Student interactions with game-based learning environments produce a wide range of in-game problem-solving sequences. These sequences can be viewed as trajectories through a game's problem-solving space. In this paper, we present a general framework for analyzing students' problem-solving behavior in game-based learning environments by filtering their gameplay action sequences into time series representing trajectories through the game's problem-solving space. This framework was investigated with data from a laboratory study conducted with 68 college students tasked with solving the problem scenario in a game-based learning environment for microbiology education, CRYSTAL ISLAND. Using this representation of student problem solving, we derive the slope of the problem-solving trajectories and lock-step Euclidean distance to an expert problem-solving trajectory. Analyses indicate that the trajectory slope and temporal distance to an expert path are both correlated with students' normalized learning gains, as well as a complementary measure of in-game problem-solving performance. The results suggest that the filtered time series framework for analyzing student problem-solving behavior shows significant promise for assessing the temporal nature of student problem solving during game-based learning. [For the full proceedings, see ED593090.]