Model Path Analysis Results for GAI-Assisted Learning in Chinese Universities

207 valid questionnaires from Chinese university students, analyzed via structural equation modeling. The dataset contains results from an empirical study investigating how role adaptation, self-efficacy, task-technology fit, and institutional support affect students' exploration and exploitation behaviors in Generative AI-assisted learning. It was authored by Yixuan Zeng and last updated on 2026-04-28.

Use Cases

• Analyzing the relationship between internal drivers like self-efficacy and GAI-related learning behaviors based on the structural equation model results.
• Investigating the impact of external enablers like institutional support on student exploration and exploitation behaviors mentioned in the description.
• Modeling the pathways from adaptation behaviors to learning outcomes based on the triadic framework of Exploration–Exploitation–Adaptation.
• Comparing the relative effects of exploration versus exploitation behaviors on learning effectiveness as indicated by the study findings.

Strengths

• Dataset is small at 5.5 KB, facilitating quick download and inspection.
• The underlying study is based on 207 valid questionnaires, providing a defined sample size.
• The model demonstrates good fit and significant path relationships among variables, as stated in the description.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The study's correlational evidence does not establish a causal mechanism, as noted by the author.

Provenance

Source

figshare

Collection Method

Survey data collected via questionnaires from Chinese university students.

Freshness

Last updated 2026-04-28 17:42:05; freshness should be verified.

Geography

China