The Investigation of Vocabulary Input in High School EFL Textbooks and
University Admission Tests
Navinda Sujinpram1*, Asst. Prof. Dr. Natawan Senchantichai2#, Dr. Kornwipa Poonpon3#
English Program, Khon Kaen Univesity, Khon Kaen
*navindy@hotmail.com, #nansen42@gmail.com, #korpul@kku.ac.th
Abstract
The relationship between vocabulary and language ability has gained theoretically and
empirically accepted. This is especially true for reading comprehension which the strong
relationship is found. As such, there is a possibility that knowledge of vocabulary can predict
the ability to use the language, in particular to gain adequate comprehension in reading any
material. This study set out to investigate vocabulary input in high school EFL textbooks and
English tests for university admission, and to find out if there is any difference between them.
Three research questions were addressed as follows: 1) what is the vocabulary input in high
school EFL textbooks? 2) what is the vocabulary input in the English tests for university
admission? and 3) is there difference of vocabulary input between them? To answer the
questions, a corpus-based investigation was conducted. The British National Corpus (BNC)
was selected as a representative corpus, and run on the VocabProfile BNC-20, an online word
profiling program. Two textbook series and three high-steak English tests for university
admission were compiled as new corpora. The criterion for the analysis was set at 95 percent
text coverage. The results showed that one textbook contained 3,000 words inclusive of
proper nouns, and the other contained 4,000 words inclusive of proper nouns as resources for
reading any texts. One of the two English tests needed 3,000 words inclusive of proper nouns
for gaining adequate comprehension, and the other two tests needed 4,000 words inclusive of
proper nouns to do so. It was apparent that a textbook with 3,000 words failed to contain
adequate vocabulary input for gaining adequate comprehension in two of the three English
tests. So, some additional materials to increase vocabulary knowledge are potentially useful
to bridge the gap between them.
Keywords: O-NET, GAT, KKU, BNC, VocabProfile BNC-20
Introduction
Due to the approaching of Association of Southeast Asian Nation [ASEAN]
integration, the role of the English language for successful regional communication is
increasing. However, the average Thai students seem far away from being proficient users of
the language20. The National Institute of Educational Testing Service [NIETS] 12-14 reveals
that the mean English test scores of Thai students in a standard-based achievement test were
rather low. So were the scores from a general aptitude test which aims to assess students’
ability to communicate in English. This is especially true for Grade 12 students who have
studied the language at least 10 years before entering university-level education. Supposing
that the tests are well specifically designed for students in a particular education level,
students’ low scores from those tests assume students’ limited ability to use the language.
Although students’ low scores are affected by a number of factors, knowledge of
vocabulary is potentially a critical one since its relationship to language ability has been
found. Many researchers3,8,10,19 claimed that a learner with extensive vocabulary knowledge
tends to be better at performing the four language skills of listening, speaking, reading and
writing while another with limited vocabulary knowledge tends to encounters language
difficulties. Previous studies3,4,6,7,12 used corpus-based investigation to measure the number of
words needed to read varieties of materials, and certain percentage of text coverage (the total
number of running words from each list multiplied by 100 and divided by the total number of
running words in each corpus18 p.54) was set as a criterion for adequate comprehension to take
place. Laufer8 and Laufer and Ravenhorst-Kalovski9 claimed that 95 percent text coverage is
minimally needed for gaining adequate comprehension in reading any material. Nation10 also
supported that a reader with 95 percent text coverage potentially succeeds in guessing
meaning of unknown words from context. However, percentage of text coverage increases to
98 percent if a reader needs reading for pleasure11,12. An example of corpus-based studies was
conducted in English test of Indonesia’s National Exams by using the British National
Corpus (BNC) as a representative corpus. Adequate comprehension was set at 95 percent text
coverage and the number of words needed to read the test was estimated accordingly.
Another example, Chujo and Oghigian4 used the British National Corpus High Frequency
Word List (BNC HFWL), the Standard Vocabulary List 12000 (SVL) and the Nation’s 14K
to measure the number of words needed to gain comprehension in the Test of English for
International Communication (TOEIC), the Test of English as a Foreign Language (TOEFL)
and the English tests for Japanese learners of English. In the study, the criterion for adequate
comprehension was also set at 95 percent text coverage. Hsu7 used the Nation’s 14K to
measure the number of words needed to read business English textbooks and articles. The
criteria were set at 95 percent for minimal requirement of adequate comprehension and 98
percent for optimal one. In Chujo’s study3, the corpus-based approach was used to estimate
the number of words provided from EFL textbooks and the number of words needed to gain
adequate comprehension in university admission tests. The comparison of vocabulary input
between the materials was made and the results showed the big difference of vocabulary
input that could hinder students’ ability to gain comprehension in the target tests. The study
of Chujo3 provided crucial implication for EFL learning, teaching and testing in particular.
This is due to the fact that EFL learners’ vocabulary knowledge primarily depends on
textbooks they use at school2. According to the above mentioned, there is a possibility that
the students’ low scores from the standardized English achievement and the general aptitude
tests are affected from students’ limited knowledge of vocabulary that they are given from
their textbooks.
In order to find out if students are provided adequate vocabulary knowledge for
gaining adequate comprehension in the target tests, the investigation of vocabulary input in
students’ textbooks and that in the target tests was conducted. The authentic data from the
investigation was analyzed for the difference of vocabulary input between those mentioned
sources. The results of the study showed not only the adequacy of vocabulary input provided
by the target textbooks, but also the number of words needed to gain comprehension in the
target tests. Students, teachers and test designers are those who are fully given benefits from
this study. That is, students and teachers may set vocabulary learning goal according to the
estimated number of words needed to gain adequate comprehension in the target tests.
Teachers may also prepare some additional materials to bridge a vocabulary gap between the
textbooks and the tests if any. Test designers may use the information to get into their
consideration of vocabulary selection for test designing, particularly if vocabulary input in
the textbooks and that in the tests are not comparable.
Methodology
The materials used in this study were six high school EFL textbooks categorized in
two series (the first series includes 3 “Upstream” textbooks for Grade 10 to Grade 12 and the
second series includes 3 “Mega Goal” textbooks for Grade 10 to Grade 12), two English test
papers of the Ordinary National Educational Test (O-NET) for the academic year 2010 and
2011, six English test papers of the General Aptitude Test (GAT) for the academic year 2010
and 2011 and three English test papers of Khon Kaen University (KKU) quota admission for
the academic year 2010, 2011 and 2012.
Each of the Upstream textbook series comprises of 5 modules presented in 10 units
with different topics. Four language skills of listening, speaking, reading and writing are
included in each single unit, together with vocabulary and grammar focuses.
Each of the Mega Goal textbook series comprises of 12 units with different topics.
The four language skills and grammatical knowledge are also included in each single unit.
Vocabulary is presented in glossary at the end of the textbooks.
The English test papers of O-NET 2010 and O-NET 2011 for Grade 12 students are
standard-based achievement test17. They are multiple-choice tests consisting of 70 questions
each. The total scores are 100 marks. Each paper covers two sections aiming to test (1)
speaking and writing ability and (2) reading comprehension. The first section includes 10
questions of conversations and 20 questions of sentence completion and error detection with
2 marks each. The second section includes a cloze test with 10 blanks and 30 questions of
reading comprehension with 1 mark each.
The English test papers of the GAT 2010 and GAT 2011 aim to test students’ ability
to communicate in English13. They are multiple-choice tests consisting of 60 questions. Each
paper covers 4 sections of Speaking, Vocabulary, Reading and Structure and Writing. The
total scores are 150 marks. The Speaking section covers 15 questions of conversations with
2.5 marks each. The Vocabulary section covers 12 questions of vocabulary meaning out of
context tests with 1.5 marks each, vocabulary meaning recognition with 2.5 marks each and
vocabulary meaning in context with 3.5 marks each. The Reading section covers 18 questions
of reading comprehension with 2.5 marks each. The Structure and Writing section covers 15
questions of error detection, sentence completion and passage completion with 2.5 marks
each.
The English test papers of the KKU quota admission for 2010 to 2012 aim to select
students with excellent academic performance to study in the university. The papers are
multiple-choice test consisting of 100 questions. The total scores are 100 marks. Each paper
covers 5 sections. The first section includes 30 questions of reading comprehension, the
second section includes 20 questions of error detection, the third section includes 20
questions of vocabulary, the fourth section includes 20 questions of conversation and the last
section includes 10 questions of cloze test. Each question accounts for 1 mark.
The above mentioned sources were compiled as new corpora. For textbooks
compilation, the content of each textbook series was typed in Microsoft Word 2007 and
saved as Microsoft Word document (.docx) files. Textbook covers, prefaces, introductions,
table of contents, references, directions, glossary and other irrelevances (e.g., literature
corner, lyrics and grammar reference) were excluded from these document files. Also, nonlexical items (cardinal and ordinal numbers, interjections, unclassified items such as www,
umm, alphabetical symbols, units, abbreviations) were manually deleted. This is because the
percentage of non-lexical items has a tendency to interfere the results of the analysis provided
that they are included in the target text files. Then, the newly-compiled textbook corpora
were proofread and revised before transcribed into plain text files (.txt).
The test corpora compilation was rather similar to that of the textbook corpora, but it
is, in fact, a lot easier and faster. This is due to the fact that all of the target tests were in
portable data format (.pdf) files, so it made sense to use the Optical Character Recognition
(OCR) to directly transcribe the target tests from portable data format files to plain text files.
Test covers, directions, other irrelevances together with non-lexical items were manually
deleted from the plain text files. Then the new test corpora were proofread and revised. It is
important to note that the OCR program was practical for transcribing the tests but not the
textbooks. This is because the textbooks are full of graphics that possibly interfere the
transcribing process of the OCR program, and consequently produce a number of errors. On
the other hands, the tests do not contain a number of graphics that can interfere the OCR
transcribing process.
A representative corpus used in the study was the British National Corpus (BNC)
which is one of the largest and most updated corpora comprising of more than 100 million
spoken and written British English words during the late twentieth century. The BNC was run
in the VocabProfile BNC-20, an online word profiling program available for free at
http://www.lextutor.ca/vp/bnc. The VocabProfile BNC-20 developed by Tom Cobb5 contains
20 1000-word lists of the BNC ranged according to frequency order. Although it is practical
instrument for investigating vocabulary input in the present study, it has limitations. First, the
program cannot count multi-word units. If multi-word units are input, they are separately
counted as a single unit. Second, the program cannot distinguish homographs. Third, words
related to computer and technology are not in the lists although the BNC is an up-to-date
corpus. Even those words are in the lists, they appear in the late lists of words with low
frequency.
Each of the new corpora was uploaded into the VocabProfile BNC-20 website. The
option for automatically recategorizing proper nouns was selected. Dealing with proper nouns
is an important issue for investigating vocabulary input in a target text, particularly if the text
contains a number of proper nouns. This is due to the fact that all proper nouns are easily
recognizable from their capital letter at the beginning of word within a sentence, and a reader
can know those proper nouns at the first time of encountering them. Therefore, the
categorization of proper nouns can greatly affect the result of any study. Specifically, a text
seems rather more difficult than it actually is provided that proper nouns are categorized as
off-list words (not included in any of the 20 1000-word lists). It is worth noting that the
VocabProfile BNC-20 recategorizes only off-list proper nouns as the first 1000-word list, but
on-list proper nouns are categorized according to their frequency lists.
The data from each particular corpus showed the total tokens, types, families, text
coverage of each frequency list and cumulative text coverage. The 95 percent coverage of
text was set as criterion for this investigation since it is the minimally needed for a reader to
gain adequate comprehension in a text as claimed by Laufer7 and Laufer and RavenhorstKalovski8. The researcher counted percentage of coverage from the first 1000-word list to the
subsequent lists until the cumulative percentage of coverage approached the predetermined
text coverage.
Results
Vocabulary input in the textbook corpora
Table 1 shows total types and tokens of each textbook corpus. The Mega Goal corpus
contained the larger number of tokens and types than the Upstream corpus. Such larger
number informed us that the Mega Goal required larger extensive reading and broader
knowledge of different word forms compared to the Upstream.
Table 1. Total types and tokens of each textbook corpus.
Textbook Corpus
Upstream
Mega Goal
Types
7,881
8,190
Tokens
83,909
86,727
Types, tokens and BNC coverage of the first 4 1000-word lists in the Upstream
corpus were as shown in Table 2. The corpus made up 95.34 percent text coverage at the third
1000-word list. This means that around 3,000 words inclusive of proper nouns were provided
as resources for students using this textbook series.
Table 2. Types, tokens and BNC coverage in the Upstream corpus.
1000-Word Frequency List
1st
2nd
3rd
4th
Types
3,059
1,590
865
507
Tokens (%)
70,370 (83.86)
7,087 (8.45)
2,544 (3.03)
1,193(1.42)
Cumulative Coverage
83.86%
92.31%
95.34%
96.76%
Types, tokens and BNC coverage of the first 4 1000-word lists in the Mega Goal
corpus were as shown in Table 3. The corpus made up 96.34 percent text coverage at the
fourth 1000-word list. This means that around 4,000 words inclusive of proper nouns were
provided as resources for students using this textbook series.
Table 3. Types, tokens and BNC coverage in the Mega Goal corpus.
1000-Word Frequency List
1st
2nd
3rd
4th
Types
3,220
1,573
866
526
Tokens (%)
73,180 (84.38)
6,476 (7.47)
2,462 (2.84)
1,435 (1.65)
Cumulative Coverage
84.38%
91.85%
94.69%
96.34%
Vocabulary input in the English test corpora
Table 4 shows the average types and tokens of each English test corpus. The O-NET
and the KKU corpora contained rather similar number of words (3,658 and 3,609) which was
larger than that of the GAT (2,969). This indicated that the O-NET and the KKU quota
admission test needed more extensive reading than the GAT. The KKU corpus contained the
most types among all of the test corpora while the O-NET corpus contained the least. So,
broad knowledge of different word forms proved the most advantageous to read the KKU
quota admission test.
Table 4. Average types and tokens of each English test corpus.
Test Corpus
O-NET (2010-2011)
GAT (2010-2011)
KKU (2010-2012)
Types
979
1,081
1,160
Tokens
3,658
2,969
3,609
The average percentage of BNC coverage in the O-NET corpus was as shown in
Table 5. The corpus made up 95.17 percent text coverage at the third 1000-word list. This
means that the around 3,000 words inclusive of proper nouns are needed for gaining adequate
comprehension in the O-NET.
Table 5. Average BNC coverage in the O-NET corpus.
1000-Word Frequency List
1st
2nd
3rd
4th
Coverage
83.81%
8.54%
2.82%
1.53%
Cumulative Coverage
83.31%
92.35%
95.17%
96.70%
The average percentage of BNC coverage in the GAT corpus was as shown in Table
6. The corpus made up 95.50 percent text coverage at the fourth 1000-word list. This means
that around 4,000 words inclusive of proper nouns are needed for gaining adequate
comprehension in the GAT.
Table 6. Average BNC coverage in the GAT corpus.
1000-Word Frequency List
1st
2nd
3rd
4th
Coverage
80.88%
9.05%
3.61%
1.96%
Cumulative Coverage
80.88%
89.93%
93.54%
95.50%
The average percentage of BNC coverage in the KKU corpus was as shown in Table
7. The corpus made 95.57 percent text coverage at the fourth 1000-word list. The means that
the around 4,000 words inclusive of proper nouns are needed for gaining adequate
comprehension in the KKU quota admission test.
Table 7. Average BNC coverage in the KKU corpus.
1000-Word Frequency List
1st
2nd
3rd
4th
Coverage
81.16%
8.73%
3.51%
2.17%
Cumulative Coverage
81.16%
89.89%
93.40%
95.57%
Differences between vocabulary input in the textbook corpora and the test corpora
Table 8 shows the percentage of BNC coverage provided by each textbook and test
corpus. The Mega Goal corpus provided the highest percentage text coverage at the first
1000-word list, but the GAT provided the lowest. In fact, 3.5 percent text coverage was found
different between the two corpora at the first 1000-word list. However, the GAT provided the
highest text coverage at the second 1000-word list while the Mega Goal corpus provided the
lowest. Still, cumulative percentage of text coverage from the Mega Goal corpus was higher
than the GAT corpus at the first list. Concerning the distribution of text coverage over the
frequency lists, it was found that the two textbook corpora had rather similar pattern. Text
coverage of the O-NET corpus also exhibited similar pattern to the two textbook series but
different from the other two test corpora. For the GAT and the KKU corpora, the distribution
of text coverage over each frequency list was rather the same.
The Upstream and the O-NET corpora approached 95 percent text coverage at the
same frequency list (the third 1000-word list) which can be assumed that there was no
difference of vocabulary input between the Upstream textbook series and the O-NET. The
Mega Goal, the GAT and the KKU corpora approached 95 percent text coverage at the fourth
1000-word list which can be assumed that there was no difference of vocabulary input
between the Mega Goal textbook series and the two tests (GAT and the KKU quota
admission test). However, the difference of vocabulary input between Upstream textbook
series and the GAT and the KKU quota admission test was evident. That is, 1000 words more
than those provided from the Upstream textbook series were necessary to gain adequate
comprehension in reading the GAT and the KKU quota admission test.
Table 8. Percentage of BNC coverage in each textbook and test corpus.
Upstream
Mega Goal
O-NET
GAT
KKU
1st 1000-Word
83.86 (83.86)
84.38 (84.38)
83.81 (83.86)
80.88 (80.81)
81.16 (81.16)
2nd 1000-Word
8.44 (92.30)
7.47 (91.85)
8.54 (92.35)
9.05 (89.93)
8.73 (89.89)
3rd 1000-Word
3.03 (95.33)
2.84 (94.69)
2.82 (95.17)
3.61 (93.54)
3.51 (93.40)
4th 1000-Word
1.42 (96.75)
1.65 (96.34)
1.53 (96.70)
1.96 (95.50)
2.17 (95.57)
Discussion and Conclusion
The investigation reveals the apparent discrepancy in number of running words
(token), number of word forms (type) and text coverage between the two textbook series
along with the tests. Regarding to the textbooks, the Mega Goal series consists of higher
number of running words and different word forms than the Upstream series. Such higher
number indicates some key issues concerning extensive reading and different word form
recognition. In other words, the results demonstrate the requirement for larger extensive
reading and broader knowledge of different word forms of the Mega Goal series compared to
the Upstream series. Text coverage analysis allows us to estimate the number of words
provided as resources by the textbooks that the Upstream series provides 3,000 words and the
Mega Goal series provides 4,000 words.
With reference to the tests, it was found the comparable numbers of running words in
the O-NET and the KKU quota admission test which are obviously higher than that in the
GAT. This basically means that the O-NET and the KKU quota admission test need larger
extensive reading than the GAT. However, the results clearly reveal that the GAT contains
higher number of different word forms than the O-NET and similar number to the KKU
quota admission test although its total running words is the lowest. This proves the usefulness
of different word form recognition to read the GAT and the KKU quota admission test, and
confirms the higher repetition rate of words in the O-NET. As text coverage analysis reveals,
to gain adequate comprehension in the tests, around 3,000 words are needed for the O-NET
and 4,000 words are needed for the GAT and the KKU quota admission test. Although this
clearly points that the GAT and the KKU quota admission test need 1,000 words more than
the O-NET, it does not ascertain anything beyond the difficulty of vocabulary input in the
tests. In fact, the O-NET though needs lesser words to gain comprehension need larger
extensive reading.
The investigation strongly indicates the adequacy of vocabulary input in the two
textbook series for gaining adequate comprehension in the O-NET. Such adequacy raises the
possibility for students to gain comprehension in reading the O-NET, and potentially get high
scores from the test. Moreover, it supports the fact that in term of vocabulary input, the ONET does not contain vocabulary beyond that in students’ textbooks. However, it was
evident that the Upstream series fails to provide students with adequate vocabulary for
gaining comprehension in the GAT and the KKU quota admission tests. Students using the
Upstream series need some additional materials to increase their vocabulary knowledge at
least 1,000 words more to gain adequate comprehension in the tests. Referring to the Mega
Goal series, there is a reasonable chance of students using the series to gain adequate
comprehension in all of the tests due to the adequacy of vocabulary input provided by the
series.
The distribution of text coverage over the BNC frequency lists provided by the
materials used in this present study can be compared with Nation’s12 range of average BNC
text coverage gathered from authentic spoken and written samples. As Nation12 described,
spoken texts cover around 81-84 percent at the first 1000-word list, 5-6 percent at the second
1000-word list, 2-3 percent at the third 1000-word list and 1.5-3 percent at the fourth and fifth
1000-word lists together. Written texts cover around 78-81 percent at the first 1000-word list,
8-9 percent at the second 1000-word list, 3-5 percent at the third list and 3 percent at the
fourth and fifth 1000-word lists together. Compared Nation’s range of text coverage to the
text coverage from the materials in the present study, it was found that text coverage from the
two textbook series and the O-NET falls into similar range of coverage obtained from spoken
texts except at the second 1000-word list which higher text coverage from textbooks and the
O-NET was found. This can be assumed that the content of the materials focuses on
communicative English. On the other hand, coverage from the GAT and the KKU admission
test fall into similar range of coverage obtained from written texts. This also allows us to
assume that the content of the GAT and the KKU quota admission test focuses on reading
skill.
The vocabulary input in the Indonesia’s National Exam (NE) was investigated in
Aziez’s study1. The test is probably a counterpart with the O-NET in Thailand because both
are national tests for high school students. The results showed that 4,000 words are needed to
gain adequate comprehension in reading the test. In other words, around 1,000 words more
than the O-NET are necessary to read the Indonesia’s NE. However, the comparison of
vocabulary input regarding to the number of running words and different word forms reveals
that the O-NET needs larger extensive reading comparing to the Indonesia NE. This again
allows us to assumed that the O-NET though needs lesser number of words to gain
comprehension is not easier than the Indonesia NE due to larger extensive reading it requires.
Chujo3 conducted a study to estimate the number of words provided in high school
EFL textbooks and those needed for adequate comprehension in university admission tests in
Japan. She found that textbooks provided 3,000 words and 3,200 words as resources while
three national universities need 3,500 words, 4,800 words and 6,300 words to gain
comprehension. Obviously, most of the tests in Chujo’s study needed larger number of words
than the textbooks, and this consequently causes big difference of vocabulary input between
the textbooks and the tests in her study. In the present study, although the difference of
vocabulary input between the Upstream series and the two tests (GAT and KKU quota
admission test) were found, that difference are not as large as those found in Chujo’s study.
In fact, it seems like the O-NET, the GAT and the KKU quota admission test were
constructed with better consideration of vocabulary selection because the vocabulary input in
the tests was rather comparable to the textbooks used for classroom instruction.
In conclusion, the investigation confirms the high possibility that students using either
of the textbook series without additional materials gain adequate comprehension in the ONET, and consequently tend to get high scores. However, students’ scores in the past years
were contrary to this. If we assume that the tests are standardized in terms of vocabulary
input, other factors contributing to such low scores should be investigated. Particularly,
students’ vocabulary size should be measured to find out whether students possess as large
vocabulary knowledge as provided by the target textbooks. Additionally, the fact that a
textbook series fails to provide students with adequate vocabulary input for gaining adequate
comprehension in the GAT and the KKU quota admission test, teachers should give students
additional materials to increase students’ knowledge of vocabulary as needed to comprehend
the GAT and the KKU quota admission test. The results also address test designers that
regarding to vocabulary input, the O-NET does not contain vocabulary input beyond what is
input in the textbooks. It is also obvious that the all of the university admission tests used in
the present study are constructed with better consideration on vocabulary selection compared
to two of the three Japan national university admission tests in Chujo’s study3.
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