Predicting reading task performance, metacognition and eye movement from individual traits
REGRESSION
To answer RQ2, we applied multiple linear regression models to estimate which learner characteristics could predict reading task performance, metacognition, and eye-movement metrics. Tests for multicollinearity revealed that the predictors had a very low level of multicollinearity (all VIF < 2). For classical regression analyses, each predictor’s standardized coefficients β, and each regression model’s effect size RClassical linear regression models of BGM group (Experimental).*Classical linear regression models of silence group (Control).Please refer to Note below Table Bayesian linear regression models of BGM group (Experimental).Please refer to Note below Table Bayesian linear regression models of silence group (Control).Please refer to Note below Table The regression model comprising learner characteristics only weakly explained the variation in passage comprehension accuracy in both BGM (In the BGM group, passage reading time and several eye movement measures were significantly correlated with learner characteristics (Table Some other correlations were identified in both BGM and Silence conditions, that is, General English Proficiency (measured by LexTale test scores) had positive relationships with three lexical-level eye movement measures: First Pass Dwell Time (BGM Group: β = − .458, However, in neither BGM nor Silence conditions did we observe significant correlations between another two tested learner characteristics (Training on Music, Extraversion) and measures of reading task performance, metacognition, or eye movements based on both classical hypothesis testing (Tables 