellation of the 2020 summer program. A larger cohort was enrolled the following year, in 2021. The program collected survey data from all participants at the end of the summer experience regarding their knowledge, beliefs, and attitudes about Ag Informatics. In 2021-2023, participants reflected on their knowledge, beliefs, and attitudes both BEFORE participation in the REEU and AFTER. The program team also solicited open-ended, written feedback from the participants on their experience. The ELRC reviewed these data; we offer summary below. Table 1 below describes the number of participants in the program evaluation each year and the evaluation methods used. This report presents cumulative findings from years 2021-2023--years in which information was collected from participants about their pre and post knowledge, beliefs and attitudes. There was no program in 2019 due to the COVID-19 pandemic. Participation in the other years were 9, 18, 7, and 13 students. FINDINGS The program evaluation assessed several outcomes of student participation in the REEU, including outcomes related to student ability, interest and knowledge of data processes, and data careers. These outcomes are: • awareness of data and data analysis-focused careers (career awareness), • interest in and aspiration for data-focused careers (data aspiration), • understanding of data creation and use (data understanding), • skills for working with data (data skills), and • self-efficacy for working with data (data efficacy). These outcomes were assessed through a series of survey items for which students rated their knowledge, skill, ability, or awareness related to these outcomes on a scale of 1-5 (1=none, 5=a great deal). Students completed the survey at the end of their REEU experience and were asked to rate their knowledge, skill, ability, or awareness at the time of the survey and before they began the REEU program. Each outcome wasassessed with multiple survey items which were then aggregated to create a scale score for each outcome. The median scale scores for each outcome were then compared across time (before participation and after) using Wilcoxon signed-rank test. Figure 1 displays the medians for each of these outcomes. Medians for all five outcomes of interest increased significantly after participation in the REEU program. Note: * indicates statistically significant change Additionally, the evaluation assessed several outcomes that are generally predictive of success in higher education settings: • ability to connect academic content to the real world, • ability to navigate the academic environment, and • ability to make connections with academic peers. These three outcomes were assessed via student responses to the post-participation survey and using the same methods described previously. Figure 2 displays median scale scores for each of these outcome variables before and after participation in the REEU. The analysis suggests achievement of all three desired outcomes; indicators of each were all significantly higher after participation in the summer program. Note: * indicates statistically significant change Students also rated their agreement with the statements, "I feel a sense of belonging at Purdue." and, "I have social support at Purdue." In the aggregate, for the three years 2021-2023, students were significantly more likely to agree with these statements after their participation in the summer REEU program--suggesting that the summer REEU program contributed to an enhanced connection to the university community. Figure 3 displays the medians for their level of agreement before and after participation. In 2021, 2022, and 2023, participants rated their likelihood of attending graduate school, seeking full-time employment, or pursuing self-employment after finishing their undergraduate studies before they participated in the summer REEU and after. Figure 4 displays the aggregate results for years 2021, 2022, and 2023. Students were more likely to aspire to graduate education after participating in the program. Aspiration to self-employment and other full-time employment were unchanged. Participants rated the value of each program component (class sessions, open ended assignments, independent research, faculty mentorship, cohort manager, peer mentoring, tours, social activities, and guest speakers) to their overall experience in the program. All components' median ratings were 4.5 or 5 representing "extremely valuable."
Publications
- Type:
Other
Status:
Submitted
Year Published:
2024
Citation:
A two-week module as introduction to Python was submitted to ASABE for a special data instruction collection.
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Progress 01/01/22 to 12/31/22
Outputs
Target Audience:The target audience was undergraduate students in assorted majors from assorted institutions who have interest in data science for agriculture. Some had data for research interests and some had data for agricultural management interests. Background knowledge in agricultural sciences was varied. Changes/Problems:Recruting and getting enough participants to accept offers to participate is a challenge. It is difficult to compete with high-paying internships. We have increased the support for 2023 with hopes to increase the yield on offers made. What opportunities for training and professional development has the project provided?The program itself was training for 7participants. Additionally, 4graduate students were engaged in the instruction to facilitate their growth in teaching and outreach to others. In their written feedback, students described a "close knit" learning community in which they learned "from world class faculty." This program provided students with potential role models and access to information about daily life and the career trajectories of researchers, graduate students, and faculty members. They also encountered and learned from a diverse group of peers and made connections across institutions. In open-ended comments on the survey students expressed strong satisfaction with the program--particularly with the knowledge and skills gained in data science, the community they formed, and connections formed with peers. Three examples are included below. "I liked the diversity of majors and people. I liked learning from world class faculty." "I enjoyed meeting people from other institutions and understanding how to use data science in agriculture." "I enjoyed the tours, faculty, and open projects the most." As a way to reconnect to past participants, we held a virtual "reunion" event. This was a most enjoyable virtual (Zoom) gathering for all who participated during which participants over 3 years of the program shared how they are using knowledge gained through this program in their careers or further studies. Perhaps most valuable was the motivation to the faculty to keep on delivering. Yet the alumni sharing experiences also appeared to be motivating to other students to see the diversity of applications of skills and knowledge. How have the results been disseminated to communities of interest?We shared some experiences via a community built around a USDA/NIFA Higher Education Challenge Grant having team members from Purdue University, University of Kentucky, and Tuskegee University. This program was mentioned in ASABE committee meetings, the Purdue Open Ag Technology and Systems Conference, and a regional Wabash Heartland Innovation Network annual meeting. After the final offering, we will summarize and share results at a professional meeting - most likely the American Society of Agricultural & Biological Engineers. What do you plan to do during the next reporting period to accomplish the goals?In 2023, we will repeat the program with some improvement. Suggestions from the 2022participants included: Include other forms of research communication training--such as paper/manuscript development. Create additional time for student-mentor relationship building and meetings (including near-peer mentors such as graduate students). Adding even more time to work on hands-on activities or research projects.
Impacts
What was accomplished under these goals?
The overall goal of the project is to equip studentswith data science knowledge with specific applications to agriculture and food systems. The program objectives and student learning goals are: • understand application program interfaces (APIs) well enough to collect data and wrangle data into informed decisions • perform proper statistical analysis and be able to communicate process and results • understand the industry opportunities in agri-business data science These students gained firsthand experience in a laboratory classroom setting as well as through an independent project. This experience included close mentoring by faculty in assorted disciplines (Agricultural & Biological Engineering, Statistics, and Electrical and Computer Engineering) and some engagement with graduate students in these fields. An assortment of activities and tours enabled them to see and experience data collection and agricultural operations and also served as social networking opportunities. The Purdue University Evaluation and Learning Research Center (ELRC) partnered with the program team to provide support for the evaluation of the development, implementation and efficacy of this program. In 2022, the program collected survey data from all participants at the end of the summer experience regarding their knowledge, beliefs and attitudes about Ag Informatics. Participants reflected on their knowledge, beliefs, and attitudes both BEFORE participation in the REEU and AFTER. The program team also solicited open-ended, written feedback from the participants on their experience. In summary: The program evaluation assesses several outcomes of student participation in the REEU. The first area of interest are outcomes related to student ability, interest and knowledge of data processes and data careers. These outcomes are: awareness of data and data analysis-focused careers (career awareness), interest in and aspiration for data-focused careers (data aspiration), understanding of how data are created and used (data understanding), skills for working with data (data skills), and self-efficacy for working with data (data efficacy). These outcomes were assessed through a series of survey items for which students rated their knowledge, skill, ability, or awareness related to these outcomes on a scale of 1-5 (1=none, 5=a great deal). Students completed the survey at the end of their REEU experience and were asked to rate their knowledge, skill, ability, or awareness at the time of the survey and before they began the REEU program. Each outcome was assessed with multiple survey items which were then aggregated to create a scale score for each outcome. The median scale scores for each outcome were then compared across time (before participation and after) using Wilcoxon signed-rank test. The medians for each of these outcomes are displayed below in Figure 1. Medians for four of the five outcomes of interest increased significantly after participation in the REEU program. Data aspiration showed positive change, but it was not a statistically significant change as indicated by Wilcoxon Signed Ranks Test. Additionally, the evaluation assessed several outcomes that are generally predictive of success in higher education settings: ability to connect academic content to the real world, ability to navigate the academic environment, and ability to make connections with academic peers. These three outcomes were assessed via student responses to the post-participation survey and using the same methods described previously. Figure 2 displays median scale scores for each of these outcome variables before and after participation in the REEU. None of these outcomes showed improvement. This suggests that this cohort did not necessarily gain valuable skills in areas that will help them succeed in all aspects of their educational career. However, their self-assessed skills before entering the program were relatively high--so there was little room for improvement. Participants were asked to rate the value of each program component to their overall experience in the program. The following components were rated by the participants: class sessions, open ended assignments, independent research, faculty mentorship, cohort manager, peer mentoring, tours, social activities, and guest speakers. All components' median ratings were 4 or 5 representing "very valuable or extremely valuable," respectively.
Publications
- Type:
Other
Status:
Published
Year Published:
2022
Citation:
https://ag.purdue.edu/news/2022/07/reeu-summer-program-provides-a-window-into-agriculture-post-ag.html
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Progress 01/01/21 to 12/31/21
Outputs
Target Audience:The target audience was undergraduate students in assorted majors from assorted institutions who have interest in data science for agriculture. Some had data for research interests and some had data for agricultural management interests. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The program itself was training for 18 participants.Additionally, 5graduate students were engaged in the instruction to facilitate their growth in teaching and outreach to others. How have the results been disseminated to communities of interest?Recruitment into this program involved extensive outreach to peer institutions and minority serving institutions. While the formal results have not been disseminated, the reactions of former participants is included in the communications to recruit the next cohorts. Additionally, this program has been mentioned in venues such as the Purdue Open Ag Technology and Systems Center annual conference, the Microsoft Research Summit, ASABE commitee meetings and conference sessions, and the GCTC Ag/Rural Broadband Supercluster meetings. What do you plan to do during the next reporting period to accomplish the goals?In 2021, we will repeat the program with some improvement. Suggestions from the 2021 participants included: Initiate the research project earlier in the program: students would like understand the expectations for their research projects and begin formulating their ideas and projects during the first weeks of the program. Decrease the amount of direct instruction/lecture: students found the hands-on data experiences, hands-on tours, and social activities to be extremely valuable. It would be ideal to build on these successes and, if possible, increase the amount of time spent working with data and data tools, experiences with agri-data tools in the field, and organized social events for the cohorts. These will be taken into consideration, however, most students do not realize the value of the direct instruction and the necessity of some of that before they select and approach an independent project. One area for improvement is to make some of this more explicit to the students so they have a better understanding not just of content, but approach toward teaching content (especially since some will inevitably become teachers at some level).
Impacts
What was accomplished under these goals?
The overall goal of the project is to equip 47 students, over 5 years, with data science knowledge with specific applications to agriculture and food systems. The program objectives and student learning goals are: • understand application program interfaces (APIs) well enough to collect data and wrangle data into informed decisions • perform proper statistical analysis and be able to communicate process and results • understand the industry opportunities in agri-business data science Due to COVID-19, we had to skip 2020, but with 18 participating in 2021 (added to 8 + a cohort manager in 2019), we are back on track with just over half having this experience with two summers (2022, 2023)to go. These students gained firsthand experience in a laboratory classroom setting as well as through anindependent project.This experience included close mentoring by faculty in assorted disciplines (Agricultural & Biological Engineering, Statistics, and Electrical and Computer Engineering) and some engagement with graduate students in these fields. An assortment of activities and tours enabled them to see and experience data collection andagricultural operations and also served as social networking opportunities. The Purdue University Evaluation and Learning Research Center (ELRC) partnered with the program team to provide support for the evaluation of the development, implementation and efficacy of this program.In 2021, the program collected survey data from all participants at the end of the summer experience regarding their knowledge, beliefs and attitudes about Ag Informatics. Participants reflected on their knowledge, beliefs, and attitudes both BEFORE participation in the REEU and AFTER. The program team also solicited open-ended, written feedback from the participants on their experience. In summary ... The program evaluation assesses several outcomes of student participation in the REEU. The first area of interest are outcomes related to student ability, interest and knowledge of data processes and data careers. These outcomes are: awareness of data and data analysis-focused careers, interest in and aspiration for data-focused careers, understanding of how data are created and used, skills for working with data, and self-efficacy for working with data. These outcomes were assessed through a series of survey items for which students rated their knowledge, skill, ability, or awareness related to these outcomes on a scale of 1-5 (1=none, 5=a great deal). Students completed the survey at the end of their REEU experience and were asked to rate their knowledge, skill, ability, or awareness at the time of the survey and before they began the REEU program. Each outcome was assessed with multiple survey items which were then aggregated to create a scale score for each outcome. The median scale scores for each outcome were then compared across time (before participation and after) using Wilcoxon signed-rank test. The medians for each of these outcomes are displayed below in Figure 1. Medians for all five outcomes of interest increased significantly after participation in the REEU program. Additionally, the evaluation assessed several outcomes that are generally predictive of success in higher education settings: ability to connect academic content to the real world, ability to navigate the academic environment, and ability to make connections with academic peers. These three outcomes were assessed via student responses to the post-participation survey and using the same methods described previously. Figure 2 displays median scale scores for each of these outcome variables before and after participation in the REEU. All three outcomes showed significant improvement. This suggests that students are gaining valuable skills not just in data science, but also in areas that will help them succeed in all aspects of their educational career. Participants were asked to rate the value of each program component to their overall experience in the program. The following components were rated by the participants: class sessions, open ended assignments, independent research, faculty mentorship, cohort manager, peer mentoring, tours, social activities, and guest speakers. All components' median ratings were 4 or 5 representing "very valuable or extremely valuable," respectively. In their written feedback, students described a warm and welcoming learning community in which they learned "an incredible amount of information" from accessible and relatable faculty, staff, and students with whom they hoped to stay connected. This program provided students with potential role models and access to information about daily life and the career trajectories of researchers, graduate students, and faculty members. Students were significantly more likely to agree that they felt a sense of belonging to Purdue University after participating. One student mentioned that Purdue has become their choice for continued education. In open-ended comments on the survey students expressed strong satisfaction with the program--particularly with the knowledge and skills gained in data science, the community they formed, and increased awareness of the potential uses for their new skills in future education and career areas. Three examples are included below. "Practically everything was enjoyable. The social activities fostered a really good community here that I will stay in contact with. The tours and graduate speakers were helpful for learning more about graduate school and research at Purdue. The coursework covered many different softwares that I now feel familiar with. I learned a lot." "Never have I ever experience data-related works in my major. However, thanks to the program, I learned more about this aspect of engineering, and I want to direct my career path to data science in agriculture." "The topic diversity was really great, and it was awesome to learn different things from different professors. The tours and presentations also really opened my eyes up to just how many different ways data science can be applied in agriculture."
Publications
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Progress 01/01/20 to 12/31/20
Outputs
Target Audience:The target audience is undergraduate students in assorted majors from assorted institutions who have interest in data science for agriculture. Unfortunately, although we had 13 participants accepted to participate (10F, 3 M), we had to cancel the program due to COVID-19. Changes/Problems:The major problem and change was COVID-19 restrictions keeping us from offering the program in 2020. We will either include more students in each of the coming years (2021-2023) to meet the target of the proposal, or offer the program an additional summer (2024) to make up for the loss of 2020. What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?Professional peers are aware of the program and its content and continue to assist with recruiting participants from assorted institutions around the U.S. What do you plan to do during the next reporting period to accomplish the goals?Regardless of COVID-19 (or other) limitations, we will offer the program in 2021 -- even if virtual only. Project personnel and students are more prepared for this type of instruction at this time than we were in 2020. We will also assess students and the program in the 2021 offering.
Impacts
What was accomplished under these goals?
Due to COVID-19, we did not offer the program because it is much more effective with "over the shoulder" supervision and interaction. We did work toward increasing data sets for use in instruction. Ongoing collaboration with others on related projects also helped to build a pool of resources and examples to share with future students. In a related vein, the project director worked with ASABE to sponsor a by-invitation-only session which is resulting in peer reviewed data science for education modules. Some of these may be integrated into thei REEU curriculum.
Publications
|
Progress 01/01/19 to 12/31/19
Outputs
Target Audience:The target audience was undergraduate students in assorted majors from assorted institutions who have interest in data science for agriculture. Some had data for research interests and some had data for agricultural management interests. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?The program itself was training for 8 participants and 1 student worker. Additionally, 2 graduate students were engaged in the instruciton to facilitate their growth in teaching and outreach to others. How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?In 2020, we will repeat the program with some improvement. Suggestions from the 2019 evaluation include: Initiate the research project earlier in the program: students would like understand the expectations for their research projects and begin formulating their ideas and projects during the first weeks of the program. Retain and ramp up highly rated components: students found the hands-on data experiences, hands-on tours, and social activities to be extremely valuable. It would be ideal to build on these successes and, if possible, increase the amount of time spent working with data and data tools, experiences with agri-data tools in the field, and organized social events for the cohorts. Additionally, participants expressed an interest in data science careers in agriculture (and knowledge about the existence of these careers), but less knowledge about how to transition into these careers after graduation from their home institution. It may be beneficial to introduce future cohorts to young agri data scientists and give examples of what types of entry-level positions or graduate education would be potential pathways into data science careers in agriculture.
Impacts
What was accomplished under these goals?
8 students completed the program in 2019. Findings from their feedback include: At the end of the program 66% of participants agreed or strongly agreed that they were confident that they could understand data processing and analytics. 88% of participants had confidence in their analytical skills at the end of the program. 100% had a strong understanding of the role of data science in agriculture science at the end of the program. Participants were asked to rate the value of each program component to their overall experience in the program. The following components were rated by the participants: class sessions, open ended assignments, independent research, faculty mentorship, cohort manager, peer mentoring, tours, social activities, and guest speakers. Of these, class sessions, faculty mentorship, tours, social activities, and guest speakers were rated the most valuable. Open ended assignments were rated as the least valuable. In their written feedback, students described a warm and welcoming learning community in which they learned "something new every day" from accessible and relatable faculty who "opened [their] eyes to new ways of thinking." This program provided students with potential role models and access to information about daily life and the career trajectories of researchers, graduate students and faculty members.
Publications
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