Design and evaluate a digital module for enhancing farmers' agro-education skills

Thesis Abstract

The persistent gap in agricultural productivity among smallholder farmers, largely attributable to limited access to effective agro-education resources, underscores the urgent need for innovative approaches to agricultural training and knowledge dissemination. This study addresses the critical challenge of enhancing farmers' agro-educational skills through the development and evaluation of a bespoke digital learning module tailored to their informational needs and technological capabilities. The primary aim was to design, implement, and critically assess a digital educational intervention aimed at improving farmers' understanding of sustainable farming practices, pest management, crop diversification, and climate-smart agriculture. The specific objectives included identifying farmers’ educational needs, developing a culturally relevant digital module aligned with adult learning principles, and evaluating the module’s effectiveness in increasing knowledge and behavioral change. The research adopted a mixed-methods approach anchored within a pragmatic paradigm. The qualitative component involved thematic analysis of focus group discussions and interviews with 50 smallholder farmers and 10 agricultural extension officers to explore current knowledge gaps, preferred learning modalities, and barriers to technology adoption. The quantitative component employed a quasi-experimental pretest-posttest control group design with a total sample of 200 farmers from two agricultural zones, randomly assigned to experimental (n=100) and control groups (n=100). The digital module was developed based on the principles of the Cognitive Load Theory and Adult Learning Theory, integrating multimedia content, interactive quizzes, and contextual case studies, delivered via a locally accessible mobile application. Data collection instruments included structured questionnaires for assessing agro-knowledge, skill acquisition tests, and user satisfaction surveys, all validated through expert review and pilot testing, achieving a reliability coefficient of 0.85. Knowledge gains were measured through pre- and post-intervention assessments, while behavioral intentions were gauged through follow-up interviews conducted three months post-intervention. Quantitative data were analyzed using descriptive statistics, paired t-tests, ANCOVA to control for baseline differences, and multiple regression analysis to determine predictors of knowledge improvement. Thematic analysis of qualitative data was conducted using NVivo software to extract recurrent themes relating to user engagement and perceived impact. Expected findings suggest that the digital module will significantly enhance farmers’ agro-educational knowledge, with the experimental group demonstrating a mean knowledge increase of 25% over controls (p < 0.01). Qualitative insights are anticipated to reveal enhanced confidence in implementing new practices, with key barriers including limited digital literacy and infrastructural constraints. The study also expects to affirm that interactive, culturally relevant content delivered through accessible mobile technologies effectively promotes adult learning and behavioral innovation. This research contributes to existing knowledge by providing empirical evidence on the feasibility and effectiveness of digital learning solutions tailored to smallholder farmers within developing country contexts, grounded in constructivist and experiential learning theories. It offers a scalable model for integrating digital education into agricultural extension services, emphasizing low-cost, sustainability, and user-centered design principles. In conclusion, the study advocates for policymakers and extension agencies to incorporate digital modules into existing farmer education frameworks, emphasizing capacity building in digital literacy and infrastructural improvements. Recommendations include further research on long-term behavioral impacts, customization for diverse farming communities, and integration with broader agricultural development strategies. The findings are expected to inform best practices and guide the strategic deployment of digital innovations to foster sustainable, knowledge-driven agricultural productivity among smallholder farmers.