Effective teachers recognize the importance of transitioning students into learning activities for the day and accounting for the natural drift of student attention while creating lesson plans. In this work, we analyze temporal patterns of gaming behaviors during work on an intelligent tutoring system with a broader goal of detecting temporal trends in students' motivation. Findings demonstrate that observing gaming the system behaviors in the near beginning or end of a working session correspond with predictions made by self-regulation theories of ego-depletion and task-switching. Furthermore, analyses provide initial evidence these gaming behaviors are indicative of partial cognitive engagement and session-level influences on student motivation. These findings provide evidence for how temporal fluctuations in students motivations might be inferred through self-regulated behaviors like gaming the system, and how such information could inform better more intelligent tutoring systems that are responsive to cognitive and motivational dynamics during student work. [For the full proceedings, see ED607784.]