Evaluating lists of high-frequency words: Teachers’ and learners’ perspectives
Abstract
I Introduction
1 Background
| Wordlists | Number of itemsa | Corpora | Selection criteria | |||
|---|---|---|---|---|---|---|
| Word types | Lemmas | Flemmas | Word families | |||
| West’s (1953) GSL | 13,451 | n/ab | n/ac | 2,168 | 5 million, 100% written | frequency, ease of learning, necessity, cover, stylistic level, and emotional neutrality |
| Nation’s (2006) BNC2000 | 13,197 | n/ab | n/ac | 1,996 | 100-million, 90% written, 10% spoken | frequency, range, dispersion, subjective judgment |
| Nation’s (2012) BNC/COCA2000 | 13,199 | n/ab | n/ac | 2,000 | 10-million, 40% written, 60% spoken | frequency, range, dispersion, subjective judgment |
| Browne’s (2014) NGSL | 8,205 | n/ab | 2,818 | n/ad | 274-million, 75.03% written, 24.97% spoken | frequency, dispersion, subjective judgment |
| Brezina and Gablasova’s (2015) New General Service List | 4,849 | 2,228 | n/ac | n/ad | 12-billion, 97.5% written, 2.5% spoken | frequency, dispersion, and distribution across language corpora. |
2 Which high-frequency word list provides the greatest lexical coverage?
3 Learner vocabulary knowledge
4 Teacher perceptions of word usefulness
II The present study
III Methodology
1 Participants
a Teachers
| ESL/EFL teachers who were native speakers of English | (n = 25) | Vietnamese EFL teachers | (n = 26) | EFL teachers from varying countries | (n = 27) |
|---|---|---|---|---|---|
| New-Zealander | 13 | Vietnamese | 26 | Indonesian | 6 |
| American | 4 | Malaysian | 6 | ||
| British | 3 | Iranian | 2 | ||
| Canadian | 3 | Japanese | 2 | ||
| Australian | 2 | Taiwanese | 2 | ||
| Thai | 2 | ||||
| Chinese | 1 | ||||
| Greek | 1 | ||||
| Jordanian | 1 | ||||
| Kenyan | 1 | ||||
| Laotian | 1 | ||||
| Sri Lankan | 1 | ||||
| Venezuelan | 1 | ||||
| Years of teaching experience | 2–40 years (M = 13.12, SD = 9.35) | Years of teaching experience | 2–22 years (M = 6.88, SD = 5.29) | Years of teaching experience | 2–20 years (M = 8.63, SD = 4.64) |
b Learners
| Academic majors | n |
|---|---|
| TESOL | 86 |
| Computer Sciences & Technology | 31 |
| Natural Sciences | 13 |
| Economics & Business | 2 |
| Law | 2 |
| Social Sciences & Humanities | 1 |
| Total | 135 |
| Group of learners (vocabulary level) | Number of learners | VLT score |
|---|---|---|
| Pre-intermediate | 37 | Scored from 50–80% at the 2,000-word level |
| Intermediate | 50 | Mastered the 2,000-word level |
| Advanced | 48 | Mastered at least the 3,000-word level |
2 Target words
a Teacher Likert surveys of target words

b Learner yes/no tests of the target words

3 Analysing the teacher data
4 Analysing the learner data
| Checked pseudowords cut-off points | Number of learners in each group | |||
|---|---|---|---|---|
| Pre-intermediate | Intermediate | Advanced | Total | |
| 0% | 0 | 0 | 0 | 0 |
| 1% | 12 | 6 | 7 | 25 |
| 5% | 25 | 28 | 35 | 88 |
| 10% | 32 | 40 | 40 | 112 |
| Original data | 37 | 50 | 48 | 135 |
IV Results
| The whole sets of words | The most frequent 428 words from each set | |||||
|---|---|---|---|---|---|---|
| BNC/COCA 2000 | New-GSL | Difference in the percentage | BNC/COCA 2000 | New-GSL | Difference in the percentage | |
| Mean score of 4 or above | 72.68 | 27.32 | 45.36 | 73.22 | 26.78 | 46.44 |
| Top 100 | 79 | 21.0 | 58.0 | 80.0 | 20.0 | 60.0 |
| Top 200 | 71.5 | 28.5 | 43.0 | 73.0 | 27.0 | 46.0 |
| Top 300 | 66.0 | 34.0 | 32.0 | 67.0 | 33.0 | 34.0 |
| Top 400 | 61.5 | 38.5 | 23.0 | 63.25 | 36.75 | 26.5 |
| Top 500 | 60.4 | 39.6 | 20.8 | 61.0 | 39.0 | 22.0 |
| Sets of words used in the comparison | All learners (n = 112) | Pre-intermediate (n = 32) | Intermediate (n = 40) | Advanced (n = 40) | ||||
|---|---|---|---|---|---|---|---|---|
| BNC/COCA 2000 | New GSL | BNC/COCA 2000 | New GSL | BNC/COCA 2000 | New GSL | BNC/COCA 2000 | New GSL | |
| Full sets of words | 61.95 | 38.05 | 67.36 | 32.64 | 56.40 | 43.60 | 52.94 | 47.06 |
| most frequent 428 items from each set | 58.46 | 41.54 | 65.44 | 34.56 | 52.82 | 47.18 | 51.27 | 51.27 |
V Discussion
VI Pedagogical implications
VII Conclusions
Funding
ORCID iDs
Footnotes
References
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