Smart Farm Simulations for Enhancing Agricultural Science Education Outcomes

Thesis Abstract

This study addresses gaps in agricultural science education where traditional instruction inadequately translates theoretical knowledge into practical competencies, particularly in limited-resource regions where access to real-field farming experiences is constrained. The problem is exacerbated by evolving farming technologies and limited student familiarity with decision-support tools, potentially hindering gains in scientific literacy, problem-solving, and readiness for modern agribusiness. The aim is to evaluate the efficacy of smart farm simulations as a technology-driven solution to enhance conceptual understanding, procedural skills, and adaptive decision-making among agriculture students. Specific objectives are (1) to determine the impact of smart farm simulations on learning outcomes in plant, soil, and water management; (2) to examine changes in students’ procedural skills and confidence in applying agronomic concepts; (3) to investigate how simulation-based learning influences critical thinking and problem-solving strategies; (4) to explore student engagement, motivation, and perceived usability of the simulation environment; and (5) to identify contextual factors that mediate or moderate learning gains, including prior knowledge, gender, and access to supplementary digital resources. The study employs a quasi-experimental design with mixed methods. The population comprises final-year undergraduate and taught master’s students enrolled in agricultural science programs at three universities in a mid-sized regional economy. A total of 240 students will be recruited, with 120 assigned to the intervention group using Smart Farm Simulations (SFS) and 120 to a control group receiving conventional laboratory and field-based instruction. The intervention spans a 12-week semester, comprising weekly 90-minute sessions integrated into existing curricula. Data collection instruments include validated multiple-choice assessments for domain knowledge (pretest, posttest, and a delayed posttest after eight weeks), performance-based tasks evaluated with a rubric aligned to Bloom’s taxonomy for procedural skills, and situational judgment tests to measure decision-making under simulated agronomic scenarios. Qualitative data will be gathered through semi-structured focus groups with 24 purposively sampled students (8 per university) and teacher interviews to capture perceptions of usability, engagement, and instructional fidelity. Validity and reliability will be established through expert review of items (content validity index >0.80), pilot testing (Cronbach’s alpha >0.70 for scales), and inter-rater reliability for performance assessments (ICC >0.75). Analytical techniques include ANCOVA to compare posttest outcomes between groups while controlling for pretest scores, multivariate regression to examine predictors of learning gains, and hierarchical linear modeling to assess classroom-level effects. Thematic analysis will be applied to qualitative data to identify patterns related to engagement, cognitive load, and perceived authenticity of the simulations. A conceptual framework grounded in constructivist learning theory and situated learning (Lave and Wenger) will guide interpretation, with the Technology Acceptance Model (TAM) informing usability insights. The study anticipates that students engaging with SFS will demonstrate statistically significant improvements in domain knowledge (medium effect size, f = 0.25), higher procedural competence (p < 0.05), and enhanced problem-solving behaviors compared with those in traditional instruction. Additional expected findings include heightened intrinsic motivation, greater perceived usefulness, and reduced cognitive load due to immersive visualizations and real-time feedback. The study contributes to knowledge by providing rigorous, empirically grounded evidence on the effectiveness of immersive ICT-enabled simulations in agricultural science education, articulating how simulated farm environments can bridge theory and practice, and outlining implementation considerations for scalability and sustainability in higher education. It will also extend understanding of how contextual factors such as prior knowledge and resource access influence learning with technology-enhanced instruction in diverse university settings. The main conclusion is that smart farm simulations offer a viable, scalable strategy to augment conventional teaching, improving both theoretical understanding and practical proficiency in agronomy-related domains. Recommendations include integrating SFS into core curricula with structured alignment to learning outcomes, investing in faculty development for simulation-based pedagogy, ensuring equitable access to digital resources, and conducting longitudinal studies to assess long-term impacts on academic performance and workforce readiness.