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Download full textJensen, Emily; Hutt, Stephen; D'Mello, Sidney K. – Grantee Submission, 2019
Recent work in predictive modeling has called for increased scrutiny of how models generalize between different populations within the training data. Using interaction data from 69,174 students who used an online mathematics platform over an entire school year, we trained a sensor-free affect detection model and studied its generalizability to…
Descriptors: Generalization, Longitudinal Studies, Psychological Patterns, Identification
Jensen, Emily; Hutt, Stephen; D'Mello, Sidney K. – International Educational Data Mining Society, 2019
Recent work in predictive modeling has called for increased scrutiny of how models generalize between different populations within the training data. Using interaction data from 69,174 students who used an online mathematics platform over an entire school year, we trained a sensor-free affect detection model and studied its generalizability to…
Descriptors: Generalization, Longitudinal Studies, Psychological Patterns, Identification
Direct linkHuang, Francis L. – Practical Assessment, Research & Evaluation, 2014
Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to…
Descriptors: Hierarchical Linear Modeling, Regression (Statistics), Educational Research, Sampling
