Longitudinal Study of Computational Thinking and AI Literacy in Chinese Undergraduates

A dataset of 216 undergraduate students from southern China, tracking the relationship between computational thinking and artificial intelligence literacy over time. The data was collected by researcher Qiuyu Li and analyzed using structural equation modeling and paired-sample t-tests. It was last updated on 2026-05-27 and is shared under a CC-BY-4.0 license.

Use Cases

• Analyzing the longitudinal effect of computational thinking on AI literacy based on the two-stage study design
• Modeling competency development pathways using structural equation modeling techniques mentioned in the description
• Conducting paired-sample statistical tests to measure skill improvement over time
• Studying the stability of cross-sectional relationships between educational constructs

Strengths

• Dataset includes longitudinal data from a defined sample of 216 undergraduate students
• Analysis employs established statistical methods (SEM-PLS and paired-sample t-tests)
• Provides a novel longitudinal perspective on the synchronous development of two competencies

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment
• Data scale is very small at 5.5 KB, indicating limited scope
• Geographic coverage is limited to southern China, which may affect generalizability

Provenance

Source

figshare, authored by Qiuyu Li

Collection Method

Likely collected via surveys or assessments from undergraduate students, analyzed with SEM-PLS.

Freshness

Last updated 2026-05-27 17:37:52; freshness should be verified.

Geography

Southern China