Bridging language and data: Transforming agricultural curricula for data analytics through linguistic insights - Zhihong Xu; Jaehyun Ahn; Shuai Ma; Anjorin Ezekiel Adyemi; Fahmida Husain Choudhury; Xiting Zhuang; Rafael Landaverde; Gary Wingenbach (PLoS ONE, 2026)
In response to the growing demand for data analytics competencies in Food, Agriculture, Natural Resources, and Human (FANH) Sciences, this study investigated how linguistic and demographic differences among learners can inform differentiated curriculum design. The objective was to identify distinct learner subgroups and explore how their expressed needs, tool preferences, and curricular priorities vary, thereby guiding the development of inclusive and responsive data analytics programs. Using a mixed-methods approach, the research team surveyed 535 alumni from a land-grant university, collecting both quantitative and qualitative data. Clustering analysis revealed two distinct groups: younger professionals emphasizing technical proficiency (e.g., coding, visualization, tool fluency), and older professionals prioritizing strategic competencies (e.g., leadership, communication, conceptual reasoning). Text mining of open-ended responses further highlighted divergent word usage patterns across curriculum dimensions, such as background tools, core topics, and supplemental skills, which validated the cluster distinctions. Key findings show that Group 1 (i.e., younger respondents with less work experience) favored hands-on, tool-centric learning, while Group 0 (i.e., older respondents with less education but with more experience) emphasized integrative applications and strategic thinking. These insights suggest that a one-size-fits-all curriculum is insufficient in curriculum design and development. Instead, differentiated learning pathways such as technical labs for early-career learners and strategic modules for experienced professionals are essential to accommodate learners’ needs. Communication skills emerged as a critical bridge across groups, underscoring the need to embed interpretive and collaborative competencies alongside technical training. This study demonstrates the value of combining linguistic analysis with demographic clustering to inform curriculum development. By aligning educational offerings with learner profiles and industry expectations, FANH science programs can better prepare graduates for diverse roles in data-driven agricultural and environmental science sectors.