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

Area of Science:

• Food, Agriculture, Natural Resources, and Human (FANH) Sciences
• Data Analytics Education
• Curriculum Design

Background:

• Growing demand for data analytics skills in FANH Sciences.
• Need for inclusive and responsive data analytics programs.
• Existing curricula may not cater to diverse learner needs.

Purpose of the Study:

• Investigate how linguistic and demographic differences inform differentiated curriculum design.
• Identify distinct learner subgroups based on needs, tool preferences, and priorities.
• Guide the development of responsive data analytics programs in FANH Sciences.

Main Methods:

• Mixed-methods approach utilizing surveys from 535 land-grant university alumni.
• Quantitative data collection combined with qualitative text mining of open-ended responses.
• Clustering analysis to identify distinct learner subgroups.

Main Results:

• Two distinct learner groups identified: younger professionals (technical focus) and older professionals (strategic focus).
• Linguistic analysis revealed divergent word usage patterns validating cluster distinctions.
• Younger learners preferred hands-on, tool-centric learning; older learners favored integrative applications and strategic thinking.

Conclusions:

• A one-size-fits-all curriculum is insufficient for data analytics education in FANH Sciences.
• Differentiated learning pathways (technical labs, strategic modules) are essential.
• Integrating communication and interpretive skills alongside technical training is crucial for diverse roles.