Smartphone-based Farm Simulation for Applied Agricultural Education and Assessment

Thesis Abstract

Smartphone-based farm simulation (S-BFS) provides an immersive, cost-effective platform for applied agricultural education and assessment, addressing the persistent gap between theoretical instruction and practical farming competencies in higher education. The study investigates how integrating a mobile farm-simulation environment influences students’ cognitive understanding, procedural skills, and attitudinal readiness for real-world agricultural tasks, particularly in crop management, irrigation scheduling, pest/disease decision-making, and resource optimization. The aim is to evaluate the educational impact of S-BFS on learning outcomes, skill acquisition, and assessment validity, and to identify design features that maximize engagement and transfer of learning to field practice. Specific objectives are (1) to determine the effect of S-BFS on academic achievement in core agronomy and farm management modules, (2) to examine changes in procedural competence measured through simulated task performance, (3) to assess shifts in self-efficacy and motivation toward technology-enhanced learning, (4) to evaluate the reliability and validity of assessment rubrics embedded within the simulation, (5) to explore student and educator perspectives on usability, engagement, and perceived transfer to field scenarios, and (6) to propose a scalable implementation framework for tertiary institutions. The methodology adopts a mixed-methods design within a quasi-experimental framework. The population comprises 420 undergraduate and postgraduate students enrolled in agricultural science and extension programs across three universities in a temperate agrarian region. A total sample of 240 students will be selected using stratified random sampling to ensure representation across year of study, gender, and prior ICT exposure. The experimental group (n=120) will complete modules integrated with the S-BFS over a 12-week semester, while the control group (n=120) will undertake conventional laboratory and field-based instruction. Data collection instruments include validated achievement tests aligned with Bloom’s taxonomy (pre- and post-tests, ? = 0.85), a performance rubric capturing procedural competencies during simulated tasks, a Computer Self-Efficacy Scale (modified for agricultural ICT contexts, ? = 0.87), and a Motivation for Technology Self-Report Instrument. Data collection also incorporates semi-structured interviews with a purposive subsample of 24 students and 8 educators, as well as think-aloud protocols during simulation sessions for a subset of 12 participants. Validity and reliability procedures include content validation by subject-matter experts, pilot testing (n=30), inter-rater reliability analysis (Cohen’s kappa > 0.70), and confirmatory factor analysis for the construct measures. Analytical approaches consist of ANCOVA to compare post-test achievement and skill scores between groups while controlling for baseline performance, multivariate analysis of covariance (MANCOVA) for multiple dependent variables (achievement, procedural competence, and motivation), and hierarchical linear modeling to account for clustering at the classroom level. Item response theory (IRT) will examine the discriminative validity of the assessment rubrics embedded in the simulation. Thematic analysis following Braun and Clarke will be employed to interpret qualitative interview and think-aloud data, with credibility enhanced through member checking and audit trails. A mixed-methods integration will use a convergent design to triangulate quantitative outcomes with qualitative insights, enabling a holistic interpretation of learning gains, engagement, and transferability. Key expected findings include statistically significant improvements in post-test achievement (p < .05) and higher procedural competence scores in the S-BFS group, increased self-efficacy regarding ICT-enabled agricultural practices, and positive shifts in motivation toward ongoing technology-assisted learning. It is anticipated that specific design features—gamified feedback, realistic farm state representations, adaptive difficulty, and scenario-based decision-making—will correlate with greater transfer to field tasks, higher session engagement, and more robust assessment validity. The study also expects nuanced evidence on potential challenges, such as cognitive overload in early adoption and accessibility constraints, with recommended mitigations like progressive onboarding and offline-capable modules. The contribution to knowledge includes empirical evidence on the efficacy of smartphone-based simulations for applied agricultural education, validation of integrated assessment rubrics for ICT-driven learning environments, and a scalable implementation framework informing curriculum design, faculty development, and resource allocation. The research advances theoretical discussions on constructivist and embodied learning in digital agricultural education, and informs policy decisions regarding the integration of mobile simulation tools in higher education curricula to enhance practical competence and readiness for modern farming demands. The study concludes that S-BFS constitutes a viable, scalable, and impactful modality for enhancing applied agricultural education, with recommendations emphasizing iterative refinement of simulation fidelity, alignment with national competency standards, and strategies for inclusive access across diverse student populations.