IMPACT OF TEACHING MULTIVARIATE MODELING WITH DIGITAL TOOLS ON THE DEVELOPMENT LEVEL OF EXPERIMENTAL DATA ANALYSIS AND INTERPRETATION SKILLS (#2026)

Authors

Hernandez-Brenes, Carmen

Rodriguez-Sanchez, Dariana Graciela

Villarreal-Lara, Raúl

Clorio-Carrillo, Jorge Abraham

Gonzalez Viejo, Claudia

Abstract

Higher education of food engineering professionals requires the skills of scientific data analysis and interpretation to aid in decision-making and problem-solving tasks of the professional exercise. This study focused on teaching multivariate modeling using digital tools to increase the development level of a program learning outcome related to the skills to analyze and interpret practical experimentation results. Data was collected from two semesters of higher education courses on sensory evaluation of foods, including a control group (Control Class, n=15) and a teaching innovation group (Experimental Class, n=20). A written report from a practical project (scientific poster format) served as the assessment instrument. The impact of the teaching innovation was assessed using a rubric to establish the learning outcome development level and the final course grades as response variables. Results indicated that in the control class, almost all the students (12/15) developed over 87.5% of the desired data analysis and interpretation skills (solid and outstanding levels); however, 3/15 students achieved only 63.5-75.0% of the desirable skills (incipient and basic levels). In the experimental group, the introduction of multivariate modeling with digital tools significantly increased (p<0.05) the overall fulfillment of the desired outcome to 96.9%, placing all students in solid and outstanding development levels. Moreover, the teaching innovation also increased the level of the domain of the students beyond the data “analysis” domain of Bloom's Taxonomy. It allowed them to go up into “evaluation” and “creation”.