A computational model of TE-dominant noticing, repetition, prior knowledge and grammatical knowledge acquisition

Abstract

AbstractComputer-assisted textual enhancement (CATE) technology has been widely used to improve English as foreign language (EFL) learners’ syntactical and grammatical learning. Visual attention, repetition, and prior knowledge are known as the vital factors in CATE-assisted knowledge-acquisition; however, there still lacks a model which can describe those factors’ intrinsic cooperating-mechanism that works in the CATE-based knowledge-acquisition. Therefore, this paper built up a computational model (PESE) of using those factors as variables, by fitting and predicting the data collected from empirical experiments with an average accuracy of 78%, PESE testified and complemented the assumptions proposed by previous studies. PESE suggested that although the efficacy of CATE is majorly decided by learners’ prior-knowledge of the targets, the interactive effects of visual-attention, repetition, and inductive activity could partly compensate for the effect from prior-knowledge, and the efficacy ceiling of repetition also could be estimated according to the ‘easy-perceiving level’ coefficient. At the end of this paper, 3 pedagogical implications were proposed for English teachers who are willing to integrate CATE into their teaching activities.