Composite Reliability (CR): Longitudinal Data on Computational Thinking and AI Literacy

A two-stage longitudinal study tracked 216 undergraduate students from southern China. The dataset, created by Qiuyu Li and last updated in 2026, contains results from structural equation modeling and paired-sample t-tests. It was used to analyze the relationship between computational thinking and artificial intelligence literacy over time.

Use Cases

• Modeling longitudinal effects of computational thinking on AI literacy based on the study's findings.
• Conducting paired-sample t-tests to measure competency improvement over time.
• Validating structural equation models for educational constructs.
• Analyzing the stability of cross-sectional relationships in educational data.

Strengths

• Dataset is based on a sample of 216 undergraduate students.
• Analysis employed structural equation modeling (SEM-PLS) and paired-sample t-tests.
• Provides a longitudinal perspective with two-stage data collection.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size indicates a very limited scope of data.

Provenance

Source

Qiuyu Li via figshare

Collection Method

Data collected from a sample of 216 undergraduate students and analyzed with SEM-PLS and t-tests.

Freshness

Last updated 2026-05-27 17:37:48; freshness should be verified.

Geography

Southern China