Longitudinal Study of Computational Thinking and AI Literacy in Chinese Undergraduates

216 undergraduate students from southern China were tracked over time to study the relationship between computational thinking and artificial intelligence literacy. The dataset, shared by Qiuyu Li on figshare, likely contains demographic information and longitudinal scores for these competencies. Structural equation modeling and paired-sample t-tests were used to confirm a significant positive effect and synchronous development of both skills.

Use Cases

• Analyze the longitudinal relationship between computational thinking and AI literacy based on the two-stage study design
• Model the development of core AI competencies over time based on the tracked evolution mentioned in the description
• Validate structural equation models for educational constructs based on the SEM-PLS methodology employed
• Assess demographic factors influencing computational skill acquisition based on the participant information

Strengths

• Longitudinal design tracking 216 participants over two stages
• Specific analytical methods (SEM-PLS, paired-sample t-tests) are documented
• Openly licensed under CC-BY-4.0 for reuse

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

Qiuyu Li via figshare

Collection Method

Likely collected via surveys or assessments from undergraduate students.

Freshness

Last updated 2026-05-27 17:37:47; freshness should be verified

Geography

Southern China