Governing games: Adaptive game selection in the Math Garden

Abstract

Utilizing online digital educational content has become the norm when teaching young students. A variety of adaptive educational practice sys- tems is readily available and allows students to practice various domains, on a preferred difficulty and pace. However, due to the intensification of the teaching profession and the possibilities of practicing from home, students might be left unsupervised, and as a result do not practice domains that are most important. This study proposes a solution to govern these students, i.e., provide comput- erized data driven supervision that guides students in practicing domains most important with no intervention of a teacher. Through an experiment involving 13 578 participants, a new governing method was tested and found to have positive effects on both engagement and learning, with almost no changes to the visual interface needed. Governing seems a promising technique in general, and was effectively tested and introduced in Math Garden.