Application of Evolutionary Algorithm-Based Symbolic Regression to Language Assessment: Toward Nonlinear Modeling

Abstract

This study applies evolutionary algorithm-based (EA-based) symbolic regression to assess the ability of metacognitive strategy use and lexico-grammatical knowledge to predict listening comprehension proficiency among English learners. Linear regression initially 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=.65). 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.