Learner Subgroup Analysis for Data Analytics Curriculum in FANH Sciences

A land-grant university study surveyed 535 alumni to inform differentiated curriculum design for data analytics in Food, Agriculture, Natural Resources, and Human Sciences. The research used clustering and text mining to identify two distinct learner groups with different needs and tool preferences. The dataset, created by Zhihong Xu and last updated in May 2026, contains analysis results from this mixed-methods investigation.

Use Cases

• Analyzing learner subgroup differences based on demographic and linguistic patterns mentioned in the description
• Informing differentiated curriculum pathways based on identified technical and strategic competency priorities
• Validating cluster distinctions through text mining of open-ended responses on curriculum dimensions

Strengths

• Based on a survey of 535 alumni, providing a substantive sample size
• Employs a mixed-methods approach combining quantitative clustering and qualitative text mining
• Clear license (CC-BY-4.0) facilitating open reuse

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small at 5.5 KB, indicating limited scope

Provenance

Source

figshare, author Zhihong Xu

Collection Method

Mixed-methods research involving a survey of university alumni, followed by clustering analysis and text mining.

Freshness

Last updated 2026-05-27 17:36:56; freshness should be verified