Bridging language and data: Transforming agricultural curricula for data analytics through linguistic insights

RESEARCH ARTICLE Bridging language and data: Transforming agricultural curricula for data analytics through linguistic insights Zhihong Xu 1*, Jaehyun Ahn 2, Shuai Ma1, Anjorin Ezekiel Adyemi 3, Fahmida Husain Choudhury 1, Xiting Zhuang 4, Rafael Landaverde1, Gary Wingenbach1 1 Department of Agricultural Leadership, Education, and Communications, College Station, Texas A&M University, TX, USA, 2 University of Florida, Gainesville, FL, USA, 3 Fort Hays State University, Hays, KS, USA, 4 North Dakota State University, Fargo, ND, USA * Abstract OPEN ACCESS Citation: Xu Z, Ahn J, Ma S, Adyemi AE, Choudhury FH, Zhuang X, et al. (2026) Bridging language and data: Transforming agricultural curricula for data analytics through linguistic insights. PLoS One 21(5): e0348935. https://doi.org/10.1371/journal.pone.0348935 Editor: Olugbenga Ige, PNG National Research Institute, PAPUA NEW GUINEA Received: October 7, 2025 Accepted: April 21, 2026 Published: May 27, 2026 Copyright: © 2026 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: The data is available at Texas Data Repository https://doi. org/10.18738/T8/EB4U0R. Funding: This work was supported by the Data Science Course Development Program at Texas A&M University. The funders had no role in study design, data collection and analysis, 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 PLOS One | https://doi.org/10.1371/journal.pone.0348935 May 27, 2026 1 / 25 decision to publish, or preparation of the manuscript. expectations, FANH science programs can better prepare graduates for diverse roles in data-driven agricultural and environmental science sectors. Competing interests: The authors have declared that no competing interests exist. Introduction In the context of digital transformation and the rising demand for analytical skills, the development and implementation of a comprehensive data analytics curriculum have become both a necessary and competitive academic offering for higher education students in the disciplines of Food, Agriculture, Natural Resources, and Human (FANH) Sciences [1,2]. These disciplines, central to food security, economic development, and the sustainability of agrifood systems, require professionals who can collect, analyze, and interpret complex data to support informed decision-making and foster innovative solutions to contemporary productive and environmental challenges. As Bounaris et al. (2022) [1] note, most students in FANH science-related academic programs recognize the importance of preparing in data analytics to remain competitive in today’s labor market. As in many other disciplinary areas, teaching data analytics within the FANH sciences presents challenges due to the diversity of student profiles, including their prior experiences and, more critically, their learning needs [3,4]. Educational researchers emphasize that heterogeneity of students enrolled in the FANH sciences need a curriculum that is flexible, adaptive, and responsive to the cultural, academic, and professional contexts of learners [5]. In cultivating data analytics competencies, educators must account for varying levels of exposure to technology, proficiency in statistical techniques, and familiarity with digital environments, all of which can influence the effectiveness of teaching and learning [6,7]. According to Daum (2025) [8], achieving mastery in data analytics requires learners to acquire theoretical knowledge and build practical skills through hands-on experience, ultimately becoming competent and confident professionals. In the United States, there is growing multisectoral interest in preparing agricultural professionals who can meet the demands of a rapidly evolving and unpredictable production landscape. Many current educational investment initiatives in the FANH sciences aim to address challenges similar to those that inspired this study. This study is grounded in the premise that understanding differences among learner subgroups provides valuable insights for curriculum development, planning, and implementation in data analytics education [9], and through the analysis of linguistic patterns, tool preferences, proposed course topics, and desired skills, this article seeks to explore how perspectives and learning needs vary across different demographic and experiential profiles. Our study was guided by the following research questions: RQ 1: Are there distinct subgroups of users based on their demographic and experiential profiles that may inform curriculum needs in data analytics education in the FANH sciences? RQ2: If distinct subgroups exist, how do their expressed needs and perspectives differ in ways relevant to curriculum design? Specifically, 1) How do overall word usage patterns differ be (...truncated)