Student SRL, Engagement, and Performance in a Flipped Database Course

A dataset from a study of 96 third-year Computer Science and Engineering students in a flipped Database Management Systems course. It was created by Monica Maiti and last updated on April 20, 2026. The data includes academic performance scores, engagement analytics from Microsoft Teams, social-emotional intelligence survey responses, and affective indicators.

Use Cases

• Modeling academic performance patterns based on self-regulated learning (SRL) and engagement indicators.
• Analyzing correlations between cognitive engagement, emotional competencies, and assessment outcomes.
• Clustering students based on alignment of self-regulation, engagement, and social-emotional intelligence.
• Investigating the mediating role of engagement in the relationship between SRL behaviors and final grades.

Strengths

• Includes data from 96 students across a semester-long course.
• Integrates multiple data sources: assessment scores (CAT 1, CAT 2, FAT), platform analytics, surveys, and affective indicators.
• Predictive modeling reported an R-squared value of 0.83 for academic performance.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• Data is contained in a single 808.7 KB DOCX file, which may require extraction and structuring.
• The dataset scope is limited to one course at a single institution, which may affect generalizability.

Provenance

Source

figshare, authored by Monica Maiti.

Collection Method

Collected via a mixed-methods study in a flipped classroom setting, using academic records, platform analytics, surveys, and a Reflect app.

Time Range

Covers one academic semester (specific dates unknown).

Freshness

Last updated 2026-04-20 05:22:16; freshness should be verified.

Geography

Institutional context unspecified; likely a single university.