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Uncovering algorithmic bias related to sensitive attributes is crucial. However, understanding the underlying causes of bias is even more important to ensure fairer outcomes. This study investigates bias associated with Attention Deficit Hyperactivity ...

For more than a decade, a handful of theoretical models have shaped a substantial amount of the research related to students’ emotional experiences during learning. This research has been productive, but articulating the underlying implicit assumptions ...

Student affect is widely recognized as a major influence on learning gains and engagement, which has led to the development of many automated affect detectors. However, in order to respond effectively to student affect, we must know how students ...

Game-based learning environments can foster student engagement in learning, offering an important venue for exploring epistemic emotions. Data from this study were collected as part of a larger effort to develop an in-game self-reporting tool for ...

Measuring algorithmic bias in machine learning has historically focused on statistical inequalities pertaining to specific groups. However, the most common metrics (i.e., those focused on individual- or group-conditioned error rates) are not currently ...

Recent years have seen a surge in research conducted on intelligent online learning platforms, with a particular expansion of research conducting A/B testing to decide which design to use, and research using secondary platform data in analyses. ...

Frustration is a natural part of learning in AIED systems but remains relatively poorly understood. In particular, it remains unclear how students’ perceptions about the learning activity drive their experience of frustration and their subsequent ...