Modeling Comprehension Processes via Automated Analyses of Dialogism

Dialogism provides the grounds for building a comprehensive model of discourse and it is focused on the multiplicity of perspectives (i.e., voices). Dialogism can be present in any type of text, while voices become themes or recurrent topics emerging from the discourse. In this study, we examine the extent that differences between self-explanations and thinkalouds can be detected using computational textual indices derived from dialogism. Students (n = 68) read a text about natural selection and were instructed to generate selfexplanations or think-alouds. The linguistic features of these text responses were analyzed using ReaderBench, an automated text analysis tool. A discriminant function analysis using these features correctly classified 80.9% of the students’ assigned experimental conditions (self-explanation vs. think aloud). Our results indicate that self-explanation promotes text processing that focuses on connected ideas, rather than separate voices or points of view covering multiple topics.