Data-informed curriculum sequences for a curriculum-integrated game

In this paper, we perform a predictive analysis of a curriculum-integrated math game, ST Math, to suggest a partial ordering for the game's curriculum sequence. We analyzed the sequence of ST Math objectives played by elementary school students in 5 U.S. districts and grouped each objective into difficult and easy categories according to how many retries were needed for students to master an objective. We observed that retries on some objectives were high in one district and low in another district where the objectives are played in a different order. Motivated by this observation, we investigated what makes an effective curriculum sequence. To infer a new partially-ordered sequence, we performed an expanded replication study of a novel predictive analysis by a prior study to find predictive relationships between 15 objectives played in different sequences by 3,328 students from 5 districts. Based on the predictive abilities of objectives in these districts, we found 17 suggested objective orderings. After deriving these orderings, we confirmed the validity of the order by evaluating the impact of the suggested sequence on changes in rates of retries and corresponding performance. We observed that when the objectives were played in the suggested sequence, we record a drastic reduction in retries, implying that these objectives are easier for students. This indicates that objectives that come earlier can provide prerequisite knowledge for later objectives. We believe that data-informed sequences, such as the ones we suggest, may improve efficiency of instruction and increase content learning and performance.