Difference between revisions of "Engagement and Affect Detection"
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(Created page with "Ocumpaugh et al. (2014) https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf * Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system * Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52) * Detectors trained...") |
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Ocumpaugh et al. (2014) | Ocumpaugh et al. (2014) [https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12156 pdf] | ||
* Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system | * Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system | ||
* Study involved urban, rural, and suburban learners | |||
* Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52) | * Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52) | ||
* Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54) | * Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54) | ||
Chiu (2020) [https://files.eric.ed.gov/fulltext/EJ1267654.pdf pdf] | |||
* Model identifies affective states (boredom, concentration, confusion, frustration, off task and gaming) of middle school students’ online mathematics learning in predicting their choice to study STEM in higher education. | |||
* Model detects interaction with the ASSISTments system | |||
* Model performs better for male students (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR) | |||
Latest revision as of 23:38, 30 September 2023
Ocumpaugh et al. (2014) pdf
- Models detecting student affective states (boredom, confusion, engaged concentration, frustration) from the interaction with ASSISTment system
- Study involved urban, rural, and suburban learners
- Detectors generally performed the best for the same subpopulation that they were trained on (average kappa = 0.26, A′ = 0.67), and worse for other subpopulations (average kappa = 0.03 and A′ = 0.52)
- Detectors trained on combined population generally performed better for urban and suburban population (kappa = 0.18, 0.16; A′ = 0.62, 0.66) and not as well for rural population (kappa = 0.06; A′ = 0.54)
Chiu (2020) pdf
- Model identifies affective states (boredom, concentration, confusion, frustration, off task and gaming) of middle school students’ online mathematics learning in predicting their choice to study STEM in higher education.
- Model detects interaction with the ASSISTments system
- Model performs better for male students (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR)