Academic Vocabulary List Research Articles

Developing and Validating an Academic Vocabulary List in Russian: A Computational Approach

AbstractTo date, attempts at empirically validating a construct of academic vocabulary in the form of a frequency list in languages other than English remain conspicuously absent in peer‐reviewed journals. This study aims to close this gap by using Russian as a case study to develop an academic vocabulary list and prove its viability through a variety of data science methods, including cross‐validation and out‐of‐sample coverage. Our findings support the utility of such a construct in Russian and its potential impact on teaching Russian for academic purposes.

The creation and evaluation of a grammar pattern list for the most frequent academic verbs

The current research project was designed according to the conceptualization that vocabulary knowledge is multidimensional (Jaen, 2007; Qian & Schedl, 2004) and the learning of grammatical and collocational patterns should be considered as important as, if not more important than, the acquisition of semantic knowledge of single words. The purposes of the project were to (1) propose a method of extracting grammar patterns for the most frequent content words (verbs, nouns, adjectives and adverbs) in a given corpus; and (2) derive a tentative grammar pattern list for the 115 most frequent academic verbs.The extraction of grammar patterns was realized through a rule-based programming approach, where the researchers coded all possible patterns of the most frequent academic verbs and used the script to extract grammar patterns from a given corpus. The extracted grammar patterns were compiled with corpus-based frequency information and developed into a grammar pattern list for the top-frequency academic verbs. The grammar pattern list is expected to complement the widely used EAP (English for academic purposes) word list, the Academic Vocabulary List (Gardner & Davies, 2014), by specifying the frequency of each verb patterns (e.g. provide n with n, select n from n, base n on n).

Spanish L1 EFL learners’ recognition knowledge of English academic vocabulary: The role of cognateness, word frequency and length

Abstract Academic vocabulary knowledge predicts students’ academic achievement across educational levels. English academic vocabulary knowledge is especially valuable because English is used in academia worldwide. Therefore, examining the factors that can predict English academic vocabulary knowledge can inform pedagogy, thus indirectly boosting students’ chances of academic success around the world. This study examines the extent to which cognateness, word frequency and length predict the ability of English as a Foreign Language (EFL) learners who have Spanish as their first language (L1) to recognise written English academic words. 38 Spanish L1 university students’ recognition knowledge of English cognates was measured via a Yes/No test containing words sampled from the most frequent 1,000 lemmas of the Academic Vocabulary List (Gardner and Davies 2014). 34 participants’ data were retained in the final analysis, a multiple regression with item facility (IF) as the outcome variable and word frequency, cognateness and word length as predictors. Most of the IF variance is explained by word frequency, followed by cognateness and finally a frequency by cognateness interaction whereby word frequency is more predictive of IF for non-cognates than cognates. These findings indicate that academic cognate-word awareness raising activities can be worthwhile. Implications for research and pedagogy are discussed.

Enriching the academic wordlist and Secondary Vocabulary Lists with lexicogrammar: Toward a pattern grammar of academic vocabulary

The interaction between lexis and grammatical patterns has been known in the literature as lexicogrammar, and research in this area has suggested that learning vocabulary necessitates learning accompanying grammatical patterns. Existing academic wordlists, focusing on isolated vocabulary items out of grammatical context, thus could be enriched with their target vocabulary's common grammatical patterns. This paper therefore describes the lexicogrammar of academic vocabulary on the widely used Academic Word List and recently published Secondary Vocabulary Lists. From a corpus of secondary school reading material, it extracts the grammatical patterns frequently associated with academic vocabulary. Drawing on frameworks such as Pattern Grammar and Construction Grammar, and using methods such as colligation and collexeme analysis, this study describes the subset of academic vocabulary associated with particular grammatical patterns. Furthermore, the results of this study are developed into a pedagogical resource that lists the vocabulary and grammar interactions, along with authentic examples of the interaction from the corpus and relevant statistical information. The paper offers specific pedagogical uses of how the resource, named the Lexicogrammar of Academic Vocabulary lists (LAV lists), can be used for developing the breadth and depth of academic vocabulary knowledge in students.

The Academic Vocabulary List in Linguistics for EFL University StudentsThis work was supported by the Ajou University research fund. An earlier version of this paper was presented at the third Asia Pacific Corpus Linguistics Conference, held in Takamatsu, 2018.

The Academic Vocabulary List in Linguistics for EFL University StudentsThis work was supported by the Ajou University research fund. An earlier version of this paper was presented at the third Asia Pacific Corpus Linguistics Conference, held in Takamatsu, 2018.

The Academic Vocabulary List (AVL) coverage in the Brazilian academic written English corpus

This paper is divided in two parts, in the first one we address the different definitions of academic vocabulary, the role of academic vocabulary in English for Academic Purposes (EAP) teaching, and present some of the academic word lists compiled up to the present day. The second part of this paper is dedicated to one of the applications of these word lists: to determine the vocabulary profile of a corpus. Hence, the investigation conducted relies on the Academic Vocabulary List (AVL) to determine the use of academic vocabulary in a corpus of Brazilian students. It also addresses the issue of the different coverage provided by the AVL and the Academic Word List (AWL). The results indicate that the AVL is more representative of academic vocabulary in the corpus used as a reference.

Developing a writing assistant to help EAP writers with collocations in real time

AbstractCorpora have given rise to a wide range of lexicographic resources aimed at helping novice users of academic English with their writing. This includes academic vocabulary lists, a variety of textbooks, and even a bespoke academic English dictionary. However, writers may not be familiar with these resources or may not be sufficiently aware of the lexical shortcomings of their emerging texts to trigger the need to use such help in the first place. Moreover, writers who have to stop writing to look up a word can be distracted from getting their ideas down on paper. The ColloCaid project (www.collocaid.uk) aims to address these problems by integrating information on collocation with text editors. In this paper, we share the research underpinning the initial development of ColloCaid by detailing the rationale of (1) the lexicographic database we are compiling to support the collocation needs of novice users of English for Academic Purposes (EAP) and (2) the preliminary visualisation decisions taken to present information on collocation to EAP users without disrupting their writing. We conclude the paper by outlining the next steps in the research.

만 5세 유아와 초등학교 1학년을 위한 학습 도구어 조사 연구

본 연구의 목적은 유치원 만 5세와 초등학교 1학년 교실의 학습활동에서 사용되는 학습도구어 현황을 조사하는 것이다. 본 연구의 목적을 위해 만 5세 누리과정 지도서 11권, 만 5세 3개 학급에서 수집된 유아와 교사 간 담화 자료를 바탕으로 말뭉치를 구축한 후 분석 대상 표제어를 추출하였다. 또한 2015 개정 초등학교 1학년 1, 2학기 교과서 12권 그리고 초등학교 1학년 3개 학급에서 수집된 1학년 유아와 교사 간 담화 자료를 바탕으로 말뭉치를 구축한 후 분석 대상 표제어를 추출하였다. 유치원 교사 3인이 추출된 표제어를 독립적으로 평정하는 과정을 거쳐 147개의 학습 도구어와 초등교사 3인이 독립적으로 추출된 표제어를 독립적으로 평정하는 과정을 거쳐 학습 도구어 168개를 선정하였다. 추출된 어휘와 국내 선행연구에서 제안된 어휘 목록을 비교해 본 결과 60-80%의 어휘가 일치하는 것으로 나타났다. 마지막으로 추출된 학습 도구어의 난이도 수준을 김광해의 어휘 등급을 기준으로 분석한 결과 유치원 학습 활동을 위한 학습 도구어의 20% 이상이 상급과 고급에 해당하는 3, 4등급 어휘로 나타났다. 초등학교 1학년 학습 활동을 위한 학습 도구어 역시 경우 약 20%의 어휘가 고난이도인 3, 4등급에 해당하는 것으로 나타났다. 본 연구결과는 유치원 시기부터 학습 도구어 교육이 필요함을 시사한다.

The Development of Academic Vocabulary among Arabic Native Speaking Middle School Pupils: How Much Do They Really Know?

The purpose of this study is to obtain a more fine-grained understanding of academic vocabulary knowledge in Arabic as L1 among middle school pupils. Accordingly, 1197 middle school Arabic native speaking pupils, representing the different Arab subgroups in Israel, have participated in this study. In the first phase of the study, a corpus based Arabic academic vocabulary list (AAVL) was developed, setting ground for developing three assessment tests that measured receptive vocabulary knowledge on different levels, commencing from mere recognition to application and production of academic vocabulary in context. In the second phase, differences in academic vocabulary knowledge were assessed in relation to age (7th and 9th grades) and Arab sub-group (General Arab, Druze and Bedouin). Significant main effects were found for age and Arab subgroup. Significantly higher performance was noted among 9th graders when compared to 7th graders with the Druze sub group outperforming the general Arab and the Bedouin subgroup. The latter sub-group, characterized by the lowest socio-economic background showed the poorest performance. Additionally, three-way interaction was found between academic vocabulary knowledge level, age and Arab subgroup. In both 7th and 9th grade, significant differences were found between the Arab subgroups on the academic cloze test and production of academic vocabulary only. Post-hoc comparisons showed that in both age groups, the Druze subgroup achieved the highest performance in both tests. Significant differences between the general Arab and Bedouin subgroups were noted on the academic cloze test but not on the production of academic vocabulary test. The results of the study and its implications are discussed.

How suitable are TED talks for academic listening?

To investigate the suitability of TED talks for academic listening in EAP contexts, this research paper compares Academic Vocabulary List (AVL) representation (Gardner & Davies, 2014), lexical density, and speech rate in a TED talk corpus and a lecture discourse corpus, which were both compiled for this study. 28 lecture series (727 lectures total) and 49 TED talks were analysed for AVL representation. TED talks were found to have lower AVL representation than the university lectures (t(75) = 4.95, p < 0.0001). 43 one-minute samples from the Lecture Discourse Corpus and 47 one-minute samples from the TED Talk Corpus were analysed for lexical density, where no differences were found; and speech rate, which was found to be significantly faster in TED talks, in terms of syllables per second (t(98) = 4.23, p < 0.0001) and words per minute (t(98) = 4.20, p < 0.0001). A negative correlation was found between lexical density and syllables per second in the lecture discourse corpus (r = −0.343, p < 0.05), where none was found in the TED talk corpus (r = −0.031, ns), perhaps due to TED talks being a scripted genre. It is concluded that TED talk variation enables a range of academic listening applications.

A response to “To what extent is the Academic Vocabulary List relevant to university student writing?”

In this paper we offer our comments on “To what extent is the Academic Vocabulary List relevant to university student writing?”.

To what extent is the Academic Vocabulary List relevant to university student writing?

This paper investigates the use of Academic Vocabulary List (D. Gardner & Davies, 2014) items in successful university study writing. Overall, levels of use of AVL items are high, and increase as students progress through the years of undergraduate and taught postgraduate study, suggesting that it may be a useful resource. However, significant variation is found across text types and disciplines. While the former is relatively minor, the latter is extensive, suggesting the list is more relevant to some student writers than others. An analysis by items indicates that around half of the words on the list are used very little. Moreover, the items which are frequent differ across disciplines. However, a small core of 427 items was found to be frequent across 90% of disciplines. This suggests that a generic productive academic vocabulary does exist, but that it is smaller in scope than the full Academic Vocabulary List.

A new medical academic word list: A corpus-based study with enhanced methodology

Vocabulary use often varies significantly across academic disciplines. Hence, it is important to develop discipline-specific academic vocabulary lists. So far, all of the existing discipline-specific word lists have been developed using Coxhead’s (2000) method that excluded general high-frequency words. However, Gardner and Davies’s (2014) recent work on a new academic vocabulary list has challenged such a practice because many general high-frequency words have a much higher frequency in academic English than in general English and often have special meanings in academic English. Drawing on and combining methods and procedures from Coxhead (2000) and Gardner and Davies (2014), this corpus study developed a new medical academic vocabulary list, the MAVL. Based on the results of a series of comparative analyses, the MAVL boasts a much better coverage of medical English while being 53% shorter than the existing medical academic word list developed by Wang, Liang, and Ge (2008). Thus, this new list should better serve the needs of medical English learners. This study also provides evidence for the need to include, in discipline-specific vocabulary lists, general high-frequency words that have a significantly higher frequency and special meanings in the discipline than in general English. Pedagogical and research implications are also discussed.

Academic Words in Education Research Articles: A Corpus Study

Since the publication of Coxhead's (2000) Academic Word List, attempts have been made to investigate the importance and relevance of this general academic vocabulary list in a range of different disciplines. The current paper is a corpus-based research to explore the word frequency and text coverage of Coxhead's AWL in research articles of education. To this end, 1.7 million-word corpus was compiled consisting of 239 research articles published by Educational Research Review, International Journal of Educational Development, International Journal of Educational Research, Linguistics and Education, Teaching and Teacher Education during the period of 2002 to 2010.Using WordSmith program, computer analysis showed that the AWL words accounted for 4.94% (84115 of the running words). The qualitative analysis also indicated huge differences in frequency of first two subsets in comparison to the rest (accounting for almost 50% of the whole occurrences). An attempt was also made to identify the most frequent word families of AWL in the Education Research Articles Corpus as well as to obtain a list of non-AWL words (not included in Coxhead's list nor West's General Service List) but still have the criteria to be included in the general academic vocabulary of education. A word to be included in the list must occur at least 50 times in the corpus and appear over 5 times in all journals. This study concluded that attempts must be made to create discipline-specific word lists. Further pedagogical implications are discussed in detail.

A New Academic Vocabulary List

This article presents our new Academic Vocabulary List (AVL), derived from a 120-million-word academic subcorpus of the 425-million-word Corpus of Contemporary American English (COCA; Davies 2012). We first explore reasons why a new academic core list is warranted, and why such a list is still needed in English language education. We also provide a detailed description of the large academic corpus from which the AVL was derived, as well as the robust frequency and dispersion statistics used to identify the AVL. Our concluding case studies show that the AVL discriminates between academic and other materials, and that it covers ?14% of academic materials in both COCA (120 million+ words) and the British National Corpus (33 million+ words). The article concludes with a discussion of how the AVL can be used in settings where academic English is the focus of instruction. In this discussion, we introduce a new web-based interface that can be used to learn AVL words, and to identify and interact with AVL words in any text entered in the search window.

Creating Spoken Academic Vocabulary Lists from the British National Corpus

English has increasingly become a global language of communication, trade and research. For ESL and EFL students, the need for English for academic purposes (EAP) skills — understanding academic textbooks and journal articles and attending or giving presentations — is also growing. Academic vocabulary development is essential in order to achieve a certain proficiency in EAP. Chujo and Utiyama (2004) and Utiyama et al. (2004) have established an easy-to-use tool using nine statistical measures to identify level-specific, domain-specific words (such as EAP) from a corpus. In this study, these measures were applied to a 1.63-million-word spoken educational/informative component of the British National Corpus composed of materials such as lectures, news commentaries and classroom interaction to produce nine word lists. We examined the top 500 most outstanding words of each list and confirmed that specific statistical measures produced level-specific lists of academic words. The selected spoken EAP words are grouped into three proficiency level sub-lists, which allow users to develop spoken EAP vocabulary lists on their own contexts.