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

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 knowl edge-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. PE SE 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 c ould 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.
Source: Reading and Writing - Category: Child Development Source Type: research