Learner Clustering Statistics for Data Analytics Curriculum Design in FANH Sciences

535 alumni survey responses from a land-grant university were analyzed to inform differentiated curriculum design for data analytics in Food, Agriculture, Natural Resources, and Human Sciences. Clustering analysis revealed two distinct learner groups: younger professionals emphasizing technical proficiency and older professionals prioritizing strategic competencies. The dataset, created by Zhihong Xu and last updated in May 2026, contains descriptive statistics from this mixed-methods study.

Use Cases

• Analyzing learner subgroup characteristics based on demographic and linguistic survey data mentioned in the description
• Comparing curriculum priorities between technical and strategic learner clusters identified in the study
• Informing the design of differentiated learning pathways based on expressed needs and tool preferences
• Validating cluster distinctions using text mining patterns from open-ended responses on curriculum dimensions

Strengths

• Survey data from 535 alumni provides a substantive sample for analysis
• Mixed-methods approach combining quantitative clustering and qualitative text mining
• Clear distinction of two learner clusters with differing demographic and competency profiles

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Data may reflect geographic or institutional bias inherent to a single land-grant university survey

Provenance

Source

Zhihong Xu via figshare

Collection Method

Survey of 535 alumni from a land-grant university, analyzed with clustering and text mining techniques

Freshness

Last updated 2026-05-27 17:36:57; freshness should be verified