Academic Word List Research Articles

Academic Word List and Cefr Levels: Profiling Academic Vocabulary in a Technical University Learner Corpus

Academic Word List and Cefr Levels: Profiling Academic Vocabulary in a Technical University Learner Corpus

Developing a Word Family-Based Depth of Academic Word Knowledge Test

<p style="text-align:justify">Despite the continued widespread use of the academic word list, there is a paucity of comprehensive and easily replicable tests which measure learners’ depth of knowledge of academic word list items. This paper outlines the development and initial validation of a test which measures multiple aspects of learners’ knowledge of academic vocabulary. The test comprises four sections – one productive knowledge section (sentence writing), and three receptive knowledge sections (collocations, synonyms, and L1 meaning). Each section includes 30 items selected from the academic word list, 10 of which are derived forms containing one or more affixes. The test was administered to 193 Japanese students studying at a public medical university in Japan. All four sections of the test showed a high level of internal consistency. In addition, the inter-rater reliability between the two different markers for the productive use (sentence writing) section was also moderately high. Furthermore, Rasch item analysis showed that the test items were well fitted to comprehensively measure learners’ knowledge of academic vocabulary. These encouraging initial results suggest that there may be merit in the test being used, evaluated, and possibly adapted in other EAP teaching and learning contexts.</p>

ACADEMIC ENGLISH VERBS ACROSS FIVE DISCIPLINES IN SOCIAL AND HUMANITIES: EVIDENCE FROM CORPUS

Corpus provides a significant amount of data that can be utilised to retrieve actual language use, especially for teaching English for academic purposes. Accordingly, exploring academic English verbs (AEVs) across five disciplines, i.e., tourism, business, linguistics, management, and cultural studies, is essential. Further, it is also crucial to compare AEVs against Coxhead’s Academic Word List (AWL). A personalised corpus was built from 100 Scopus-indexed paper abstracts to achieve these objectives. Subsequently, the AntConc software was employed to retrieve AEVs. The study discovered that the top 50 AEVs varied across five disciplines and had similarities and differences, representing the characteristics and uniqueness of each field of study. The AEVs generated from the established corpus also appeared in Coxhead’s AWL. Consequently, the findings generated by this study are worth teaching, especially to EFL students, to robust the richness of verbs for academic purposes. Consequently, pedagogical implications are also described further in this study. Keywords: academic article abstracts, academic English verbs, corpus linguistics, English for academic purposes

Creating and validating a corpus-based English academic word list for physics

Abstract In this study a Physics Research Articles Word List (PRAWL) is built, which includes the most extensively used English vocabulary in the genre of physics research articles outside the most frequent general-purpose vocabulary. PRAWL can be of great use in English for Physics teaching and learning. This discipline-specific academic word list was produced from a corpus of 1.25 million running words, consisting of 200 physics research articles. PRAWL contains 929 words and has a 17.48% coverage in the corpus it was derived from. Together with 2,000 high-frequency general-purpose words, as well as words bearing no or minimal learning load, PRAWL reaches a coverage close to 95% in this corpus. We validate PRAWL in three independent corpora and also discuss the pedagogical implications and recommendations regarding its use. The list is intended for students of physics who do not have a command of English which is sufficiently adequate for reading research articles in it without outside support.

Flipped classroom with gamified technology and paper-based method for teaching vocabulary

While gamified technology integration in vocabulary instruction within a flipped classroom has yielded beneficial teaching outcomes, specific studies have raised concerns about potential adverse effects linked to this approach. As a result, conducting a comparative analysis between gamified technology and conventional paper-based methods within the flipped classroom framework has become essential. This analysis aims to foster the development of a targeted teaching approach that adeptly addresses the unique needs of students. This study employed a sequential explanatory research design to examine the effectiveness of flipped classroom with gamified technology and paper-based method in teaching vocabulary to students with different proficiency levels. Quantitative data was gathered from a pretest and a posttest, whilst qualitative data was collected through teachers’ guided reflection. Using Academic Word List (300 target words), control groups employed a paper-based, while experimental groups applied gamified technology (Quizlet, Kahoot!, Quizizz, Socrative, and Google Form), which lasted 10 weeks. The participants were 144 non-English major students who took a general English course in the 2nd semester of 2023. Quantitative data analysis ran in SPSS 25 using Paired Sample t-Test and One-way ANOVA. The qualitative data were analyzed using thematic progression. The results showed that gamified technology did not affect students’ learning outcomes, while the paper-based method resulted conversely. It revealed that the paper-based method is more effective than gamified technology for students in general, with low proficiency and high-proficiency level. Further, teachers’ beliefs admitted distinctive issues that gamified technology was more effective for high-proficiency learners, whereas paper-based was more effective for low-proficiency learners. The difference analysis of quantitative and qualitative data sheds light on discussing threats while implementing gamified technology and possible solutions.

Assembling a justified list of academic words in veterinary medicine: The veterinary medicine academic word list (VMAWL)

The assembly of corpus-based discipline-specific word lists for pedagogical purposes has recently been on the rise (e.g., Arndt, 2022; Fraser, 2007; O'Flynn, 2019; Yang, 2015). In order to cater to the needs of learners in veterinary medicine (VM) and for field-specific academic literacy, this paper analyses a reiteration of the Veterinary Medicine Corpus (Özer and Akbaş, 2023; hereafter the VMC), assembling a specified list of academic words used in published research articles (RAs) in VM. So far, VM has remained largely unexplored with the exception of Durrant's (2009) limited coverage of VM texts. The analysis was conducted using LancsBox 6.0, prompted to yield a list of PoS-tagged lemmas (P-lemmas) sorted by frequency (Coxhead, 2000; Yang, 2015) and dispersion (Arndt, 2022). The most frequent 1,000 lemmas for each content word category were collated into a candidate list. We then manually eliminated proper nouns and GSL (West, 1953) words. The final list, the Veterinary Medicine Academic Word List (VMAWL), contains 835 PoS-tagged lemmas (P-lemmas), a reiteration of which was produced by adding the types in Bauer and Nation’s (1993) taxonomy at the second level to test coverage. The VMAWL was profiled and validated against the four parent categories independently on AntWordProfiler (Anthony, 2022a) with analysis showing the VMAWL comprises 13.75 % of the VMC and diverges greatly from generic word lists like the AWL, NGSL, new-GSL, and AVL. The list can be used to develop teaching materials for EAP or ESP academic writing courses.

VOCABULARY BENCHMARKING FOR THE COMPREHENSION OF CEFR-ALIGNED ASSESSMENT READING TEXTS

In Malaysia, research on the essential vocabulary for academic comprehension among pre-university and university ESL students is rather limited. This study introduces the "Comprehension Corpus" to pinpoint critical words vital for reading understanding. This study aims to develop the Malaysian University English Test (MUET) Reading Corpus, in order to identify the vital vocabulary for text comprehension and the specific categories or word lists that improve reading based on their texts coverage in the text. In addition, the vocabulary size needed to comprehend the comprehension texts was identified. By analysing CEFR-aligned texts using tools like RANGE BNC-COCA and WordSmith, it was found that to comprehend 98% of the content, students needed familiarity with 8,000-word families. this extensive demand, a streamlined list of 100 words, grouped by frequent topics and enhanced with the New General Service List (NGSL) and New Academic Word List (NAWL), was developed. The research underscores the necessity for educators to adopt targeted vocabulary teaching methods, highlighting the interplay between vocabulary breadth and reading comprehension. This tailored approach aids teachers in addressing students' specific lexical needs, ensuring more effective academic reading outcomes.

A frequency, coverage, and dispersion analysis of the academic collocation list in university student writing

Abstract This study investigates whether cross-disciplinary academic collocation lists are of equal importance to EAP (English for academic purposes) learners across different disciplines. We target Ackermann and Chen’s (Ackermann, Kirsten & Yu-Hua Chen. 2013. Developing the Academic Collocation List (ACL)–A corpus-driven and expert-judged approach. Journal of English for Academic Purposes 12(4). 235–247) Academic Collocation List (ACL) and conduct a frequency, coverage, and dispersion analysis of its 2,464 pairs in the British Academic Written English (BAWE) corpus. Using BAWE data, we also examine whether university students increase use of academic collocations across years. Generally, our results indicate that cover-all academic collocation lists, like academic word lists, show uneven frequency distributions across disciplines; the ACL covers considerably more word combinations in soft than hard sciences. According to our dispersion analysis, furthermore, an important cause for such skewed distributions is that a substantial part of the ACL (i.e., almost 25 %) is in actuality discipline-specific rather than generic collocations. In terms of collocation development, we do observe steady increases across years. Interestingly, university students throughout levels not only use more academic collocations, but display a stronger tendency to raise the proportion of discipline-specific collocations in writing. This study is novel as it systematically examines distributions and developments of academic collocations, and is pedagogically valuable in that it collects 483 ACL entries which are suitable targets for EAP learning.

Lexical Demands of Academic Written English: From Students’ Assignments to Scholarly Publications

The article presents a lexical study that investigates the lexical demands of academic written texts at different levels of writing. By employing the British National Corpus/ Corpus of Contemporary American English (BNC/COCA) word list and the Academic Word List (AWL), the present study analyzed data from the British Academic Written English (BAWE) corpus, which contained 2,761 student assignments, and the Public Library of Science One (PLOS ONE) corpus which included 4,000 scholarly articles. Results from the analyses demonstrated significant differences in lexical difficulty between students’ assignments and scholarly publications. The proportion of academic vocabulary in written texts was also found to increase as the writers’ levels went up. Cross-discipline comparisons highlighted the difference in lexical difficulties between scientific disciplines.

A Study of Academic Vocabulary Use by Advanced EFL University Students

Academic writing is one of the most essential skills for university students’ success during and beyond their study. Successful academic writing requires having peculiar grammatical and lexical features. One of the best-known features of academic writing style is the use of academic words. In light of these facts, the current study has been conducted to explore different aspects of academic words use by EFL students. The main aim of the study is to discover how proficient the students are in using academic words in general. In addition, the study aims to know the size and frequency of academic words that students demonstrate in their writing. It is also expected from the study to gain insight into students’ vocabulary knowledge through using academic words. Making students’ academic word list (hereafter AWL) and classifying them based on the Coxhead’s sub-lists is the ultimate goal of the study. For this purpose, a specialized corpus of 34 argumentative essays was collected from advanced EFL students. The essays were analyzed and interpreted by a well-established web-based corpus tool, ‘Text Inspector’. An outstanding conclusion drawn from the study is that the Kurdish EFL students of advanced level have a satisfactory proficiency in using academic words in general. Although the size of academic vocabulary observed in the essays is relatively small, students have proved their determined vocabulary knowledge as most of the used words are among the high frequency ones and contain various derivational forms.

Orthographic similarity ratings for English-Spanish cognates from the academic word list

Orthographic similarity ratings for English-Spanish cognates from the academic word list

The Dimensionality of Lexical Features in General, Academic, and Disciplinary Vocabulary

ABSTRACT Purpose There are many aspects of words that can influence our lexical processing, and the words we are exposed to influence our opportunities for language and reading development. The purpose of this study is to establish a more comprehensive understanding of the lexical challenges and opportunities students face. Method We explore the latent relationships of word features across three established word lists: the General Service List, Academic Word List, and discipline-specific word lists from the Academic Vocabulary List. We fit exploratory factor models using 22 non-behavioral, empirical measures to three sets of vocabulary words: 2,060 high-frequency words, 1,051 general academic words, and 3,413 domain-specific words. Results We found Frequency, Complexity, Proximity, Polysemy, and Diversity were largely stable factors across the sets of high-frequency and general academic words, but that the challenge facing learners is structurally different for domain-specific words. Conclusion Despite substantial stability, there are important differences in the latent lexical features that learners encounter. We discuss these results and provide our latent factor estimates for words in our sample.

A corpus-based study of vocabulary in massive open online courses (MOOCs)

Massive open online courses (MOOCs) provide rich academic content to learners around the world. However, understanding such content is challenging to second-language learners. Given the importance of vocabulary knowledge to comprehension, this study constructed a 10.2-million-word corpus of MOOCs from four disciplinary areas (engineering, humanities and arts, science and math, and social sciences), and examined (a) the lexical demands of MOOCs, (b) the coverage of general and discipline-specific academic vocabulary lists in MOOCs, and (c) the extent to which these lists helped learners with MOOCs' lexical demands. The results show that – together with proper nouns, marginal words, transparent compounds, and acronyms – the most frequent 3,000 and 4,000 word families of general English respectively provide 90% and 95% coverage of the corpus, indicating that MOOCs are as lexically challenging as university lectures. Also, because both general and discipline-specific academic vocabulary lists provide high coverage of MOOCs, studying them can lower students' learning burdens and help them achieve higher coverage of MOOCs than learning words by frequency. Lastly, based on learners’ existing vocabulary knowledge and target disciplines, this study provides pedagogical recommendations to teachers on how to employ general and discipline-specific academic word lists as vocabulary support for EAP students.

Academic Words in Discourse News: A Corpus Based Study Of Pakistani English Newspaper

This study addressed two research questions regarding academic words used in the Corpus of Pakistani English Newspaper Articles (COPENA).Firstly it asked about the frequently used words in the corpus of Pakistani English Newspaper, Secondly it was asked, to what extent authors or editor use Academic words in newspaper as compared with Coxhead 570 (AWL). Total corpus was 414435 running words taken from online Newspaper; E. dawn and The Nation. The computer software ANTCONC was used to find out the frequency and percentage of words frequently occurred in COPENA. Another tool WORDSMITH was used to find out the frequency of AWL (Academic word list) in COPENA. The result revealed that words in the AWL covered 2.58% of the total words in the corpus, and frequency and percentage of words in COPENA are clearly presented in the table. It was also revealed from the result that almost all the AWL is present in COPENA with high frequency. So the result indicated that newspaper articles might be highly beneficial for learning a language and academic achievement.

A Corpus-Based Mechanical Engineering Academic Word List

A Corpus-Based Mechanical Engineering Academic Word List

LEXICAL ERRORS IN SECOND LANGUAGE ACADEMIC WRITING

The main focus of error analysis, a branch of applied linguistics, is investigating and studying errors that are made by second language (L2) learners. The present study is based on quantitative and qualitative analysis as well as corpus-aided analysis of lexical errors in academic vocabulary. The study aims to assist educators and teachers to develop, organize lessons or courses, which will enhance the effectiveness of academic vocabulary learning on the part of the students. Based on the results of the given research, teachers can refer to the errors in academic vocabulary, which will help them notice the gap in Academic Word List (AWL), by raising their awareness and emphasizing on certain areas of academic vocabulary. The instruments of the study were 39 essay scripts written by the AUA (American University of Armenia) first-year students majoring in English and Communications.

The Implementation of Academic Word List and Its Implication to the Improvement of EFL Students’ Academic Writing Quality

The research aimed to investigate the effectiveness or ineffectiveness in implementing an academic word list and its implication for improving English as a Foreign Language (EFL) students’ writing quality. Academic Word List (AWL) played an important role in expanding the student’s academic vocabulary knowledge. Basically, an academic word list was considered one of the effective ways to reduce students’ errors in writing. The research was proposed to review several related preceding studies, and the data found were described qualitatively. From the research conducted, it is found that fifteen out of eighteen studies (83,33%) confirm that the implementation of academic word lists provides a significant contribution to the improvement of students’ writing quality. On the other hand, three out of eighteen studies (16,67%) confirm that implementing an academic word list does not significantly contribute to improving students’ academic word knowledge. This may be attributable to the given portion of the academic word list utilized as supplementary material, and it can only be used outside the classroom without any teacher guidance. The result shows that implementing the academic word list may act as an effective/ineffective tool for improving students’ academic writing quality based on the portion and utilization of this tool in the course.

Evaluation of keyness metrics: performance and reliability

Abstract The methodological debates surrounding keyword analysis have given rise to a wide range of keyness metrics. The present paper delineates four dimensions of keyness, which distinguish between frequency- and dispersion-related perspectives. Existing measures are then organized according to these dimensions and evaluated with regard to their performance on a specific keyword analysis task: The identification of key verbs in academic writing. To this end, the rankings produced by 32 different metrics are evaluated against an established academic word list. Further, the reliability of measures is assessed, to determine whether they produce stable rankings across repeated studies on the same pair of text varieties. We observe notable differences among metrics with regard to these criteria. Our findings provide further support for the superiority of the Wilcoxon rank sum test and text-dispersion–based measures, and allow us to identify, within each dimension of keyness, metrics that may be given preference in applied work.

The History, Present Situation and Trend of Specific Topic Word List

As an important tool in vocabulary learning, word lists play an important role in vocabulary research. From the perspective of diachronic and synchronic, this paper comprehensively analyzes the research process, current situation and trend of word lists with specific themes, and summarizes the construction of its corpus, vocabulary screening criteria and the presentation of vocabulary. The results show that: 1) In terms of the construction of the corpus, the size of the corpus used in the construction of various thesaurus varies, but the written language corpus of the academic word list with specific topics is mostly composed of journal papers and textbooks. The topic - specific technical word list is related to manuals, reference books and other more specialized genres. 2) In terms of vocabulary selection criteria, most the academic word list with specific topics adopts frequency, range and specialized occurrence, but specialized occurrence has been questioned in recent years. The recognition methods of technical words are mostly mixed. In addition, scholars will also use criteria such as frequency and range to further select words, but the adoption of the criteria of range is also controversial. 3) In terms of the presentation of vocabulary, the academic word list with specific topics mostly uses lemma, while the technical word list mostly uses word type. The findings of this paper will provide references for ESP researchers and English teachers on the development and application of vocabulary in this subject area in their research and teaching.

A New Computer Science Academic Word List

This corpus-based vocabulary study aimed to develop a new computer science academic word list across ten sub-disciplines of computer science defined by Association for Computing Machinery (hereafter ACM). A corpus of Computer Science containing 2,500,990 running words was developed from 300 Computer Science Research Articles (hereafter CSRAC) as a database of this study. Drawing on and combining procedures and methods from Coxhead (2000), Gardner and Davies (2014) and other previous studies, this study developed a New Computer Science Academic Word List (hereafter NCSAWL), containing the most frequently-used computer words in computer research articles from the corpus. The NCSAWL contains 444 words, which accounts for approximately 20.33% of the coverage in the CSRAC, the NCSAWL has a much better coverage of computer English. The result of this study has numerous implications for computer science learners, English teachers, researchers, as well as material writers and course syllabus designers. For examples, English computer teachers should focus on teaching learners the most high-frequent words which have a dispersed coverage and have special meaning and use in the discipline of computer science. Teachers could also raise the awareness of learners that some words have different meanings and uses in general English. The material designers for English for academic and special purposes could incorporate the NCSAWL vocabulary into their academic reading and writing materials for computer science students. Researchers and English language teachers who are interested in expanding their computer science academic vocabulary could also use this NCSAWL.