<p>Paired samples T-Test.</p>

A sample of 216 undergraduate students from southern China participated in a two-stage longitudinal study on computational thinking and artificial intelligence literacy. The dataset, created by Qiuyu Li and last updated in May 2026, includes results from structural equation modeling and paired-sample t-tests. It tracks the evolution of these two competencies over time.

Use Cases

• Conducting longitudinal analysis of competency development based on the two-stage data collection.
• Performing paired-sample t-tests to compare pre- and post-intervention scores for computational thinking and AI literacy.
• Applying structural equation modeling to explore the relationship between computational thinking and AI literacy.
• Benchmarking educational outcomes for undergraduate students in STEM or related fields.

Strengths

• Dataset includes 216 undergraduate student records, providing a defined sample size.
• Employs a longitudinal design with two-stage data collection, allowing for analysis of change over time.
• Uses established statistical methods (SEM-PLS and paired-sample t-tests) as described.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for specific variables is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to the sample from southern China.

Provenance

Source

figshare

Collection Method

Likely collected via educational assessments from undergraduate students.

Freshness

Last updated 2026-05-27 17:37:53; freshness should be verified.

Geography

Southern China