Advancing Agricultural Forensics through Pedagogical Innovation: Development of a Training Module on Machine Learning-assisted Ubi “Kinampay” Geographic Origin Differentiation

Abstract

The growing emphasis on food traceability and authenticity has heightened the demand for advanced analytical techniques capable of verifying both the geographic origin and varietal identity of premium crops. In the Philippine context, ubi ‘Kinampay’ (Dioscorea alata L.) is regarded as a culturally significant and economically valuable commodity, yet remains susceptible to misidentification and economically motivated fraud. Although methods such as X-ray fluorescence (XRF) and stable isotope analysis present viable solutions for forensic verification, their adoption is hindered by the scarcity of skilled practitioners and the absence of formalized training programs. This study responds to that need through the design and evaluation of a competency-based training module tailored for science professionals working in nuclear forensic applications related to agriculture. Drawing on the principles of Mezirow’s Transformative Learning Theory and Kolb’s Experiential Learning Cycle, the training integrates adult learning pedagogies with domain-specific content. A mixed-method developmental research design was employed, combining expert validation via Likert-scale assessment with qualitative insights obtained through thematic analysis of interviews. Results showed consistent strong agreement across key dimensions, including content coherence, instructional design, visual aids, and science identity reinforcement. Qualitative data further emphasized the module’s clarity, scientific accuracy, and relevance to professional practice, while also noting the need for refinements in cognitive load and interface usability. Overall, the module demonstrates both pedagogical soundness and technical validity, positioning it as a scalable solution for institutional capacity-building in agricultural traceability. Strategic recommendations focus on module enhancement and alignment with policy frameworks such as Geographical Indication (GI) protection.