E-learning Research

TL;DR: This work lays the learning theoretic foundations of domain generalization, building on the earlier conference paper where the problem of DG was introduced, and presents two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis.

TL;DR: The proposed framework incorporates various types of analytics, with the teacher playing a key role in bringing context to the analysis and making decisions on the feedback provided to students as well as the scaffolding and adaptation of the learning design.

TL;DR: This article tested the hypothesis that cultural differences in group orientation predict an interaction between the student variable (ethnicity) and a learning context variable (reward structure) on math performance after group learning.

TL;DR: This work explores feature selection to improve the performance in the vascular anomaly detection domain and attempts to determine features that improve classification performance and to define guidelines for feature selection.