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Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school students (n = 232) generated self-explanations while they read a science text. Recurrence Plots were generated to show qualitative differences in students' linguistic sequences that were later quantified by indices derived by Recurrence Quantification Analysis (RQA). To predict self-explanation quality, RQA indices, along with summative measures (i.e., number of words, mean word length, and type-token ration) and general reading ability, served as predictors in a series of regression models. Regression analyses indicated that recurrence in students' self-explanations significantly predicted human rated self-explanation quality, even after controlling for summative measures of self-explanations, individual differences, and the text that was read (R2 = 0.68). These results demonstrate the utility of RQA in exposing and quantifying temporal structure in student's self-explanations. Further, they imply that dynamical systems methodology can be used to uncover important processes that occur during comprehension.

The assessment of writing proficiency generally includes analyses of the specific linguistic and rhetorical features contained in the singular essays produced by students. However, researchers have recently proposed that an individual's ability to flexibly adapt the linguistic properties of their writing might more closely capture writing skill. However, the features of the task, learner, and educational context that influence this flexibility remain largely unknown. The current study extends this research by examining relations between linguistic flexibility, reading comprehension ability, and feedback in the context of an automated writing evaluation system. Students (n = 131) wrote and revised six essays in an automated writing evaluation system and were provided both summative and formative feedback on their writing. Additionally, half of the students had access to a spelling and grammar checker that provided lower-level feedback during the writing period. The results provide evidence for the fact that developing writers demonstrate linguistic flexibility across the essays that they produce. However, analyses also indicate that lower-level feedback (i.e., spelling and grammar feedback) have little to no impact on the properties of students' essays nor on their variability across prompts or drafts. Overall, the current study provides important insights into the role of flexibility in writing skill and develops a strong foundation on which to conduct future research and educational interventions.

In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students' self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students' comprehension scores at both surface- and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students' overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students' comprehension.