Master's Student Online Learning Burnout Causes via Q-Methodology

Fifteen master's students participated in a Q-methodology study to identify causes of learning burnout in online environments. The research categorized burnout factors into four distinct groups, including self-management challenges and environmental distractions. The findings are intended to inform graduate course design and instructional practices.

Use Cases

• Analyze the four categorized groups of burnout causes (e.g., lack of self-management, external interference) to identify primary intervention targets for graduate educators.
• Examine the Q-ranking data from the 15 participants to understand consensus and divergence in perceived burnout factors.
• Use the identified Q-sample statements to benchmark or compare burnout causes across different student populations or learning modalities.
• Investigate the relationship between specific burnout categories (e.g., challenges adapting to online classroom environment) and proposed instructional design changes.

Strengths

• Study design employs Q-methodology, a structured approach for studying subjective viewpoints.
• Findings are categorized into four distinct, interpretable groups of burnout causes.
• Data is openly shared under a CC BY 4.0 license, facilitating reuse.

Limitations

• The dataset is a qualitative study document (DOCX), not a structured tabular dataset, limiting computational analysis.
• Sample size is small, with only 15 master's student participants.
• The 30.0 KB file size indicates a limited scope, likely containing summary results rather than raw response data.

Provenance

Source

figshare

Collection Method

Q-methodology study involving Q-sample construction, participant Q-ranking, and factor analysis.

Time Range

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Freshness

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Geography

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