A study conducted with students using the DARA augmented reality tool. Students' emotions were collected via the Emolive facial recognition tool and the AEQ questionnaire. The dataset shows the emotions collected by these two instruments.
Use Cases
- Training emotion detection models based on facial recognition data from the Emolive tool.
- Comparing self-reported emotional states from questionnaire data with AI-inferred emotional states.
- Analyzing the correlation between augmented reality learning experiences and student emotional responses.
Strengths
- Data collected using two distinct instruments (Emolive and AEQ questionnaire), allowing for potential cross-validation.
- Focuses on a specific educational technology context (the DARA augmented reality tool).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for large-scale modeling.
- The dataset description is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- Paredes, Maximiliano via e-cienciaDatos Harvested Dataverse
- Collection Method
- Data collected from a study where students used the DARA augmented reality tool.
- Time Range
- null
- Freshness
- Last updated 2025-10-14 21:37:22; freshness should be verified.
- Geography
- null