Examining the association between elementary students’ lexcio-syntactic writing features and cognitive-motivational profiles using Natural Language Processing

AbstractNatural language processing (NLP) provides an innovative avenue to understand and explore human language content, yet minimal research has utilized it to classify students’ literacy, cognition, or motivation. This study investigated the association between grade 4-6 students’ (n = 143) writing and their cognitive-motivational profiles (CMPs) based on their self-regulated learning, locus of control, writing self-efficacy, and goal-orientation. LPA (Mplus 7.4) results indicated a two-class CMP solution with predominantly positive or negative CMPs. Using NLP, 404 lexico-syntactic writing features were extracted from students’ writing. Random forest with 10-fold cross-validation was implemented in Weka 3.8 (with SMOTE to equate class instances) to accurately (93%) classify students’ CMPs (class 1 True Positive Rate (TPR) = .942; class 2 TPR = .925) based on the NLP-processed lexico-syntactic writing features. These results highlight the potential for machine learning to analyze students’ writing and accurately classify learner profiles to provide formative feedback and customized interventions.

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