Application of evolutionary algorithm- based symbolic regression to language assessment: Toward nonlinear modeling

This study applies evolutionary algorithm-based (EA-based) symbolic regression to assess the ability of metacognitive strategy use tested by the metacognitive awareness listening questionnaire (MALQ) and lexico-grammatical knowledge to predict listening comprehension proficiency among English learners. Initially, the psychometric validity of the MALQ subscales, the lexico-grammatical test, and the listening test was examined using the logistic Rasch model and the Rasch-Andrich rating scale model. Next, linear regression found both sets of predictors to have weak or inconclusive effects on listening comprehension; however, the results of EA-based symbolic regression suggested that both lexico-grammatical knowledge and two of the five metacognitive strategies tested predicted strongly and nonlinearly listening proficiency (R2 = .64). Constraining prediction modeling to linear relationships is argued to jeopardize the validity of language assessment studies, potentially leading these studies to inaccurately contradict otherwise well-established language assessment hypotheses and theories.