Idea Generation in Student Writing: Computational Assessments and Links to Successful Writing
Idea generation is an important component of most major theories of writing. However, few studies have linked idea generation in writing samples to assessments of writing quality or examined links between linguistic features in a text and idea generation. This study uses human ratings of idea generation, such as idea fluency, idea flexibility, idea originality, and idea elaboration, to analyze the extent to which idea generation relates to human judgments of essay quality in a corpus of college student essays. In conjunction with this analysis, linguistic features extracted from the essays are used to develop a predictive model of idea generation to further understand relations between the language features in an essay and the idea generation scores assigned to that essay. The results indicate that essays rated as containing a greater number of ideas that were flexible, original, and elaborated were judged to be of higher quality. Two of these features (elaboration and originality) were significant predictors of essay quality scores in a regression analysis that explained 33% of the variance in human scores. The results also indicate that idea generation is strongly linked to language features in essays. Specifically, the use of unique multiword units, more difficult words, semantic but not lexical similarities between paragraphs, and fewer word repetitions explained 80% of the variance in human scores of idea generation. These results have implications for writing theories and writing practice.
|Keywords||cognitive writing models, college student essays, corpus linguistics, linguistic features and writing quality, linguistics, natural language processing|
Crossley, S.A. (Scott A.), Muldner, K, & McNamara, D.S. (Danielle S.). (2016). Idea Generation in Student Writing: Computational Assessments and Links to Successful Writing. Written Communication, 33(3), 328–354. doi:10.1177/0741088316650178