Longitudinal Study of Computational Thinking and AI Literacy in 216 Chinese Undergraduates

A longitudinal dataset from a study of 216 undergraduate students in southern China, tracking the relationship between computational thinking and artificial intelligence literacy. The data was collected in two stages and analyzed using structural equation modeling (SEM-PLS) and paired-sample t-tests. The study found a significant positive effect of computational thinking on AI literacy and that both competencies improved over time.

Use Cases

• Analyzing the longitudinal relationship between computational thinking and AI literacy based on the two-stage data collection.
• Validating structural equation models (SEM-PLS) for educational competency assessment using the provided data.
• Conducting paired-sample t-tests to measure changes in computational thinking and AI literacy scores over time.
• Studying the stability of cross-sectional relationships between educational constructs in a longitudinal context.

Strengths

• Dataset is based on a sample of 216 undergraduate students, providing a specific scale for analysis.
• Employs a two-stage longitudinal design, allowing for the tracking of competency evolution over time.
• Analysis includes advanced statistical methods like structural equation modeling (SEM-PLS) and paired-sample t-tests.

Limitations

• Row count and column-level documentation are unknown, which limits suitability assessment and requires field semantics to be inferred after download.
• Data is limited to 216 students from southern China, which may reflect geographic and demographic bias.
• The dataset is very small at 5.5 KB, indicating limited scope and potentially few variables.

Provenance

Source

Author: Qiuyu Li. Platform: figshare.

Collection Method

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

Freshness

Last updated 2026-05-27 17:37:50; freshness should be verified.

Geography

Southern China