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The present study compared 4-word lexical bundles found in a general engineering corpus (2,030,000 words) with those found in a corpus of texts collected from a Pathway engineering course for ESL (English as a Second Language) students (356,000 words) and a corpus of pedagogical materials used to teach advanced ESL students at an intensive English...
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... Cortes (2004) indicates, LBs belonging to this category include those that are largely not present in the corpora used in previous studies. As expected, the distributions of the functional characteristics of the LBs identified in the engineering corpora differed substantially from the LBs found in the GAEC (see Table 5). However, the distributions for both engineering corpora were quite similar. ...Citations
... Setren, E, based on the Boston Liberal Studies teaching practice case, discusses the results of public English general education, and high-quality English general education dramatically improves the possibility of students receiving special education to enter higher education institutions [14]. Nekrasova-Beker et al. compare the Engineering Corpus, the Pathways Engineering Corpus Vocabulary, and the advanced version of the Pathways Engineering Corpus and point out that a finer classification of specialties has a more specialized English corpus [15]. Zammit, N. et al. pointed out the need for a deeper understanding of the specific requirements of local vocational skills and career development to adjust the teaching expertise and English proficiency scoring shares in Maltese vocational and further education, where performance scores place more emphasis on specialized knowledge. ...
... Jun Song. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] ...
In this paper, we first use Hamming distance to mine students’ English achievement, construct the data matrix to get the English achievement, and analyze the standard deviation of each row and column in the matrix to get the similarity and the degree of dispersion among the students’ accomplishments. The BGLL algorithm is used to analyze achievement data by clustering. The modularity of the data calculated by clustering is applied to the weighted graph to obtain the results of achievement clustering analysis. By analyzing the effect of the fusion teaching mode of academic English and general English after practice, it was found that the English reading and speaking scores of the students in the experimental class improved significantly. There was a significant difference between the reading posttest scores of the experimental class and those of the control class (p =0.002<0. 05), with a difference of nearly 10 points in the average scores. The student’s awareness score of the importance of English learning after teaching practice reached 4.308, the attitude score of English learning motivation averaged about 3.192, and the student’s knowledge and understanding of English teaching objectives were also more precise, with a mean value of 3.399. The application effect of the integrated English teaching model proposed in this paper is excellent, which lays the foundation for the integrated teaching of academic English and general English, and also provides reference data for the application of the teaching model in the future.
... -The improved flipped classroom teaching mode of the dataset improves the teaching efficiency, which is more conducive to the teachers' teaching work, and makes the students' learning information and interest in learning significantly improved. Literature [21] compares the vocabulary bundles of four words found in the general engineering corpus, and the results show that the discourse features in the general engineering corpus are used by the paths and the structure and structure of the vocabulary bundles in the engineering materials. Literature [22] describes the application status and characteristics of ESP teaching, which is a product of the combination of professional knowledge and English practical teaching, ESP teaching theory has the characteristics of intuition, flexibility and so on, and can effectively cultivate the students' thinking ability and the ability to use English. ...
The teaching of English courses in colleges and universities relies too much on grades to judge students’ mastery of knowledge points, and there are problems of slow feedback and difficulty in timely targeted teaching. To address the drawbacks of traditional English teaching methods, this paper constructs a random forest algorithm based on the decision tree model, uses the information gain index to judge the feature segmentation effect of the random forest, and calculates the degree of students’ mastery of knowledge points. At the same time, the recommendation model is utilized to match students with topics equivalent to their mastery level, and the alternating least squares method is introduced to improve its recommendation efficiency, thus constructing an efficient English teaching course assistance model. The model was implemented in colleges and universities after it had been designed. The highest similarity match of the educational resource ontology tree was achieved when the weights of knowledge points, resource difficulty, and resource type were 1.121, 0.986, and 1.129, respectively. The average score of the class was 65.3 before the application, after the application, the second test score increased to 69.28, which was a significant improvement, and the fourth test was 75.32, which exceeded the average score of 72.23. The investigation of this study shows the direction for the innovation of the English curriculum and promotes the benign development of English teaching.
... Regarding structural characteristics, lexical bundles are often classified into categories such as noun-based structures, verb-based structures, prepositional-phrase fragments, adjective-based structures and other structures (Biber et al. 1999). A substantial body of research examining lexical bundles in previous studies mostly focused on either describing their use in different disciplines or registers (Biber et al. 1999;Biber et al. 2004;Biber and Barbieri 2007;Hyland 2008Hyland , 2012Durrant 2017;Nekrasova-Beker and Becker 2019;Wright 2019) or comparing the use of lexical bundles by native and non-native speakers and by expert and novice writers (Cortes 2004;Chen and Baker 2010;Leńko-Szymańska 2014;Salazar 2014;Pan et al. 2016;Bychkovska and Lee 2017;Esfandiari and Barbary 2017;Pan and Liu 2019). These studies, though different in their research aims, have seen and proven the importance of phraseological expressions in academic discourse. ...
With emerging developments in the use of computer and corpus-linguistic software to analyse language features and linguistic patterns, competent language users frequently rely on a stock of semi-automatic, prefabricated word chunks, rather than constantly creating new combinations of individual words. This has illuminated ways to improve academic writing among novice writers and students. Researchers have begun compiling academic expressions by deriving pedagogically useful lists of phraseological expressions, such as collocations (e.g. dark night), lexical bundles (e.g. the importance of) and phrase frames (e.g. the * of). These studies have widely advocated the use of a wide-angle approach in compiling academic phraseological expressions by focusing on language forms common in all disciplines. In the current corpus-driven study, we expand this line of research by arguing that discipline-specific academic phraseological expressions should be explored further by focusing on phrase frames that have so far received little attention from scholars. Findings from the study will help novice writers and students by providing them a repository of phrase frames that are pedagogically useful.
... Most of them have found a mismatch between the lexical bundles presented in ELT materials and that of a reference corpus (Allan, 2017;Alquraishi, 2014;L. Chen, 2010;Coxhead et al., 2017;Wood, 2010;Wood & Appel, 2014), with only one study reporting a match in their data (Nekrasova-Beker & Becker, 2019). For example, Allan (2017) analyzed lexical bundles in five different selfstudy books for English language learners to examine their frequency, structural and functional patterns by comparing them to those used in a spoken corpus of conversational English as a Lingua Franca (ELF). ...
Current English Language Teaching (ELT) textbooks have largely adopted the communicative approach by using authentic materials to foster EFL students’ communicative competence. However, the communicative status of Saudi high school English textbooks has been underexplored. One way to assess the authenticity of Saudi EFL textbooks is by considering their use of a frequent linguistic item known as lexical bundles. Thus, the present study investigated whether the lexical bundles in communicative Saudi high school textbooks are representative of conversational English. This comparative corpus study used a lexical bundle approach to compare the ten most frequent lexical bundles in the textbooks to those in an English reference corpus. Results show that three and four-word lexical bundles are less frequent in the textbooks compared to the reference corpus and that there is considerable variation in the structural and functional patterns of the bundles in the two corpora. Pedagogical implications are discussed in light of the findings.
The use of technical terminology in EAP setting offers a significant challenge in terms of lexico-grammatical aspects of the English language and the efficient use of word combinations in the form of cluster and N-gram structures. Moreover, academic writing or "article writing" in EAP setting can be a source of further difficulty for novice graduate students who have not yet been exposed to structured lexical patterns (special jargon) and the most frequent lexical combinations (word structures) in their specific fields, which may create a sense of an "inefficient language user profile" on their parts. Thus, this contrastive corpus-based study investigated the most frequently used specific terminology with one engineering field (mechanical engineering) and compared it to a larger framework through a general engineering reference corpus. The study compared a small mechanical engineering article corpus (MecEnCorp) to the general engineering corpus (GenEngCorp) to find out various shared lexical properties of a specific engineering field against the larger one. We also aimed to determine the extent of mutual correspondence between the two corpora in terms of lexico-grammatical aspects of the defined field. The findings provided useful insights for the future implementation of EAP classes in university setting in Turkey and elsewhere in the world so that EAP-based technical vocabulary, frequent collocates, frequent cluster pairs, and various N-gram groups are sufficiently known and used by EAP writers. The results were discussed along with their pedagogical implications, and the relevant suggestions/recommendations were made regarding the successful implementation of future EAP classes in Turkish universities.
Formulaic language has traditionally not been given sufficient consideration by ELT materials developers. Thus, the lexical bundles (LBs) used in reading texts found in high stakes language tests may not reflect the kinds of phrasings used in the target domain of the test calling its validity into question. To investigate this issue in the English section of the Korean college scholastic ability test (CSAT), all of the reading texts from the 2017 through 2021 CSAT examinations were used to build a CSAT corpus which was compared with the Manually Annotated Sub-Corpus (MASC) reference corpus and Martinez & Schmitt’s (2012) PHRASE list. LBs were extracted
for the analysis using Wordsmith 8. The analysis involved examining the frequency and distribution of lexical bundles and other linguistic features in the two corpora. It was found that only two LBs were shared between the top ten most frequent phrases in the CSAT and MASC, indicating that most of the high-frequency LBs in the CSAT were not the same as those in the MASC. The structures and functions of LBs were also compared, and while there were some minor differences between the two corpora, they were generally similar. Additionally, the CSAT contained
fewer useful LBs than the MASC, but the difference was not large. Based on these findings, it is suggested that CSAT creators give more consideration to the formulaic language used in the adapted texts used in the CSAT.
This study examined the use of lexical bundles in the results and discussion sections of public health research articles (RAs) by comparing the native English writers with Iranian non-native English writers. To this end, four-, five-, and six-word lexical bundles were contrastively investigated in two different sub-corpora i.e. a native English corpus (NEC) and a non-native English corpus (NNEC). The corpus contained 496 985 words, and each sub-corpora included 100 RAs. The RAs were then examined structurally and functionally. The data were analysed both quantitatively, using frequency count and chi-square analyses, and qualitatively through content analysis. Based on the results, from among the lexical bundles, 29 four-word, 17 five-word, and one six-word bundles were identified in the NEC, while 52 four-word, 27 five-word, and five six-word bundles were found in the NNEC. The findings highlight that Iranian non-native English authors employed more four-word, five-word and six-word lexical bundles than native English authors. The descriptive and overall findings also suggested some differences in the two groups’ functional and structural patterns of lexical bundles, whereas statistically insignificant differences were identified in the structural patterns of bundles in the groups. These findings lay considerable emphasis on the central part that lexical bundles play in English for academic purposes (EAP) and English for specific purposes (ESP) courses.
