A study of frequency-based and mobile terminal-based deep learning of college English vocabulary

Abstract

In the era of rapid development of artificial intelligence, mobile English vocabulary APP has become the primary way for college students to learn English vocabulary. At present, the effect of English vocabulary mobile learning on students' vocabulary acquisition is not satisfactory. Therefore, it is essential to analyze the causes and propose solution strategies. This paper explores the four major elements of improving students' vocabulary width and depth based on the frequency effect, deep learning, coverage, practice frequency, and automaticity. Also, automaticity is a prerequisite to alleviate lexical fossilization. A repetition rate of 5–15 times a word is a prerequisite for long-term vocabulary retention [14], while the frequency of practice and vocabulary automaticity are positive variables that contribute to the transformation of passive vocabulary into automatic vocabulary, leading to vocabulary learning with higher-order thinking and ultimately to the transformation of automatic vocabulary into the active vocabulary. The teacher plays a role in assisting students to adjust their learning mood in their autonomous deep vocabulary learning. Therefore, This paper provides a theoretical basis for optimizing English vocabulary APP and English vocabulary teaching and helps students improve vocabulary ability through efficient and autonomous deep learning methods.