<p>Formative indicator loadings.</p>

216 undergraduate students from southern China participated in a two-stage longitudinal study on computational thinking and artificial intelligence literacy. The dataset includes indicator loadings from structural equation modeling analysis, showing a significant positive effect of computational thinking on AI literacy. The data was collected by Qiuyu Li and last updated in May 2026.

Use Cases

• Validate structural equation models based on formative indicator loadings mentioned in the description
• Analyze the longitudinal relationship between computational thinking and AI literacy over two time points
• Benchmark the synchronous development of computational thinking and AI literacy competencies
• Replicate paired-sample t-tests for competency improvement over time

Strengths

• Dataset is based on a sample of 216 undergraduate students, providing a defined population size
• Employs a two-stage longitudinal design, allowing for analysis of change over time
• Uses structural equation modeling (SEM-PLS), a rigorous analytical method for the stated research question

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 bias inherent to the sample from southern China

Provenance

Source

figshare

Collection Method

Data collected via a longitudinal study of undergraduate students, analyzed using SEM-PLS and paired-sample t-tests.

Freshness

Last updated 2026-05-27 17:37:51; freshness should be verified

Geography

Southern China