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

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