The current study examined the degree to which the quality and characteristics of students’ essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students’ essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students’ holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

Research on individual differences indicates that students vary in how they interact with and perform while using intelligent tutoring systems (ITSs). However, less research has investigated how individual differences affect students’ interactions with game-based features. This study examines how learning outcomes and interactions with specific game-based features (off-task personalization vs. on-task mini games) within a game-based ITS, iSTART-ME, vary as a function of students’ performance orientation. The current study (n=40) is part of a larger study (n=126) conducted with high school students. The analyses in this study focus on those students assigned to iSTART-ME. Results indicate that students with higher levels of performance orientation perform better during training, progress further within the system, and interact less frequently with off-task game-based features. These results provide further evidence that individual differences play an important role in influencing students’ interactions and achievement within learning environments.