Self-regulated learning (SRL) is a form of learning guided by the student's own meta-
cognition, motivation, and strategic action, often in the absence of an educator. The use of …

Esports play can cultivate real world skills. However, the path to mastery is not easy, and
difficulty progressing can result in discontinuation. In the absence of a human coach …

Computational support for learning in the domain of esports has seen a great deal of
attention in recent years as an effective means of helping players learn and reap the benefits …

The rapid increase in the availability of player data and the advancement of player modeling
technologies have resulted in an abundance of data-driven systems for the domain of …

Previous work demonstrated that esports players often leverage insights from other players
and communities to learn and improve. However, little research examined social learning in …

Complex games, those with multiple correct strategies and unpredictable outcomes, are
seeing increased popularity and integration into high-impact domains such as health …

While existing work has discussed ethics and fairness in relation to data generally, and a
small number of papers have raised the same issues within games specifically, work on …

In this article, we provide an application that produces consistent in-game estimates of win
probabilities in Dota 2. Previous work shows that common methods of identifying the effect of …

We explore several popular (and unpopular) systems for matchmaking and ranking in free-
for-all (FFA) environments. The commonplace existing methods involve the reinterpretation …

Self-regulated learning (SRL) is a form of learning guided by the students own meta-
cognition, motivation, and strategic action, often in the absence of an educator. The use of …