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

Yang, Juan; Qi, X.F.; Liu, R.; Wang, L.; Sun, B.

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


Juan Yang

R. Liu

L. Wang

B. Sun


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, 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.

Journal Article Type Article
Acceptance Date Feb 22, 2022
Online Publication Date Mar 23, 2022
Publication Date 2022-10
Deposit Date Mar 23, 2022
Publicly Available Date Mar 29, 2022
Journal Reading and writing.
Print ISSN 0922-4777
Electronic ISSN 1573-0905
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 35
Issue 8
Pages 1953-1974
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