A Study of Vocabulary Teaching Strategies in ESP Instruction and Their Effects on Students' English Vocabulary Learning Using the Subsumption Sorting Algorithm

Abstract

To facilitate the application of the subsumption sorting algorithm within vocabulary instruction, this study develops a novel Learning Management System (LMS). This system adopts the ESP vocabulary teaching model as its primary instructional framework delves into the requisites of English vocabulary acquisition, the system's architecture, and its instructional content. It employs an enhanced DTW pattern-matching algorithm to dynamically refine English vocabulary resources. Furthermore, it integrates the merging and sorting algorithm to augment the efficiency of the LMS operations, enabling precise management of student learning progression and vocabulary retention. Comparative analyses of various sorting algorithms were conducted alongside assessments of the impacts of the LMS and ESP vocabulary teaching models on vocabulary learning outcomes. The findings from a semester-long study of vocabulary instruction indicate significant improvements within the experimental group: increases of 6.3, 5.32, and 9.33 points in word form, meaning, and usage accuracy, respectively. Notably, the usage of vocabulary saw the most substantial enhancement. The ESP model-based English vocabulary LMS effectively ameliorates students' vocabulary usage errors and enhances accuracy in vocabulary application.