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

15 master's students participated in a Q-methodology study exploring causes of learning burnout in online environments. The research identifies four factor groups, including lack of self-management and challenges in adapting to the online classroom. The findings are presented in a 12.2 KB DOCX file.

Use Cases

• Analyze the four identified factor groups (e.g., lack of self-management, external interference) to inform graduate course design.
• Use the Q-methodology results to develop targeted interventions addressing specific burnout causes like academic pressure or limited learning resources.
• Compare the study's qualitative findings on motivation and classroom environment challenges with quantitative data from other educational datasets.

Strengths

• Data is derived from a structured Q-methodology study with 15 participants.
• Findings are clearly categorized into four distinct groups of burnout causes.
• Dataset is openly available under a CC BY 4.0 license.

Limitations

• The sample size is small, with only 15 master's student participants.
• Data is qualitative and presented as study results, not raw survey responses or tabular data.
• The dataset is a 12.2 KB document, limiting its scope for direct computational analysis.

Provenance

Source

figshare, authored by Shunli Zuo.

Collection Method

Q-methodology study involving Q-ranking and factor analysis.

Time Range

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Freshness

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Geography

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