Student Perceptions of Digital Training Systems in Physical Education

Survey data from 643 Chinese university students majoring in physical education or sport-related disciplines collected across three time points in 2025-2026. The dataset supports analysis of a structural equation model linking perceived system quality to technology acceptance, self-regulation, adherence, and engagement. Author KEYI LIU published the data on figshare in April 2026.

Use Cases

• Test the hypothesized mediation model linking Perceived Digital Training Support System Quality (PDTSQ) to training adherence via perceived ease of use and perceived usefulness.
• Analyze the relationship between behavioral intention, system use, and subsequent training engagement scores across three measurement waves.
• Examine the role of training self-regulation as a mediator between technology acceptance variables (attitude toward use) and system use.
• Validate survey constructs like PDTSQ, perceived ease of use, and perceived usefulness using confirmatory factor analysis on the 643 student responses.
• Investigate temporal patterns in training adherence and engagement from Wave 1 (September 2025) to Wave 3 (March 2026).

Strengths

• Data from 643 participants provides a substantial sample for structural equation modeling.
• Three-wave longitudinal design allows for analysis of temporal mediation effects.
• Explicit model testing with bias-corrected bootstrap analyses supports rigorous statistical validation.

Limitations

• Data is from a single country (China) and specific academic majors, limiting generalizability.
• The dataset size is 145.7 KB, indicating it contains processed model results and scores, not raw survey responses.
• All participants are university students, which may not represent other user populations.

Provenance

Source

KEYI LIU via figshare.

Collection Method

Survey data collected from university students across three time points, analyzed with exploratory factor analysis, confirmatory factor analysis, and structural equation modeling.

Time Range

Survey waves conducted on September 2, 2025 (T1), December 4, 2025 (T2), and March 5, 2026 (T3).

Freshness

Data covers collection up to March 2026 and was last updated in April 2026.

Geography

Data collected from Chinese university students.