Performing Department
(N/A)
Non Technical Summary
An increasing human population and changing climate continue to stress the US ability to sustain food security, while maintaining economic and environmental vitality. Advances in agriculture technologies are key to success, but the workforce has an under-prepared skillset which is not being met by existing academic infrastructures."AI will not completely replace humans, but those who use AI will replace those who do not." this widely cited statement from a 2023 IBM report encapsulates a broader but urgent need in the agricultural workforce. We need innovative programs to train youth in the latest cutting-edge technology, not only in fields like data science and AI but across a range of skills, including Internet-of-Things (IoT) sensors, robotics and automation, and biotechnology. Students must learn to leverage data-driven insights to optimize crop yields, monitor environmental conditions, and implement precision irrigation techniques. Familiarity with advanced technologies in agriculture will prepare the next generation of professionals to address the challenges posed by a growing global population, climate change, and resource constraints (e.g., land, water). Moreover, youth should not only obtain ag technological proficiency but also cultivate essential soft skills that enable them to collaborate effectively within leadership teams while remaining lifelong learners. While a tech-savvy agricultural workforce is clearly needed, the pedagogy of many conventional educational systems is not adapted to train youth in these cutting-edge skills.In this project, we will deliver a transformative micro-credentialing pilot for training youth in ag technology built through a novel experiential learning cycle. The enhanced durable learning approach (following the U-Behavior model) integrates self-paced online learning supported by hands-on training featuring a custom IoT sensors kit and real-world field demonstrations - including observations of how data science and AI impact decision-making. Our program supports the AFRI Farm Bill Priority area of agricultural systems and technology, specifically addressing advanced data science technologies. We build on our successful piloted work by the investigators that teachyouth how IoT soil sensors and app-based irrigation schedulers can improve irrigation management.We will build a more comprehensive badging system that teaches on-farm IoT monitoring in urban, rural, and controlled environments. It also includes how sensors and data science can be used with other technologies (e.g., seed technology, soil health, food safety). High School and college-aged youth in Colorado and Nebraska will jointly learn to integrate fundamental concepts in soil, plant, and food science into a cyber support system that optimizes automation and decision-making using real-time data. Youth will be empowered to co-create and revise badging content to continuously improve the program. We expect over 600 students to earn badges and credentials over the three-year project and several thousand youth to gain awareness of technology's impact and ag careers through community experiment demonstration sites. Collaborating with various industries and professional societies, the program will secure formal recognition for the agricultural technology credentials earned by participants.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
Goal 1: Improve agriculture workplace readiness in high school and early college-aged youth through micro-credentialled training in emerging agriculture technology skills and topics.Objective 1-1: Design and build online credentialling modules that 1) follow U-Behavior's model for improved durable learning and 2) integrate hands-on learning with agricultural STEM data and technologies. Curriculum addresses how sensors, IoT, data science, and AI can improve Ag production by optimizing and automating irrigation in rural and urban systems as well as other ag technologies that use IoT sensors and data science.Objective 1-2: Facilitate active learning by expanding hands-on learning kits and applied desktop exercises.Objective 1-3: Implement real-world demonstrations of the technology across multiple indoor and outdoor growing sites that can be used in micro-credentialling courses and for public tours increasing youth awareness of agriculture technology.Objective 1-4: Nurture collaboration and leadership essential skills competencies through Youth Innovator program which incorporates hackathons and group activities.Goal 2: Build the organizational foundation to guarantee the longevity of the micro-credentialing system after project completion.?Objective 2-1: Implement the learning system across a network of high schools, community colleges, and university classrooms while empowering students and teachers to customize and improve the badging system over time.Objective 2-2: Create an evaluation mechanism to assess the teaching system's impact on advancing student focused results and propose content adjustments for improved outcomes.
Project Methods
1) EffortsOur approach to developing a micro-credentialling system in Ag technology is the integration of four fundamental high-impact learning strategies:Online Instructional Modules: Our system will feature an expert-curated series of interconnected subject-area badges, culminating in industry-recognized credentials. These online modules will utilize a structured but self-paced learning method known as UBehavior. U-Behavior coaches students to use the well-researched and powerful science of learning behaviors to increase their long-term retention. This will allow students (High School and College) to be prepared for the future with skills ready to solve next-generation Ag technology problems.Hands-on Experiential Learning Kits: Complementing the online content, hands-on kits, and desktop exercises will facilitate active experiential learning. These kits will immerse students in practical exercises, allowing them to apply concepts and problem-solve as they progress through the badging activities.Demonstration Sites with Real-world Applications: To bridge the gap between theory and practice, we will establish demonstration sites showcasing real-world applications of the technology. These sites will serve as immersive learning environments and also provide live data for participants to utilize in completing specific badges related to data science and AI.Youth Involvement: Hackathons and Group Activities: Nurturing collaboration and leadership competencies, our micro-credentialing system incorporates hackathons and group activities, held both in-person and virtually. These events empower students to apply their recently acquired technical proficiency in real-world scenarios while also involving them in the design and enhancement of curriculum content.2) EvaluationWe will employ a comprehensive evaluation plan that combines developmental evaluation (Patton, 2010) and strategic foresight (Carelton et al., 2013) to assess outcomes and guide adjustments for our innovative teaching approach. Our goal is to create an evaluation mechanism for assessing the impact on student-focused results and proposing content adjustments. By integrating these approaches, we aim to explore and adapt to emerging changes, co-creating solutions with participating youth. Our findings will evaluate the effectiveness of integrating hands-on learning in STEM and emerging data technologies, with a focus on the open innovation process and training approaches for safe technology use and agricultural career needs.Evaluands Evaluation Data Collection Methods1) Evaluand: Credentialling Modules with agricultural STEM data and technologiesEvaluationFormative: # of learners, modules, and hands-on learning experiencesSummative: Demonstrated increase in ability to use agricultural STEM data and technologiesData Collection MethodsInstructor feedback, Module/badge user surveys, and data from learning systems.2) Evaluand: Learning systems across networks of NGOs, high schools, community colleges, and university classroomsEvaluationFormative: # of high schools, community colleges, university classrooms, partnersSummative: The network effectively and efficiently implements learning systems youth can use to advance their knowledge, experiences, networks, and career interests; Participating partners work together to create dynamic, stakeholder-focused learning systems that are innovative, effective, and efficient.Data Collection MethodsFocus groups to assess network engagement and goals; Ripple Network map to demonstrate stakeholders, relationships, levels of trust, and resources3) Evaluand: Real-world demonstrations of technology across multiple indoor and outdoor growing sitesEvaluationFormative: # of demonstration sites, # of participants, # of team members, # and types of technologySummative: Do demonstrations 1) increase student understanding of available technologies, and 2) do students plan to use technologies as part of future educational and career experiences in agriculture.Data Collection MethodsPost-then-pre evaluation; Data collected from the sites and kits.4) Evaluand: Evaluation mechanism designed to assess the teaching system's impact on advancing student-focused results and content adjustmentsEvaluationFormative: # of evaluation touchpoints and participants; summary of approaches and strategiesSummative: Novel evaluation approaches and strategies that evolve each year and inform next steps as well as future directionsStrategic: Combine evaluations with Social, Environmental, Economic, Exponential, and Technological signs and signals to create a Futures Playbook Toolkit for easy implementations.Data Collection MethodsStructured team reflections and surveys use case scenarios, timeline, and STEEEP Analysis with Visual indicators5) Evaluand: Organizational foundation to guarantee the longevity of the micro-credentialing systemEvaluationFormative: # of leaders and organizations participating in projectsSummative: Participating organizations and leaders are committed to dedicating time, money, and energy to the micro-credentialing systemData Collection MethodsLeadership Focus Groups; Timeline and Janus Cone outlining past, present, and future commitments and plans.