T1 112012018 Full text

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VOCABULARY PROFILE OF ACADEMIC LISTENING MATERIALS USED IN THE ENGLISH LANGUAGE EDUCATION PROGRAM

THESIS

Submitted in Partial Fulfilment of the Requirements for the Degree of

Sarjana Pendidikan

Joannita Ratih Wiharmastu 112012018

ENGLISH LANGUAGE EDUCATION PROGRAM FACULTY OF LANGUAGE AND LITERATURE

SATYA WACANA CHRISTIAN UNIVERSITY 2016

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vi TABLE OF CONTENT

COVER ...

INSIDE COVER PAGE ... i

APPROVAL PAGE ... ii

COPYRIGHT STATEMENT ... iii

PUBLICATION AGREEMENT DECLARATION ... iv

TABLE OF CONTENT ... v

VOCABULARY PROFILE OF ACADEMIC LISTENING MATERIALS USED IN THE ENGLISH LANGUAGE EDUCATION PROGRAM ... 1

ABSTRACT ... 1

A. INTRODUCTION ... 1

B. LITERATURE REVIEW ... 3

1. Definition of Vocabulary ... 3

2. Importance of Learning Vocabulary ... 4

3. Lexical Coverage for Comprehension ... 4

4. Definition of Vocabulary Profile and Vocabulary Profiler ... 5

5. Categories Based on The Word Frequency ... 5

6. Benefits of Knowing Low and High Frequency Words ... 7

7. Relevant Previous Studies ... 8

C. THE STUDY ... 9

1. Context of The Study ... 9

2. Material ... 10

3. Data Collection Instrument ... 10

4. Data Collection Procedure ... 10

5. Data Analysis ... 11

D. FINDING AND DISCUSSION ... 11

1. Overall Result ... 12

2. Negative Vocabulary Profiles of the Academic Listening Course Material ... 13

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a. Negative Vocabulary Profile of K1 ... 14

b. Negative Vocabulary Profile of K2 ... 14

c. Negative Vocabulary Profile of AWL ... 15

3. Block Frequency Output of Off-list Words ... 16

4. Comparison of Vocabulary Frequency across Materials Used in Each Meeting ... 17

5. Text Comparison of Materials ... 20

a. Comparison of Materials for Meeting 2 vs. Meeting 11... 20

b. Comparison of Materials for Meeting 8 vs. Meeting 16 ... 22

c. Comparison of Materials for Meeting 10 vs. Meeting 17... 23

E. CONCLUSION ... 24

ACKNOWLEDGEMENT ... 26

REFERENCES ... 27

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VOCABULARY PROFILE OF ACADEMIC LISTENING MATERIALS USED IN THE ENGLISH LANGUAGE EDUCATION PROGRAM

Joannita Ratih Wiharmastu 112012018

A. INTRODUCTION

Knowing the content of the materials which are used in a language class is very crucial because the students can expect what they are going to learn. The most visible content that can be analysed in a language lesson material is the grammar and vocabulary. Discoursing vocabulary learning, getting exposed to as many vocabulary items as possible in the target language would be beneficial because it is the “basic building block of language” (Read, 2000, p.1). As the result, it becomes the call for material developers to create course books that would be able to maximize the input and carefully choose what vocabulary the learners need to know (Kafipour & Naveh, 2011; Lessard-Clouston, 2013, as cited in Farjami, 2014).

Because of the importance of vocabulary learning, the teachers need to know the vocabulary levels that are appropriate for the students. As what Krashen (1982) presents about the input hypothesis, the second language acquisition can only take place if the

language used is above the students’ level, but not too advanced. Therefore, the educators

should adjust the difficulty of the vocabulary items based on their students’ level. To see the most used and uncommon words in a book, vocabulary profiler can be utilized.

On account of the implementation of KKNI (Indonesian National Qualification Framework) in 2012, the number of credits for Academic Listening course for English Language Education program students in Satya Wacana Christian University starting from

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2014 was converted from three to four credit hours. This led to changing the content of the materials. The Academic Listening course in English Language Education program used a material that was compiled from TOEFL, IELTS, and book chapters. Since these materials have just been used in 2014, there is no research investigating the vocabulary items of it. These newly generated materials need to be investigated in its early practice and hence, immediate action could be carried out if needed.

In response to the change of the material, this study attempted to provide the vocabulary profile of the newest Academic Listening course materials. Vocabulary profile is a description of breakdowns that includes the ratio of how many times a word occurs in a text (Morris & Cobb, 2004). Farjami (2014) mentioned that studies on vocabulary profile were able to give information about the frequency of function and content words. The information about the vocabulary profile will be broken down into four parts which are the first thousand most common words (K1), the second thousand most common words (K2), Academic Word List (AWL), and Off-list words (Cobb, n.d.).

In this study, an investigation was completed to profile the vocabulary in the Academic Listening class materials. This study was aimed to answer three research questions that were:

1. What is the vocabulary profile used in the Academic Listening course material? 2. What is the number of vocabulary items that was not covered in the Academic

Listening course material?

3. What is the token recycling index of the Academic Listening course material? The objectives of this study were:

1. To investigate the vocabulary profile of the Academic Listening course materials used in English Education Program in Satya Wacana Christian University.

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3. To generate the token recycling index of the material.

From the result of this vocabulary profile study, the material developers can have the insight whether the vocabulary coverage can be taught and understandable for a particular

students’ level (Laufer, 2010). The researcher hopes that this study could give beneficial inputs for the language teaching practitioners in this university. After knowing the degree of vocabulary occurrence in the new class, the material developers may be able to design their teaching by deciding which vocabulary is worth highlighting and being remedied. In addition, they could also see whether the vocabulary items were appropriate for the students or not.

B. LITERATURE REVIEW

1. Definition of Vocabulary

The scientific study of language can cover some areas namely syntax, morphology, phonology, and lexicon. The last mentioned area, commonly known as vocabulary, is defined

by Richards and Schmidt as “a set of lexemes (the smallest unit in the meaning system of a

language that can be distinguished from other similar units), including single words,

compound words and idioms” (2002, as cited in Hassani, Zarei, & Sadeghpour, 2013).

2. Importance of Learning Vocabulary

According to Zimmerman (1997), vocabulary proficiency is more substantial for the language learners compared to the knowledge of syntactic rules. This statement is supported by Koosha and Akbari (2010) claiming that while utterances containing grammatical errors would be somehow understandable, incorrect dictions would be a real obstacle in conversations. Subsequently, vocabulary becomes an essential component in language learning because it is the building block of the things trying to be conveyed (Schmitt,

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Schmitt, & Calpham, 2001; Read, 2000; Mayer & Alexander, 2010). In addition, it is suggested that various types of vocabulary tests could be used to predict one’s language proficiency in a second or foreign language (Stæhr, 2008; Zavera in Golkar & Yamini, 2007).

3. Lexical Coverage for Comprehension

In order to gain comprehension, only one word in every 20 words within the overall text is allowed to be left unknown by the reader. In other words, the vocabulary coverage should reach 95% to achieve understanding. However, most learners will make an adequate comprehension when only one word in every 50 words is unknown or 98% coverage (Nation, 2006). Another theory proposed by Betts (as cited in Mikulecky, 2007) was written as follows:

Independent: 99% of words already known for fluent, enjoyable reading.

Instructional: 98%-95% of words known and some instructional support such as teacher suggestions, vocabulary explanations, illustrations etc. needed for the benefit.

Frustrational: Below 95% of words known can damage fluency and lead to disruptions in comprehension strategies. (p.1)

Furthermore, Nation (2006) drew a conclusion based on his research that the variation of the word used in a written text would be less varied than in a spoken text. The percentage of K1 plus K2, according to his study, would normally cover around 78%-81%, while in spoken text the coverage would be around 85%. In the same study, he also assumed that as much as 95% of the text coverage also generally included K1, K2, and Off-list because the proper nouns from this category could be easily understood.

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4. Definition of Vocabulary Profile and Vocabulary Profiler

Vocabulary profile, according to Graves (2005), is a collection of words classified by their tokens, families, and frequencies. Browne and Culligan (2008) assert that the integration between a work under CALL (Computer Assisted Language Learning) and vocabulary teaching leads to the emerging of a vocabulary profiler program. Therefore, a vocabulary

profiler is an online “public domain computer program” (Laufer, 1994) developed by Tom

Cobb which can group the vocabulary in a text into frequency levels (Paul, 2005; Nemati, 2009).

5. Categories Based on The Word Frequency

There are four types of word frequency, respectively: high frequency which is divided into the first thousand most common words (K1) and the second thousand most common words (K2); Academic Word Lists (AWL); and Off-list words (OL) (Nation, 1990). The first

3000 word families of English “represent the current best estimate of the basic learner lexicon of English” (Schmitt & Schmitt, 2012). Below is the example of thirty words under the

category of K1 taken from Rebecca Sitton’s Spelling Sourcebook Series (Sitton, 1992):

Table 1. Examples of the first thousand most common words (K1) (Sitton, 1992)

The A Is It From Or Had Their Do About

Of To You Be I By Not Said Will How

And In That This Have One Which If Each Up

The vocabulary profiler on http://www.lextutor.ca/ (Cobb, n.d.) provides the list of K1, K2, and Academic Words Lists. The K1 is divided into two categories which are function and content words. Function words are the grammatical items to organise a language, while

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content words (nouns, verbs, adjectives, and adverbs) define the lexical density in a text (Vidiakovic & Barker, 2010). The AWL is split up into two parts which are university word lists and academic word lists. However, this study grouped all the academic words into AWL. On the web, the list is presented in an alphabetical order. Here is the example of the K2 word lists from the online Compleat Lexical Tutor. Only one word which is the first appearance for every letter in the alphabet is taken.

Table 2. Examples of the second thousand most common words (K2) (Cobb, n.d.)

Abroad Fade Key Pack Ugly

Baby Gap Ladder Qualify Vain

Cage Habit Mad Rabbit Waist

Damage Ice Nail Sacred Yard

Eager Jaw Oar Tail Zero

Furthermore, the AWL developed by Coxhead (2000) cointains 570 word families excluding the most frequent 2,000 words. She justified that the coverage of AWL in an academic text could be up to 570 word families or as much as 10% of the total words. It means that even in academic texts, the learner will find 90% of the general service list (GSL) or the 2,000 most common words. Hence, the academic words are only supportive to the topic in the text they occur. Table 3 shows the example of AWL sublist families retrieved

from Sublist Families of the Academic Word Lists (2015) cited in Cobb’s online vocabulary

profiler.

Table 3. Examples of the Academic Word Lists (Cobb, n.d.)

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Approach Assume Benefit Constitue Create Derive Environment Area Authority Concept Context Data Distribute Establish

Off-list family words consist of proper nouns, unusual words, specialist vocabulary, acronyms, abbreviations, and misspelling (Cobb, n.d.). Table 4 provides the examples of some Off-list words appearing in various articles.

Table 4. Examples of the Off-list words (Menken, 2010; Walinski, Kredens, & Goźdź -Roszkowski, 2007; Laufer & Ravenhorst-Kalovski, 2010)

Appeal Biology Criticism

Moscow Opposition Rebar

Reference Richard Shish

6. Benefits of Knowing Low and High-Frequency Words

Laufer (1999) stated that classifying the high and low frequency words are principally required for educators, because it can be a useful help for them to know the current vocabulary development stages of the students. However, educators are usually found spending time on teaching the low-frequency words and the learners will get the minimal advantage from knowing them (Laufer, 1999). Therefore, Nation (2014) suggests that learning the high frequency words will be well repaid because of the high chance of meeting and using them. Therefore, after knowing the distinction between high and low frequency words, the teacher could emphasize more on the most common words of academic vocabulary and design the exercises in which the students will be using the vocabulary.

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7. Relevant Previous Studies

The previous study by Farjami (2014) using the vocabulary profiler was aimed to provide a specific detail about the vocabulary coverage in seven English textbooks used in Iranian context. The researcher excluded the marginal information such as the table of content, page numbers, and the index at the end of the book but included the metatextual information such as instructions and summaries to be analyzed. The result was presented in the form of table showing the numbers of the frequency profile of vocabulary in those seven textbooks. From the analysis, the researcher could notice that the lexical input containing high frequency and academic vocabulary in the material should be developed.

Another study was conducted by Liontou (2015). VocabProfile 3.0 was used to examine the lexicogrammatical differences between intermediate and advanced reading comprehension tests in Greek. The program was utilized to analyze 135 texts and the result showed Fhigh differences between those two levels. The text in the intermediate level contained a higher proportion of unique words, whereas the advanced level is richer according to the lexical density.

The two previous studies mentioned above utilized vocabulary profiler generated by Cobb. They described the representativeness of exposure to English in the texts analyzed. In addition, Farjami also investigated the word recycling index.

C. THE STUDY

This study was aimed to investigate the vocabulary profile of Academic Listening class material used by English Language Education Program in the Faculty of Language and Literature in Satya Wacana Christian University. The descriptive method was chosen for this

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study because the result of the vocabulary profile reported will aspire to describe the word frequencies of the material.

1. Context of The Study

The context of this study was the English Language Education program of the Faculty of Language and Literature in Satya Wacana Christian University. The students in this study program have to take compulsory courses, and one of the required skill courses is Academic Listening. Academic Listening course was the last listening course in this study program, so the vocabulary was expected to contain adequate academic words appropriate for intermediate to advanced level students. Meanwhile, Satya Wacana Christian University in Salatiga has implemented a new curriculum named Indonesian National Qualification Framework. Due to the change of the curriculum, the material of this course has to undergo some modifications. The number of the academic credit for this course was increased from three to four. As a result, there must be addition in the lexical coverage of its material.

2. Material

The material for the purpose of this present study was Academic Listening material used in English Language Education program of FLL for the 2015/2016 academic year. Since there has been the implementation of the new curriculum, the newest material designed for Academic Listening course in Satya Wacana Christian University has not been investigated yet. The material was used by the students and teacher in a twenty-eight-meeting semester. Only the audio-visual materials such as audio recordings and a short movie were taken to analyse since this study focused on the language exposure for listening skills. Considering the validity and reliability, the audio-visual materials used in all meetings were analysed.

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3. Data Collection Instrument

This study used vocabulary profiler that can be accessed from http://www.lextutor.ca/ to get the data. This Lextutor program is an electronic tool for profiling vocabulary, created by Tom Cobb in 1999. The online program breaks down words in the text to four classifications which are K1 (1-1,000 most common words), K2 (1,001-2,000 most common words), Academic Word Lists, and Off-list words.

4. Data Collection Procedures

For the data collection, the main purpose is to provide the text in form of a word document to make them compatible with the vocabulary profiler. In order to do so, this study used Microsoft Word for the word-processing software. The transcripts of the audio-visual materials were transcribed first. However, during the transcribing process, there were some categories of words were omitted i.e. names (people’s names, brand, and places), foreign vocabulary items, and abbreviations. Therefore, only the meaningful words to English were involved in the data analysis.

5. Data Analysis

The first step to analyse the data is accessing the vocabulary profiler. Go to http://www.lextutor.ca/ and then click on Vocabprofile. Click VP-Classic to get the result

categorized into Laufer & Nation’s 4-way sorter. Go back to the word document, copy, and paste the text per chapter into the dialogue box provided. There is a limitation of the text i.e. 200,000 characters or 35,000 words. Finally, submit the word by clicking SUBMIT_Window and the program will start doing the analysis.

Lextutor as the vocabulary profiler analyses the data automatically. The output will be presented in colours indicating the category. The categories are K1, K2, AWL, and Off-list

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and the colour represent them respectively are blue, green, yellow, and red. This result will be discussed to answer the first research question. For the second research question, displaying the negative vocabulary profile was done by clicking VP negative under the family list of each category. For the third research question, go back to the home page, click Text Lex Compare.

D. FINDINGS AND DISCUSSIONS

This part would attempt to present the vocabulary profile of the Academic Listening course material used. The discussion is divided into three parts. The first one is the overall result to show the vocabulary profile of the Academic Listening course material. The second part shows the negative vocabulary and the list of vocabulary items that were not found in the course material. The third part is the comparison of vocabulary profile between two materials used in two meetings. This last part shows the repeated and unrepeated words in the material being compared. The result of the analysis is presented in forms of tables to show the percentage and number of words.

1. Overall Result

This section shows the finding of vocabulary profile of the Academic Listening material for the whole meeting. The data analysis using The Compleat Lexical Tutor v.4 yielded the result shown in Table 5. As can be seen in the table, the Academic Listening material comprised 59,377 words. It also shows the information of families and types found in the material. According to Bauer and Nation (1993), a word family is the bare stem form of a word and all its derived and inflected varieties. A word type is defined as “any word

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Lamy, & Jones, 2002). For example, the words does, did, doing, and done may all be part of the same word family, but they are considered four different word types. The token is the total number of words found in the text. If for example, there are words could_[2], insects_[2], of_[5], and previously_[1] in a text, the number of tokens is 10. Table 5 shows that there were 1,8585 word families and 5,084 word types out of the total words.

Table 5 indicates that K1 got the highest proportion of the whole material used in the Academic Listening course. Of the vocabulary in all the material, 86.37% of the words were the K1. K2 calculated in the material was as much as 5.29%. The sum of K1 and K2 would be 91.66%. This percentage was below the suggested amount of known words for uninterrupted comprehension which should be at least 95%. Thus, according to Nation (2006), this material could probably be described as linguistically complex for advanced language users, such as students in university level (Schmitt & Schmitt, 2012). The lowest category is from the AWL. It only represented 3.96% from the material. It needs to investigate whether the 389 families of AWL in the Academic Listening material was adequate or not for university levels. The cumulative of K1, K2, and AWL would reach 95.62%. From this percentage, the students would be assumed to have an uninterrupted understanding if they knew all the words from K1 until AWL. The remaining 4.38% (2,600 tokens) of the vocabulary items were from the Off-list words. Despite the fact that the Off-list words are not in the frequency list, they could probably determine the main idea of discussions and be the keywords for the topic. Therefore, this list should not be neglected when teachers are making the selection for vocabulary list to teach.

Table 5. Overall vocabulary profile

Frequency Level

Families (%)

Types (%)

Tokens (%)

Cum. Tokens (%)

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K1 Words 901

48.49%

2,058 40.48%

51,285

86.37% 86.37%

K2 Words 568

30.57%

961 18.90%

3,142

5.29% 91.66%

AWL [570 Fams.]

Tot. 2,570

389 20.94%

704 13.85%

2,350

3.96% 95.62%

Off-list ?? 1,361

26.77%

2,600

4.38% 100.00%

Total

(Unrounded) 1858+?

5,084 100%

59,377

100% ≈100.00%

2. Negative Vocabulary Profiles of the Academic Listening Course Material

This section shows the description of negative vocabulary profile of the Academic Listening course material. The analysis of negative vocabulary profile covered the data from K1, K2, and AWL. Negative vocabulary is vocabulary items which are members of New General Service List (Browne, Culligan, & Phillips, 2013) but not found in the text. Negative vocabulary profile lists would seem to be useful for the teacher to help students enrich their language productions.

a. Negative Vocabulary Profile of K1

In this section, the negative vocabulary profile of K1 words is presented. The summary of data analysis of K1 words that were not found in the material is shown as follows:

K1 Total word families : 964

K1 Families in input : 889 (92.22%) K1 Families not in input : 76 (7.88%)

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As can be seen in the summary, the material comprised 92.22% of word families from NGSL. Therefore, the remaining 7.88% of word families were missing from the material. The numbers indicate the material used in the course appeared to be quite rich in words families. Table 6 shows some word families that were not involved in the course material. The complete list has been put in Appendix A.

Table 6. Negative vocabulary profile of K1 in the Academic Listening course material

ACCOUNTABLE ADVENTURE AFFAIR AGENT ANCIENT

ARISE ARM ATTEMPT BRIGHT CAPITAL

CAPTAIN CASTLE CHIEF CHURCH CLAIM

CLOUD COIN COLONY COMMITTEE CORN

CROWN DOG DREAM FACTORY FAITH

FOREST FORMER FORTH FRESH GATE

b. Negative Vocabulary Profile of K2

This section presents negative vocabulary profile of K2. The vocabulary profiler tool yielded the summary as presented below.

K2 Total word families : 986

K2 Families in input : 567 (57.51%) K2 Families not in input : 420 (42.49%)

From the summary above, it can be seen that the total families in NGSL are 986 headwords. In the Academic Listening course material, the K2 word families accounted were as much as 57.51%, which means 42.49% were missing in the material. In Table 7, some examples from the list of K2 word families that were not found in the material is presented. The complete list has been put in Appendix B.

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ABSENCE ABSENT ABSOLUTELY ACCUSE ACCUSTOM

AEROPLANE ALIVE ALOUD AMBITION AMUSE

ANGLE ANNOY APOLOGIZE APOLOGY APPLAUD

APPLAUSE APPLE APPROVE ARCH ARREST

ARROW ASH ASHAMED ASTONISH AUTUMN

AWAKE AWKWARD AXE BAND BARBER

c. Negative Vocabulary Profile of AWL

This section presents the word families of AWL that were not found in the Academic Listening course material. The summary of negative vocabulary profile for AWL category is presented below.

AWL Total word families : 569

AWL Families in input : 387 (68.01%) AWL Families not in input : 183 (32.16%)

The summary above shows that the course material comprised 68.01% of AWL word families from NGSL. In other words, there were 387 AWL head words found in the material out of 569 word families in the AWL from NGSL. Accordingly, there were 32.16% of AWL word families were not found. Table 8 shows some example of AWL word families missing from the Academic Listening course material. The full list has been put in Appendix C.

Table 8. Negative vocabulary profile of AWL in the Academic Listening course material

ABSTRACT ACCUMULATE ACKNOWLEDGE ADEQUATE ADJACENT

ADJUST ADMINISTRATE AGGREGATE ALBEIT AMEND

ANALOGY APPEND ARBITRARY ATTAIN ATTRIBUTE

AUTHORITY AUTOMATE BEHALF BIAS BULK

CAPABLE CHANNEL CHART CLAUSE COHERENT

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3. Block Frequency Output of Off-list Words

For the reason that each of Off-list words could not be sorted into certain word families, the negative vocabulary of this category is not able to be analysed. Consequently, this study utilized the vocabulary profiler to count the frequency of Off-list words. This online tool sorted the data in a descending order (from high to low) and frequency-blocked them per ten words. The data analysed covered 1,361 types and 2,600 tokens of Off-list words. Table 9 shows the three highest block frequency of Off-list words. In the table, RANK is the ranking of words, FREQ is the number of word occurrence in the text, COVERAGE is the percentage of individual and cumulative word occurrences, and the last column contains the vocabulary items. The complete list of the block frequency has been put in Appendix D. The frequency list yielded by the tool in the vocabulary profiler could probably serve as clues to make a selection of useful and relevant words to teach.

Table 9. The three highest block-frequency output of Off-list words

RANK FREQ COVERAGE WORD

individ. cumulative

1. 1 0.07% 0.07% ABACK

2. 1 0.07% 0.14% ABBREVIATE

3. 1 0.07% 0.21% ABBREVIATED

4. 1 0.07% 0.28% ABBREVIATIONS

5. 1 0.07% 0.35% ABSORBED

6. 1 0.07% 0.42% ABSORBING

7. 1 0.07% 0.49% ABSORBS

8. 1 0.07% 0.56% ABUNDANCE

9. 1 0.07% 0.63% ACCLAIM

10. 1 0.07% 0.70% ACCOMPLISH

11. 1 0.07% 0.77% ACE

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13. 1 0.07% 0.91% ADMISSIONS

14. 1 0.07% 0.98% ADO

15. 1 0.07% 1.05% ADOLESCENCE

16. 1 0.07% 1.12% ADVISOR

17. 1 0.07% 1.19% AERIAL

18. 1 0.07% 1.26% AERONAUTICS

19. 1 0.07% 1.33% AFFECTION

20. 1 0.07% 1.40% AGENDA

21. 1 0.07% 1.47% AGGIE

22. 1 0.07% 1.54% AGGRESSIVENESS

23. 1 0.07% 1.61% AIRPORT

24. 1 0.07% 1.68% AIRY

25. 1 0.07% 1.75% AJAR

26. 1 0.07% 1.82% ALERT

27. 1 0.07% 1.89% ALGEBRA

28. 1 0.07% 1.96% ALLERGEN

29. 1 0.07% 2.03% ALLERGIC

30. 1 0.07% 2.10% ALLERGIES

4. Comparison of Vocabulary Frequency across Materials Used in Each Meeting

This section compares the finding of vocabulary profile of the Academic Listening material across meeting. Table 10 shows the comparison of K1, K2, AWL, and Off-list frequencies among materials used in each Academic Listening course meeting.

As can be seen in Table 10, for the K1 category, the highest coverage was found in the material for meeting 2 with 92.69% and the lowest was for meeting 11 which only comprised 79.06% of K1. The highest and lowest proportions of K2 were respectively found in the material for meeting 16 (7.24%) and 8 (2.65%). The AWL coverage ranged from 0.94% (meeting 17) to 7.20% (meeting 10). For the Off-list category, the highest coverage was found in meeting 17 with 7.24% and the lowest was in meeting 2 with only 1.18% of Off-list. The amount of word frequency level in each meeting may highly depend on the main topic of the discussion.

Few materials for the Academic Listening course (meeting 2, 5, 14, 24, and 27) reached the least 95% from the cumulative of K1 and K2. Moreover, there were materials for ten meetings in which the cumulative percentage of K1, K2, and AWL could not cover the

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required percentage for a good understanding. Thus, according to Nation (2006), the material could generally be described as linguistically complex for advanced language users, such as students in university level (Schmitt & Schmitt, 2012).

Table 10. Comparison of word frequency levels

Chapters K1 words (%) K2 words (%) AWL words (%) Off-list words (%) Meeting 1 Cumulative 86.89 86.89 5.86 92.75 3.13 95.88 4.12 100 Meeting 2 Cumulative 92.69 92.69 4.35 97.04 1.78 98.82 1.18 100 Meeting 3 Cumulative 88.52 88.52 3.57 92.09 6.58 98.67 1.33 100 Meeting 4 Cumulative 87.31 87.31 5.76 93.07 2.18 95.25 4.75 100 Meeting 5 Cumulative 89.91 89.91 5.62 95.53 2.17 97.70 2.30 100 Meeting 6 Cumulative 89.54 89.54 4.03 93.57 2.76 96.33 3.67 100 Meeting 7 Cumulative 86.43 86.43 4.89 91.32 2.81 94.13 5.87 100 Meeting 8 Cumulative 86.16 86.16 2.65 88.81 4.33 93.14 6.86 100 Meeting 9 Cumulative 86.24 86.24 4.22 90.46 2.51 92.97 7.03 100 Meeting 10 Cumulative 82.31 82.31 3.75 86.06 7.20 93.26 6.74 100 Meeting 11 Cumulative 79.06 79.06 6.63 85.69 6.17 91.86 8.14 100 Meeting 12 Cumulative 83.17 83.17 6.36 89.53 3.96 93.49 6.51 100

19 Meeting 13 Cumulative 85.77 85.77 4.03 89.80 4.83 94.63 5.37 100 Meeting 14 Cumulative 90.83 90.83 4.85 95.68 2.11 97.79 2.21 100 Meeting 15 Cumulative 87.14 87.14 4.02 91.16 5.71 96.87 3.13 100 Meeting 16 Cumulative 83.31 83.31 7.24 90.55 3.94 94.49 5.51 100 Meeting 17 Cumulative 85.16 85.16 6.66 91.82 0.94 92.76 7.24 100 Meeting 18 Cumulative 89.50 89.50 4.12 93.62 4.32 97.94 2.06 100 Meeting 19 Cumulative 87.80 87.80 3.77 91.57 6.05 97.62 2.38 100 Meeting 20 Cumulative 87.55 87.55 5.71 93.26 3.67 96.93 3.07 100 Meeting 21 Cumulative 88.24 88.24 4.99 93.23 3.71 96.94 3.06 100 Meeting 22 Cumulative 87.65 87.65 5.20 92.85 4.11 96.96 3.04 100 Meeting 23 Cumulative 87.31 87.31 4.73 92.04 3.71 95.75 4.25 100 Meeting 24 Cumulative 90.10 90.10 5.56 95.66 1.22 96.88 3.12 100 Meeting 25 Cumulative 85.40 85.40 3.73 89.13 5.24 94.37 5.63 100 Meeting 26 Cumulative 88.15 88.15 6.27 94.42 2.20 96.62 3.38 100 Meeting 27 Cumulative 89.40 89.40 6.04 95.44 1.94 97.38 2.62 100 Meeting 28 Cumulative 86.89 84.88 5.86 92.75 3.13 95.88 4.12 100

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5. Text Comparison of Materials

This last section displays the comparison of text between materials for two meetings of the Academic Listening course. The data of comparison also presents the token recycling index of the materials being compared. Recycling index is the recycled words found in the second text (Cobb, 2007). In other words, recycling index is the ratio between words that are shared by two texts and the total number of words in the second text. It provides useful information about what words are shared in both texts and what words are new or unique in the second text. The higher the token recycling index, the higher the text comprehensibility would be. After knowing the shared and unique words in two texts, teachers could highlight important words that are not covered in the first text. The materials that were compared in this section are those having the highest and lowest percentage of word frequency levels.

a. Comparison of Materials for Meeting 2 vs. Meeting 11

The first comparison contrasts the material for meeting 2 and 11. Considering the high differences of K1 percentage in those two meetings, the materials were compared. Table 11 displays the summary of the comparison between materials for meeting 2 and meeting 11. It also shows the number of token and families recycling index. The word frequency can be seen next to that word. The complete table has been put in Appendix E. As can be seen on top of the table, the token recycling index was 52.64%. It means that as much as 52.64% of words in materials for meeting 2 and meeting 11 were shared. From that result, the unique words in the second text (meeting 11) can be calculated by deducting the recycling index from 100% (100%-52.64%). Thus, the unique words that were only found in the material for meeting 11 were 47.36%. It means that if the students were initially exposed to the material for meeting 2 and they proceed to the material for meeting 11, they would likely to listen to 1,704 new words.

21

Table 11. Shared and unique words in materials for meeting 2 and 11 TOKEN Recycling Index: (897 repeated tokens : 1704 tokens in new text) = 52.64% FAMILIES Recycling Index: (110 repeated families : 502 families in new text) = 21.91%

Unique to first 121 tokens 70 families 001. write 7 002. week 6 003. assign 5 004. book 5 005. find 5 006. last 5 007. go 4 008. really 4 009. bed 3 010. feel 3 011. tire 3 012. early 2 013. essay 2 014. haven’ 2 015. hullo 2 016. i’ll 2 017. library 2 018. lot 2 019. night 2 020. note 2

Shared 897 tokens 110 families 001. the 82 002. be 69 003. to 57 004. this 50 005. a 45 006. i 43 007. in 42 008. of 42 009. and 35 010. it 34 011. have 24 012. they 19 013. do 17 014. use 14 015. as 13 016. for 12 017. not 12 018. some 11 019. you 10 020. but 9

Unique to second 807 tokens

392 families Freq first (then alpha)

001. autoclave 22 002. heat 12 003. water 12 004. with 12 005. if 11 006. will 11 007. indicate 10 008. sterilise 10 009. he 9

010. instrument 9 011. medical 8 012. or 8 013. want 8 014. by 7 015. care 7 016. pressure 7 017. bacterium 6 018. boil 6

VP novel items

Same list Alpha first 001. aback 1 002. above 2 003. absorb 1 004. access 1 005. add 2

006. advertise 1 007. after 1 008. against 1 009. air 2 010. ajar 1 011. also 5 012. although 1 013. angry 1 014. anticipate 1 015. any 2

016. approximate 1 017. artificial 1

b. Comparison of Materials for Meeting 8 vs. Meeting 16

The second comparison is between materials for meeting 8 and meeting 16. These two meetings constituted the highest differences of K2 percentage. Table 12 displays the summary of the comparison between materials for meeting 8 and meeting 16 as well as some examples of the shared and unique words. The complete table has been put in Appendix F. Table 12 indicates that the token recycling index between the two materials was 54.89%. It indicates that 54.89% of the total words counted were found in both materials. From this data, it can be concluded that the words unique to the material for meeting 16 were 45.11% (100%-54.89%). Suppose that the students listened to the material for meeting 16 after they listened to the materials for meeting 8, they would find 634 new tokens.

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Table 12. Shared and unique words in materials for meeting 8 and 16 TOKEN Recycling Index: (348 repeated tokens : 634 tokens in new text) = 54.89% FAMILIES Recycling Index: (81 repeated families : 263 families in new text) = 30.80%

Unique to first 333 tokens 185 families 001. yes 15 002. course 10 003. see 7 004. with 7 005. class 6 006. well 6 007. doctor 5 008. good 5 009. interact 5 010. professor 5 011. ten 5

012. hope 4 013. how 4 014. primate 4 015. question 4 016. really 4 017. sir 4 018. species 4 019. then 4 020. think 4

Shared 348 tokens 81 families 001. and 30 002. the 28 003. be 22 004. he 18 005. a 17 006. in 16 007. to 16 008. of 15 009. this 15 010. will 13 011. as 11 012. can 10 013. you 8 014. have 7 015. term 6 016. able 5 017. they 5 018. at 4 019. on 4 020. come 3

Unique to second 286 tokens

182 families Freq first (then alpha) 001. child 8 002. artery 6 003. develop 6 004. heart 6 005. reach 6 006. skill 6 007. also 4 008. blood 4 009. by 4 010. stage 4 011. use 4 012. block 3 013. cognitive 3 014. diet 3 015. even 3 016. mature 3 017. middle 3 018. more 3

VP novel items

Same list Alpha first 001. #number 1 002. according 1 003. acquire 2 004. adolescence 1 005. adult 1

006. affect 2 007. affection 1 008. age 2

009. already 1 010. also 4 011. around 1 012. artery 6 013. assist 1 014. attack 1 015. baby 2 016. become 2

c. Comparison of Materials for Meeting 10 vs. Meeting 17

The third comparison measures the shared and unique words between materials for meeting 10 and meeting 17. Meeting 10 got the highest percentage of AWL, while meeting 17 comprised the lowest amount of AWL. Table 13 presents the summary of the comparison between materials for those two meetings as well as some examples of the shared and unique words. The complete table has been put in Appendix G. Table 13 denotes that the recycling index was 70.03%. Thus, it indicates that as much as 29.97% (100%-70.03%) of the total words from two materials was unique to the material for meeting 17. The token recycling index in this section is the highest compared to the other two results.

Table 13. Shared and unique words in materials for meeting 10 and 17 TOKEN Recycling Index: (1397 repeated tokens : 1995 tokens in new text) = 70.03% FAMILIES Recycling Index: (172 repeated families : 462 families in new text) = 37.23%

23

Unique to first 703 tokens 335 families 001. compute 28 002. rule 17 003. thou 16 004. ethics 13 005. language 12 006. write 12 007. internet 11 008. number 11 009. tale 9 010. command 8 011. ethical 8 012. example 8 013. it’ 8 014. wrong 8 015. else’ 7 016. read 7 017. ten 7 018. people 6 019. develop 5 020. file 5

Shared 1397 tokens 172 families 001. the 83 002. be 78 003. you 77 004. i 62 005. it 45 006. to 44 007. not 42 008. a 39 009. no 36 010. this 34 011. he 32 012. of 32 013. and 29 014. have 29 015. do 26 016. but 24 017. one 24 018. for 22 019. she 21 020. so 19

Unique to second 598 tokens

290 families Freq first (then alpha) 001. wait 15 002. aree 13 003. master 10 004. just 9 005. total 9 006. bad 8 007. cool 8 008. secret 8 009. awesome 7 010. dance 7 011. fang 7 012. fast 7 013. leave 7 014. let 7 015. monster 7 016. too 7 017. best 6 018. crocodile 5

VP novel items

Same list Alpha first 001. academy 2 002. actual 1 003. adopt 1 004. adult 1 005. afraid 4 006. after 1 007. again 4 008. ago 3 009. almost 1 010. amaze 3 011. aree 13 012. armour 2 013. around 1 014. art 2 015. attend 1 016. awe 2

E. CONCLUSION

This study was aimed to investigate the vocabulary profile of Academic Listening course materials used in the English language education program in Satya Wacana Christian University. There were three main results of the data analysis in this study. The first one is that the Academic Listening course material used in 2015/2016 academic year would probably be lexically challenging for the students because the K1 and K2 cumulative only covered 91.66%. That number was below the suggested percentage for good understanding of the running text (Nation, 2006). The second result revealed the negative vocabulary profiles of K1, K2, and AWL including the list of vocabulary items that were not appearing in the material as well as the block frequency output of off-list words. The result shows negative vocabulary profiles of K1, K2, and AWL, respectively 7.88%, 42.49%, and 32.16%. It might indicate that the material was quite rich in terms of word families. The third result disclosed

24

the token recycling index of two materials. There were three pairs of material being compared in this study. The analysis demonstrates the recycling index for the material for meeting 2 and 11 was 52.64%. The material for meeting 8 and 16, the recycling index was 54.89%. The highest percentage of token recycling index was found in the material for meeting 10 and 17 which contained 70.03% shared vocabulary items. All the yielded result was hoped to be fruitful for the course developer in order to create the vocabulary learning strategies for the students.

The limitation of this study is the data taken to analyse neglected to differentiate the material sources. In the Academic Listening course material, the material was compiled from TOEFL, IELTS, textbooks, video podcasts, and a short movie. Yet, the sources were treated the same because this study focused more on the material used in each meeting.

For the further research, it is suggested to profile the material of the Academic Listening course based on its material source, whether it is from TOEFL, IELTS, textbooks, or other sources. This is based on the consideration that spoken texts from various resources would have different vocabulary profile characteristics.

For the recommendation, it would be suggested for the course developer to recheck the material in terms of its vocabulary coverage and consider the words that need to be highlighted by creating a vocabulary reference list. The result of this study was expected to give the course developer some assistant in later choosing the listening exercises for the course takers.

25

ACKNOWLEDGEMENT

This undergraduate thesis could be completed with the kind support and help from many individuals. I would like to extend my sincere thanks to all of them.

Foremost, I want to offer this endeavour to God Almighty for the blessings He bestowed upon me, peace of my mind, and good health in order to finish this study.

I would like to express my gratitude towards my beloved father (Simon Slamet S.) and mother (Christina Yustina) for the abundant encouragement and motivation to pursue this undertaking.

I feel indebted to my supervisor, Anne Indrayanti Timotius, M.Ed., for imparting her knowledge and expertise in this study. I cannot also express my gratitude enough to Prof. Dr. Gusti Astika, M. A., for the tutorial and approval of my work.

This study could also be completed in time because of the help from my friends, Ika Runtiana and Luki Rias who kindly spent the time giving a private technical tutorial to me. My thanks and appreciations also go to the other colleague and people who willingly helped me out in any way they could.

26

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30

APPENDICES

Appendix A

The complete list of K1 negative vocabulary profile

ACCOUNTABLE ADVENTURE AFFAIR AGENT ANCIENT

ARISE ARM ATTEMPT BRIGHT CAPITAL

CAPTAIN CASTLE CHIEF CHURCH CLAIM

CLOUD COIN COLONY COMMITTEE CORN

CROWN DOG DREAM FACTORY FAITH

FOREST FORMER FORTH FRESH GATE

GIFT HEAVY INCH IRON JUSTICE

LAUGHTER LAY LIP LORD MILK

MINISTER MOTOR MRS NECESSITY NEIGHBOUR NOBLE NUMERICAL ORDINARY OWE PROMISE

PROVISION QUARTER RACE RANK RELIGION

REPLY REPUBLIC SHADOW SHINE SILENCE

SKY SOUL SPITE SQUARE STONE

STREAM SWORD TEMPLE TRUST VICTORY

VIRTUE WAGE WAR WEALTH WINDOW

YIELD

Appendix B

The complete list of K2 negative vocabulary profile

ABSENCE ABSENT ABSOLUTELY ACCUSE ACCUSTOM AEROPLANE ALIVE ALOUD AMBITION AMUSE ANGLE ANNOY APOLOGIZE APOLOGY APPLAUD

APPLAUSE APPLE APPROVE ARCH ARREST

ARROW ASH ASHAMED ASTONISH AUTUMN

AWAKE AWKWARD AXE BAND BARBER

BARE BASIN BASKET BATH BATHE

BEAM BEG BELL BERRY BLADE

BLESS BOLD BOUND BOWL BRAVE

BREATH BRIBE BROWN BRUSH BUNDLE

BURIAL BURST BURY BUTTER BUTTON

CAGE CALCULATE CANAL CAPE CARRIAGE

CART CATTLE CAUTION CENTIMETRE CHARM

CHEAT CHEER CHEESE CHEQUE CHEST

CHIMNEY CHRISTMAS CIVILISE CLAY COARSE COLLAR COMB COMPANION CONFESS CONSCIOUS

COPPER CORK COTTAGE COW COWARD

CRASH CREAM CRIME CRIMINAL CRUEL

CUPBOARDS CURL CURSE CURVE CUSHION

DARE DEAF DEBT DECAY DECEIVE

DEED DEER DELICATE DESPAIR DECIL

DIAMOND DISGUST DISMISS DITCH DONKEY

DRAG DRAWER DULL DUST EARNEST

EASE ELASTIC ELDER ELEPHANT ENCLOSE

ESSENCE EXCESS FADE DAINT FANCY

31

FEVER FIERCE FLASH FLESH FORBID

FORGIVE FORK FRY FUNERAL GALLON

GAP GARAGE GAY GLORY GOAT

GRAIN GRAM GRAVE GREED GRIND

GUARD GUILTY HAMMER HANDKERCHIEF HASTE

HAY HEAL HEAP HINDER HOLE

HOLY HORIZON HOST HUMBLE HURRAH

HUT IDEAL IDLE IMMENSE INFORMALLY

INN INSULT JAW JEALOUS JEWEL

HUICE JUMP KILOGRAM KISS KNEEL

KNIFE KNOT LADDER LAMP LAZY

LEAF LEAN LIBERTY LIMB LIQUID

LITRE LOAF LODGING LOG LOOSE

LUMP LUNG MAD MEANTIME MEANWHILE

MEND MERCY MERRY MESSENGER MILD

MILL MILLIGRAM MILLILITRE MILLIMETRE MINERAL

MISERABLE MODEST MOTION MOUSE MUD

MURDER NEAT NECK NEPHEW NEST

NET NIECE NOSE NOUN NUISANCE

OAR OBEY OFFEND OMIT OPPOSE

ORANGE ORNAMENT OVERCOME PALE PAN

PARCEL PARDON PASSAGE PASTE PATH

PATRIOTIC PAUSE PAW PEARL PECULIAR

PENNY PET PIG PIGEON PIN

PINCH PINK PINT PIPE PLASTER

PLOUGH PLURAL POLITE POSTPONE POUR

POWDER PRAISE PRAY PREACH PREJUDICE

PRIEST PROCESSION PROFESSION PROGRAMME PRONOUNCE

PUMP PUNCTUAL PUNISH PUPIL PURE

PURPLE QUARREL QUART RABBIT RAIL

RAKE RAT RAW RAY RAZOR

REFRESH REJOICE REMEDY REQUEST RESCUE

RESIGN REVENGE RICE ROAR ROAST

ROB ROD ROPE ROT RUB

RUBBER RUBBISH RUIN RUSH RUST

SCARED SACRIFICE SADDLE SALARY SAND

SAUCE SAUCER SAWS SCENT SCISSORS

SCOLD SCORN SCRAPE SCREEN SEED

SEIZE SELDOM SEVERE SHAVE SLIDE

SOCK SOLEMN SORE SOUP SOUR

SOW SPADE SPARE SPIN SPIT

SPLENDID SPLIT SPOON STAIN STEEP

STEM STIR STOCKING STOVE STRAP

STRAW STRING STRIP STRIPE SUGAR

SUPPER SUSPECT SUSPICION SWALLOW SWEAR

SWING SYMPATHY TAILOR TAP TAXI

TELEGRAPH TENDER THICK THIEF THIN

THIRST THORN THREAD THROAT THUMB

TIDY TIN TOBACCO TOWEL TRANSLATE

TRAY TREASURE TRUNK TUBE TUNE

TWIST UGLY UMBRELLA UPPER UPRIGHT

VAIN VEIL VERB VERSE VOYAGE

WAIST WAX WEAPON WEAVE WEED

WET WHEAT WHIP WHISPER WICKED

32

WRAP WRECK WRIST YELLOW

Appendix C

The complete list of AWL negative vocabulary profile

ABSTRACT ACCUMULATE ACKNOWLEDGE ADEQUATE ADJACENT ADJUST ADMINISTRATE AGGREGATE ALBEIT AMEND

ANALOGY APPEND ARBITRARY ATTAIN ATTRIBUTE

AUTHORITY AUTOMATE BEHALF BIAS BULK

CAPABLE CHANNEL CHART CLAUSE COHERENT

COMMENCE COMMIT COMPATIBLE COMPENSATE COMPILE COMPLEMENT COMPONENT COMPRISE CONCEIVE CONCURRENT

CONFINE CONSENT CONTEXT CONTRACT CONTRARY

CONTROVERSY CONVENE CORPORATE CURRENCY DECLINE

DEDUCE DENOTE DEPRESS DERIVE DEVIATE

DIMINISH DISCRETE DISCRIMINATE DISPLAY DOMAIN DURATION ELEMENT ELIMINATE EMPIRICAL ENHANCE

ENTITY ERROR ESTATE EVALUATE EXCEED

EXPLOIT EXTERNAL EXTRACT FEDERAL FINITE

FLEXIBLE FLUCTUATE FORMULA FORTHCOMING FOUNDATION FRAMEWORK FURTHERMORE HENCE HIERARCHY HYPOTHESIS IDEOLOGY IGNORANT IMPLICIT IMPOSE INCLINE INCOME INEVITABLE INFRASTRUCTURE INHERENT INITIAL

INITIATE INJURE INPUT INSERT INTEGRITY

INTERVAL INTERVENE INTRINSIC INVOKE ISOLATE

JUSTIFY LABOUR LAYER LEGISLATE LEVY

LIKEWISE MANIPULATE MINIMISE MINISTYR MINOR

MODE MODIFY MUTUAL NEUTRAL NONETHELESS

NORM NOTWITHSTANDING OBTAIN OFFSET ONGOING OUTCOME OVERLAP PARAMETER PARTNER PERCEIVE PERSIST PORTION PRACTICIONER PREDOMINANT PRELIMINARY

PRINCIPAL PRIOR RATIONAL RECOVER REGIME

REINFORCE REJECT RELUCTANCE RESTORE RESTRAIN

RETAIN REVEAL REVENUE REVERSE REVOLUTION

RIGID SCENARIO SECTOR SECURE SEEK

SEX SIMULATE SO-CALLED SPECIFY SPHERE

SUBORDINATE SUBSIDY SUBTITUTE SUCCESSOR SUM

SUSPEND SUSTAIN TRIGGER ULTIMATE UNDERGO

UNDERLINE UNDERTAKE UNIFY VIOLATE VISIBLE VISION VOLUNTARY WHEREAS WHEREBY WIDESPREAD

33 Appendix D

Block Frequency output of off-list words

RANK FREQ.

COVERAGE

WORD individ. cumulative

1. 1 0.07% 0.07% ABACK

2. 1 0.07% 0.14% ABBREVIATE

3. 1 0.07% 0.21% ABBREVIATED

4. 1 0.07% 0.28% ABBREVIATIONS

5. 1 0.07% 0.35% ABSORBED

6. 1 0.07% 0.42% ABSORBING

7. 1 0.07% 0.49% ABSORBS

8. 1 0.07% 0.56% ABUNDANCE

9. 1 0.07% 0.63% ACCLAIM

10. 1 0.07% 0.70% ACCOMPLISH

11. 1 0.07% 0.77% ACE

12. 1 0.07% 0.84% AD

13. 1 0.07% 0.91% ADMISSIONS

14. 1 0.07% 0.98% ADO

15. 1 0.07% 1.05% ADOLESCENCE

16. 1 0.07% 1.12% ADVISOR

17. 1 0.07% 1.19% AERIAL

18. 1 0.07% 1.26% AERONAUTICS

19. 1 0.07% 1.33% AFFECTION

20. 1 0.07% 1.40% AGENDA

21. 1 0.07% 1.47% AGGIE

22. 1 0.07% 1.54% AGGRESSIVENESS

23. 1 0.07% 1.61% AIRPORT

24. 1 0.07% 1.68% AIRY

25. 1 0.07% 1.75% AJAR

26. 1 0.07% 1.82% ALERT

27. 1 0.07% 1.89% ALGEBRA

28. 1 0.07% 1.96% ALLERGEN

29. 1 0.07% 2.03% ALLERGIC

30. 1 0.07% 2.10% ALLERGIES

34

32. 1 0.07% 2.24% ALPHABET

33. 1 0.07% 2.31% ALRIGHT

34. 1 0.07% 2.38% ALTITUDE

35. 1 0.07% 2.45% ALTITUDES

36. 1 0.07% 2.52% AMAZING

37. 1 0.07% 2.59% ANNOTATED

38. 1 0.07% 2.66% ANNOUNCEMENT

39. 1 0.07% 2.73% ANNUALS

40. 1 0.07% 2.80% ANT

41. 1 0.07% 2.87% ANTHROPOLOGIST

42. 1 0.07% 2.94% ANYTIME

43. 1 0.07% 3.01% APARTMENT

44. 1 0.07% 3.08% APPALLING

45. 1 0.07% 3.15% APPETITE

46. 1 0.07% 3.22% ARCHAEOLOGICAL

47. 1 0.07% 3.29% ARCHAEOLOGY

48. 1 0.07% 3.36% ARCHITECTURE

49. 1 0.07% 3.43% ARENA

50. 1 0.07% 3.50% ARGUABLY

51. 1 0.07% 3.57% ARISTOCRAT

52. 1 0.07% 3.64% ARITHMETIC

53. 1 0.07% 3.71% ARMOR

54. 1 0.07% 3.78% ARROGANT

55. 1 0.07% 3.85% ARTERIES

56. 1 0.07% 3.92% ARTERY

57. 1 0.07% 3.99% ASSET

58. 1 0.07% 4.06% ASTEROIDS

59. 1 0.07% 4.13% ATHLETE

60. 1 0.07% 4.20% ATHLETES

61. 1 0.07% 4.27% ATHLETIC

62. 1 0.07% 4.34% ATLAS

63. 1 0.07% 4.41% ATMOSPHERE

64. 1 0.07% 4.48% ATMOSPHERIC

65. 1 0.07% 4.55% ATTENTIVE

66. 1 0.07% 4.62% ATTEST

35

68. 1 0.07% 4.76% AUDIO

69. 1 0.07% 4.83% AUDIT

70. 1 0.07% 4.90% AUDITING

71. 1 0.07% 4.97% AUDITORIUM

72. 1 0.07% 5.04% AUTOCLAVE

73. 1 0.07% 5.11% AUTOCLAVED

74. 1 0.07% 5.18% AUTOCLAVES

75. 1 0.07% 5.25% AUTOCLAVING

76. 1 0.07% 5.32% AWARD

77. 1 0.07% 5.39% AWE

78. 1 0.07% 5.46% AWESOME

79. 1 0.07% 5.53% AWESOMENESS

80. 1 0.07% 5.60% AWFUL

81. 1 0.07% 5.67% AWFULLY

82. 1 0.07% 5.74% BACHELOR

83. 1 0.07% 5.81% BACKPACKS

84. 1 0.07% 5.88% BACKSTAGE

85. 1 0.07% 5.95% BACTERIA

86. 1 0.07% 6.02% BACTERIAL

87. 1 0.07% 6.09% BACTERIUM

88. 1 0.07% 6.16% BADMINTON

89. 1 0.07% 6.23% BALD

90. 1 0.07% 6.30% BANANA

91. 1 0.07% 6.37% BANANAS

92. 1 0.07% 6.44% BANDIT

93. 1 0.07% 6.51% BANDITS

94. 1 0.07% 6.58% BARBECUE

95. 1 0.07% 6.65% BARRELLING

96. 1 0.07% 6.72% BARRIERS

97. 1 0.07% 6.79% BASEBALL

98. 1 0.07% 6.86% BASEMENT

99. 1 0.07% 6.93% BASICS

100. 1 0.07% 7.00% BASKETBALL

101. 1 0.07% 7.07% BASTIONS

102. 1 0.07% 7.14% BATCH

36

104. 1 0.07% 7.28% BEACH

105. 1 0.07% 7.35% BEAUTIES

106. 1 0.07% 7.42% BEDTIME

107. 1 0.07% 7.49% BEE

108. 1 0.07% 7.56% BEER

109. 1 0.07% 7.63% BEES

110. 1 0.07% 7.70% BEFOREHAND

111. 1 0.07% 7.77% BEHOLD

112. 1 0.07% 7.84% BENCHES

113. 1 0.07% 7.91% BET

114. 1 0.07% 7.98% BEVERAGES

115. 1 0.07% 8.05% BIBLE

116. 1 0.07% 8.12% BIBLICAL

117. 1 0.07% 8.19% BIBLIOGRAPHY

118. 1 0.07% 8.26% BIKING

119. 1 0.07% 8.33% BILINGUAL

120. 1 0.07% 8.40% BIMONTHLY

121. 1 0.07% 8.47% BIODEGRADES

122. 1 0.07% 8.54% BIOLOGICAL

123. 1 0.07% 8.61% BIOLOGIST

124. 1 0.07% 8.68% BIOLOGY

125. 1 0.07% 8.75% BIOMETRICS

126. 1 0.07% 8.82% BIOPHYSICAL

127. 1 0.07% 8.89% BISON

128. 1 0.07% 8.96% BIZARRE

129. 1 0.07% 9.03% BLANKET

130. 1 0.07% 9.10% BLEND

131. 1 0.07% 9.17% BLENDED

132. 1 0.07% 9.24% BLINK

133. 1 0.07% 9.31% BLOCKAGE

134. 1 0.07% 9.38% BLOG

135. 1 0.07% 9.45% BLOGGER

136. 1 0.07% 9.52% BLOGGING

137. 1 0.07% 9.59% BLONDE

138. 1 0.07% 9.66% BLOSSOM

37

140. 1 0.07% 9.80% BONUS

141. 1 0.07% 9.87% BOOKCASE

142. 1 0.07% 9.94% BOOKCASES

143. 1 0.07% 10.01% BOOKED

144. 1 0.07% 10.08% BOOKING

145. 1 0.07% 10.15% BOOKLISTS

146. 1 0.07% 10.22% BOOKSELLERS

147. 1 0.07% 10.29% BOOKSHELF

148. 1 0.07% 10.36% BOOKSHOP

149. 1 0.07% 10.43% BOOKSTORE

150. 1 0.07% 10.50% BOOM

151. 1 0.07% 10.57% BOOSTER

152. 1 0.07% 10.64% BOOTH

153. 1 0.07% 10.71% BOOTS

154. 1 0.07% 10.78% BORING

155. 1 0.07% 10.85% BORROWINGS

156. 1 0.07% 10.92% BOSS

157. 1 0.07% 10.99% BOTANISTS

158. 1 0.07% 11.06% BOXING

159. 1 0.07% 11.13% BOYFRIEND

160. 1 0.07% 11.20% BRAND

161. 1 0.07% 11.27% BREEDING

162. 1 0.07% 11.34% BREEZE

163. 1 0.07% 11.41% BRILLIANT

164. 1 0.07% 11.48% BROADCASTER

165. 1 0.07% 11.55% BROCHURE

166. 1 0.07% 11.62% BRONZE

167. 1 0.07% 11.69% BUDGET

168. 1 0.07% 11.76% BUFFALO

169. 1 0.07% 11.83% BUG

170. 1 0.07% 11.90% BULLET

171. 1 0.07% 11.97% BULLYING

172. 1 0.07% 12.04% BUMMER

173. 1 0.07% 12.11% BUNK

174. 1 0.07% 12.18% BUNNIES

38

176. 1 0.07% 12.32% BURDEN

177. 1 0.07% 12.39% BUSTER

178. 1 0.07% 12.46% BUTLER

179. 1 0.07% 12.53% BUTT

180. 1 0.07% 12.60% BUTTERFLIES

181. 1 0.07% 12.67% BYE

182. 1 0.07% 12.74% CAB

183. 1 0.07% 12.81% CAFE

184. 1 0.07% 12.88% CAFETERIA

185. 1 0.07% 12.95% CALCULUS

186. 1 0.07% 13.02% CALMER

187. 1 0.07% 13.09% CAMPAIGN

188. 1 0.07% 13.16% CAMPER

189. 1 0.07% 13.23% CAMPSITES

190. 1 0.07% 13.30% CAMPUS

191. 1 0.07% 13.37% CANCELLED

192. 1 0.07% 13.44% CAPITALISM

193. 1 0.07% 13.51% CARBON

194. 1 0.07% 13.58% CAREER

195. 1 0.07% 13.65% CAREERS

196. 1 0.07% 13.72% CARTOGRAPHY

197. 1 0.07% 13.79% CARTONS

198. 1 0.07% 13.86% CASH

199. 1 0.07% 13.93% CASHIER

200. 1 0.07% 14.00% CASSETTES

201. 1 0.07% 14.07% CASUALTIES

202. 1 0.07% 14.14% CATALOGUES

203. 1 0.07% 14.21% CATER

204. 1 0.07% 14.28% CATSUP

205. 1 0.07% 14.35% CELEBRATE

206. 1 0.07% 14.42% CELEBRATION

207. 1 0.07% 14.49% CELL

208. 1 0.07% 14.56% CELLS

209. 1 0.07% 14.63% CELLULAR

210. 1 0.07% 14.70% CENSUS

39

212. 1 0.07% 14.84% CHAMPIONS

213. 1 0.07% 14.91% CHAPEL

214. 1 0.07% 14.98% CHAT

215. 1 0.07% 15.05% CHEMIST

216. 1 0.07% 15.12% CHEMISTRY

217. 1 0.07% 15.19% CHERRIES

218. 1 0.07% 15.26% CHILLED

219. 1 0.07% 15.33% CHIMPANZEES

220. 1 0.07% 15.40% CHOLERA

221. 1 0.07% 15.47% CHRYSANTHEMUM

222. 1 0.07% 15.54% CHUCK

223. 1 0.07% 15.61% CHUNKS

224. 1 0.07% 15.68% CINEMA

225. 1 0.07% 15.75% CIRCULATED

226. 1 0.07% 15.82% CLAN

227. 1 0.07% 15.89% CLASSROOM

228. 1 0.07% 15.96% CLASSROOMS

229. 1 0.07% 16.03% CLEANSED

230. 1 0.07% 16.10% CLERGY

231. 1 0.07% 16.17% CLICKS

232. 1 0.07% 16.24% CLIMATE

233. 1 0.07% 16.31% CLIP

234. 1 0.07% 16.38% CLIPPED

235. 1 0.07% 16.45% CLIPPER

236. 1 0.07% 16.52% CLOAKROOM

237. 1 0.07% 16.59% CLOSET

238. 1 0.07% 16.66% CLUES

239. 1 0.07% 16.73% CLUMPS

240. 1 0.07% 16.80% CLUSTERED

241. 1 0.07% 16.87% CO

242. 1 0.07% 16.94% COACH

243. 1 0.07% 17.01% COASTAL

244. 1 0.07% 17.08% COASTLINES

245. 1 0.07% 17.15% COGENT

246. 1 0.07% 17.22% COGNITION

40

248. 1 0.07% 17.36% COINING

249. 1 0.07% 17.43% COLLATING

250. 1 0.07% 17.50% COLLIDE

251. 1 0.07% 17.57% COLLISIONS

252. 1 0.07% 17.64% COLUMN

253. 1 0.07% 17.71% COMBAT

254. 1 0.07% 17.78% COMBUSTIBILITY

255. 1 0.07% 17.85% COMEDY

256. 1 0.07% 17.92% COMETS

257. 1 0.07% 17.99% COMMANDMENT

258. 1 0.07% 18.06% COMMANDMENTS

259. 1 0.07% 18.13% COMMUNISM

260. 1 0.07% 18.20% COMPASS

261. 1 0.07% 18.27% COMPASSION

262. 1 0.07% 18.34% COMPETENCE

263. 1 0.07% 18.41% CONCERT

264. 1 0.07% 18.48% CONDITIONING

265. 1 0.07% 18.55% CONDUCTOR

266. 1 0.07% 18.62% CONFIDENTIAL

267. 1 0.07% 18.69% CONTAGIOUS

268. 1 0.07% 18.76% CONTAINMENT

269. 1 0.07% 18.83% CONTINENTAL

270. 1 0.07% 18.90% CONTINUALLY

271. 1 0.07% 18.97% CONTRACTIONS

272. 1 0.07% 19.04% CONVENORS

273. 1 0.07% 19.11% CONVEY

274. 1 0.07% 19.18% COPE

275. 1 0.07% 19.25% COPYRIGHT

276. 1 0.07% 19.32% COSMIC

277. 1 0.07% 19.39% COUCHANT

278. 1 0.07% 19.46% COUNTERACT

279. 1 0.07% 19.53% COUNTY

280. 1 0.07% 19.60% COURIER

281. 1 0.07% 19.67% COURTESIES

282. 1 0.07% 19.74% COVENTRY

41

284. 1 0.07% 19.88% COWER

285. 1 0.07% 19.95% CRAFTS

286. 1 0.07% 20.02% CRAZINESS

287. 1 0.07% 20.09% CREST

288. 1 0.07% 20.16% CRESTS

289. 1 0.07% 20.23% CRITICIZE

290. 1 0.07% 20.30% CROCODILE

291. 1 0.07% 20.37% CROCODILES

292. 1 0.07% 20.44% CROWED

293. 1 0.07% 20.51% CRUDE

294. 1 0.07% 20.58% CRUST

295. 1 0.07% 20.65% CUES

296. 1 0.07% 20.72% CUMBERSOME

297. 1 0.07% 20.79% CURRICULUM

298. 1 0.07% 20.86% CURVIER

299. 1 0.07% 20.93% CUTE

300. 1 0.07% 21.00% DADDY

301. 1 0.07% 21.07% DATABASE

302. 1 0.07% 21.14% DAUNTING

303. 1 0.07% 21.21% DEADLINE

304. 1 0.07% 21.28% DEADLINES

305. 1 0.07% 21.35% DEBILITATING

306. 1 0.07% 21.42% DEBRIS

307. 1 0.07% 21.49% DECEPTIVE

308. 1 0.07% 21.56% DECODE

309. 1 0.07% 21.63% DEDICATED

310. 1 0.07% 21.70% DEDICATION

311. 1 0.07% 21.77% DEFICIENCY

312. 1 0.07% 21.84% DEFICIENT

313. 1 0.07% 21.91% DEFIES

314. 1 0.07% 21.98% DEGRADATION

315. 1 0.07% 22.05% DELIBERATE

316. 1 0.07% 22.12% DELIBERATELY

317. 1 0.07% 22.19% DELICIOUS

318. 1 0.07% 22.26% DEMOCRACY

42

320. 1 0.07% 22.40% DENTIST

321. 1 0.07% 22.47% DEPARTURE

322. 1 0.07% 22.54% DEPICT

323. 1 0.07% 22.61% DEPICTED

324. 1 0.07% 22.68% DEPICTION

325. 1 0.07% 22.75% DEPOSIT

326. 1 0.07% 22.82% DEPOSITS

327. 1 0.07% 22.89% DESPERATE

328. 1 0.07% 22.96% DESSERT

329. 1 0.07% 23.03% DETERGENT

330. 1 0.07% 23.10% DETERGENTS

331. 1 0.07% 23.17% DEVASTATING

332. 1 0.07% 23.24% DEVISED

333. 1 0.07% 23.31% DEVISING

334. 1 0.07% 23.38% DIET

335. 1 0.07% 23.45% DIETS

336. 1 0.07% 23.52% DIGEST

337. 1 0.07% 23.59% DINOSAUR

338. 1 0.07% 23.66% DINOSAURS

339. 1 0.07% 23.73% DIPLOMAT

340. 1 0.07% 23.80% DISARRAY

341. 1 0.07% 23.87% DISASTER

342. 1 0.07% 23.94% DISASTERS

343. 1 0.07% 24.01% DISCARDED

344. 1 0.07% 24.08% DISCERNIBLE

345. 1 0.07% 24.15% DISCOMFORTS

346. 1 0.07% 24.22% DISCONSOLATE

347. 1 0.07% 24.29% DISCOUNT

348. 1 0.07% 24.36% DISCOUNTS

349. 1 0.07% 24.43% DISCOURSE

350. 1 0.07% 24.50% DISHWASHERS

351. 1 0.07% 24.57% DISPUTES

352. 1 0.07% 24.64% DISRUPTION

353. 1 0.07% 24.71% DISSERTATION

354. 1 0.07% 24.78% DISSIPATE

43

356. 1 0.07% 24.92% DIVINE

357. 1 0.07% 24.99% DIVORCE

358. 1 0.07% 25.06% DOCK

359. 1 0.07% 25.13% DOCUMENTARIES

360. 1 0.07% 25.20% DOLPHIN

361. 1 0.07% 25.27% DOLPHINS

362. 1 0.07% 25.34% DORMITORY

363. 1 0.07% 25.41% DOS

364. 1 0.07% 25.48% DOWNLOAD

365. 1 0.07% 25.55% DOWNLOADING

366. 1 0.07% 25.62% DOWNSIDE

367. 1 0.07% 25.69% DOWNWARD

368. 1 0.07% 25.76% DRAGON

369. 1 0.07% 25.83% DRAGONS

370. 1 0.07% 25.90% DRAINED

371. 1 0.07% 25.97% DRAWBACK

372. 1 0.07% 26.04% DRAWBACKS

373. 1 0.07% 26.11% DREADING

374. 1 0.07% 26.18% DRESSER

375. 1 0.07% 26.25% DROPLETS

376. 1 0.07% 26.32% DROUGHT

377. 1 0.07% 26.39% DRUGSTORE

378. 1 0.07% 26.46% DUCTS

379. 1 0.07% 26.53% DUDES

380. 1 0.07% 26.60% DYSLEXIA

381. 1 0.07% 26.67% DYSLEXIC

382. 1 0.07% 26.74% EARDRUMS

383. 1 0.07% 26.81% EATER

384. 1 0.07% 26.88% EATERS

385. 1 0.07% 26.95% EDIFICES

386. 1 0.07% 27.02% EFFICIENTLY

387. 1 0.07% 27.09% EGOMANIAC

388. 1 0.07% 27.16% ELECTIVE

389. 1 0.07% 27.23% ELECTIVES

390. 1 0.07% 27.30% ELECTRONIC

44

392. 1 0.07% 27.44% ELECTRONICS

393. 1 0.07% 27.51% ELEGANT

394. 1 0.07% 27.58% ELIGIBLE

395. 1 0.07% 27.65% EMAIL

396. 1 0.07% 27.72% EMBARRASS

397. 1 0.07% 27.79% EMBARRASSED

398. 1 0.07% 27.86% EMBOSSED

399. 1 0.07% 27.93% EMOTIONAL

400. 1 0.07% 28.00% EMOTIONS

401. 1 0.07% 28.07% EMPATHIZE

402. 1 0.07% 28.14% ENCYCLOPAEDIA

403. 1 0.07% 28.21% ENDINGS

404. 1 0.07% 28.28% ENDOSPORES

405. 1 0.07% 28.35% ENDURED

406. 1 0.07% 28.42% ENGAGEMENT

407. 1 0.07% 28.49% ENGAGING

408. 1 0.07% 28.56% ENGRAVED

409. 1 0.07% 28.63% ENGRAVING

410. 1 0.07% 28.70% ENGRAVINGS

411. 1 0.07% 28.77% ENGULFED

412. 1 0.07% 28.84% ENROL

413. 1 0.07% 28.91% ENROLMENT

414. 1 0.07% 28.98% ENTERPRISE

415. 1 0.07% 29.05% ENTERPRISING

416. 1 0.07% 29.12% ENTHUSIASTIC

417. 1 0.07% 29.19% EPHEMERA

418. 1 0.07% 29.26% EPICENTRE

419. 1 0.07% 29.33% EPICS

420. 1 0.07% 29.40% EPISODE

421. 1 0.07% 29.47% EQUILIBRIUM

422. 1 0.07% 29.54% ERA

423. 1 0.07% 29.61% ERADICATED

424. 1 0.07% 29.68% ERECTED

425. 1 0.07% 29.75% ERUPTION

426. 1 0.07% 29.82% ERUPTIONS

83

050. friend 3

051. guideline 3 052. important 3 053. influence 3 054. inform 3 055. let’ 3

056. literature 3 057. modern 3 058. own 3

059. recognize 3 060. says: 3 061. six 3 062. snoop 3 063. social 3 064. software 3 065. speak 3 066. such 3

067. technology 3 068. true 3

069. vocabulary 3 070. vowel 3 071. you’re 3 072. abandon 2 073. affect 2 074. another 2 075. apply 2 076. appropriate 2

077. aware 2 078. benefit 2 079. bible 2 080. call 2 081. card 2 082. cause 2 083. century 2 084. change 2 085. common 2 086. company 2 087. conquest 2 088. consequence 2

089. consider 2 090. credit 2 091. deal 2

092. difference 2 093. edit 2

094. educate 2 095. fair 2 096. familiar 2 097. four 2 098. hurt 2 099. increase 2 100. instance 2 101. issue 2 102. keen 2 103. might 2 104. much 2 105. music 2 106. nevertheless

049. good 7 050. know 7 051. really 7 052. still 7 053. well 7 054. any 6 055. learn 6 056. out 6 057. right 6 058. there 6 059. day 5 060. father 5 061. first 5 062. from 5 063. into 5 064. like 5 065. need 5 066. would 5 067. easy 4 068. help 4 069. here 4 070. long 4 071. move 4 072. next 4 073. power 4 074. should 4 075. student 4

076. take 4 077. who 4 078. world 4 079. year 4 080. also 3 081. carry 3 082.

excellent 3 083. guess 3 084. matter 3 085. or 3 086. other 3 087. place 3 088. see 3 089. start 3 090. then 3 091. thing 3 092. three 3 093. up 3 094. want 3 095. without 3

096. word 3 097. begin 2 098. each 2 099. even 2 100. ever 2 101. family 2 102. final 2 103. five 2 104. give 2

048. care 3 049. confidence 3

050. escape 3 051. feel 3 052. guy 3 053. happy 3 054. hullo 3 055. kick 3 056. laugh 3 057. must 3 058. night 3 059. plan 3 060. small 3 061. smile 3 062. snake 3 063. soon 3 064. stay 3 065. tough 3 066. when 3 067. academy 2 068. armour 2 069. art 2 070. awe 2 071. blow 2 072. clan 2 073. coat 2 074. compassion 2

075. control 2 076. courage 2 077. dad 2 078. defy 2 079. deny 2 080. discipline 2

081. dragon 2 082. east 2 083. fable 2 084. face 2 085. fall 2 086. famous 2 087. festival 2 088. fist 2 089. furious 2 090. gang 2 091. gorilla 2 092. leg 2 093. luck 2 094. lunch 2 095. map 2 096. mighty 2 097. miss 2 098. month 2 099. oh 2 100. old 2 101. only 2 102. play 2 103. please 2

047. clean 1 048. coat 2 049. compassion 2

050. complete 1 051. confidence 3

052. control 2 053. cool 8 054. cotton 1 055. courage 2 056. cower 1 057. crazy 1 058. crocodile 5 059. cry 1

060. cute 1 061. dad 2 062. dance 7 063. danger 1 064. daughter 1 065. dead 1 066. dear 1 067. death 1 068. deceptive 1 069. defeat 1 070. defy 2 071. delight 1 072. deny 2 073. descend 1 074. disappoint 1

075. discipline 2

076.

disconsolate 1 077. distil 1 078. dragon 2 079. duck 1 080. dude 1 081. east 2 082. eat 1 083. either 1 084. escape 3 085. every 5 086. evil 1 087. fable 2 088. face 2 089. fail 1 090. fall 2 091. fame 1 092. famous 2 093. fang 7 094. fanged 1 095. far 1 096. fashion 1 097. fast 7 098. feel 3 099. ferocious 1 100. festival 2

84

2

107. paper 2 108. password 2 109. person 2 110. pick 2 111. poet 2 112. policy 2 113. proper 2 114. reflect 2 115. research 2 116. resource 2 117. rich 2 118. shift 2 119. shouldn’ 2 120. spell 2 121. term 2 122. utilise 2 123. we’ll 2 124. yet 2 125. able 1 126. access 1 127. account 1 128. across 1 129. adapt 1 130. advance 1 131. alter 1 132. aristocrat 1 133. available 1 134. bachelor 1 135. background 1 136. bank’ 1

137. base 1 138. behaviour 1 139. belong 1 140. between 1 141. bilingual 1 142. billion 1 143. blend 1 144. boil 1 145. book 1 146. borrow 1 147. boundary 1 148. broad 1 149. business 1 150. butler 1 151. career 1 152. certain 1 153. chapter 1 154. classic 1 155. clergy 1 156. clerk 1 157. coin 1 158. college 1 159. comedy 1 160. comment 1 161. commerce 1 162. concept 1 163. connect 1 164. conquer 1

105. happen 2 106. hard 2 107. hold 2 108. idea 2 109. new 2 110. off 2 111. over 2 112. point 2 113. say 2 114. school 2 115. simple 2 116. sit 2 117. skill 2 118. stop 2 119. teach 2 120. use 2 121. very 2 122. way 2 123. week 2 124. where 2 125. work 2 126. yes 2 127. always 1 128. ask 1 129. back 1 130. because 1

131. become 1 132. break 1 133. by 1 134. class 1 135. decide 1 136. down 1 137. enough 1 138. enter 1 139. full 1 140. hand 1 141. job 1 142. letter 1 143. live 1 144. look 1 145. may 1 146. mean 1 147. meaning 1

148. mention 1

149. middle 1 150. more 1 151. part 1 152. pay 1 153. possible 1

154. remember 1

155. second 1 156. seem 1 157. send 1 158. sense 1

104. proof 2 105. rhino 2 106. ribbon 2 107. sad 2 108. self 2 109. show 2 110. slow 2 111. terrible 2 112. thank 2 113. tiny 2 114. tip 2 115. touch 2 116. treat 2 117. tremble 2 118. until 2 119. warn 2 120. win 2 121. wool 2 122. actual 1 123. adopt 1 124. adult 1 125. after 1 126. almost 1 127. around 1 128. attend 1 129. beak 1 130. bear 1 131. beast 1 132. behold 1 133. beneath 1 134. bit 1 135. blossom 1 136. boat 1 137. booth 1 138. bottom 1 139. brilliant 1 140. bug 1

141. build 1 142. bummer 1 143. challenge 1 144. chance 1 145. check 1 146. clean 1 147. complete 1 148. cotton 1 149. cower 1 150. crazy 1 151. cry 1 152. cute 1 153. danger 1 154. daughter 1 155. dead 1 156. dear 1 157. death 1 158. deceptive 1 159. defeat 1 160. delight 1 161. descend 1 162. disappoint

101. fight 5 102. fine 4 103. finger 1 104. finish 1 105. fist 2 106. flag 1 107. flame 1 108. floor 1 109. focus 1 110. foot 1 111. force 1 112. forget 1 113. freeze 1 114. fun 1 115. fur 1 116. furious 2 117. game 1 118. gang 2 119. generate 1 120. girl 1 121. gorilla 2 122. grab 1 123. grow 1 124. guy 3 125. half 1 126. happy 3 127. hat 1 128. heart 1 129. hero 1 130. hill 1 131. hit 1 132. home 1 133. hullo 3 134. hunger 1 135. impatient 1 136. indeed 1 137. inspire 1 138. instead 1 139. introduce 1 140. invincible 1

141. island 1 142. ivory 1 143. janitor 1 144. jerk 1 145. joy 1 146. just 9 147. keep 1 148. kick 3 149. knockout 1 150. laugh 3 151. leave 7 152. leg 2 153. legacy 1 154. legend 1 155. lesson 1 156. let 7

157. lightning 1 158. literal 1

85

165. consequent 1

166. consist 1 167. constrain 1 168. contemporary 1

169. content 1 170. couple 1 171. courier 1 172. culture 1 173. curriculum 1 174. damage 1 175. database 1 176. daunt 1 177. decade 1 178. define 1 179. devise 1 180. dictionary 1 181. didn’ 1

182. die 1

183. difficult 1 184. diplomat 1 185. discuss 1 186. divine 1 187. doesn’ 1 188. dollar 1 189. drop 1 190. during 1 191. ear 1 192. early 1 193. emerge 1 194. end 1 195. enforce 1 196. engage 1 197. enjoy 1 198. envelope 1 199. eventual 1 200. experience 1 201. explain 1 202. fact 1 203. few 1 204. foreign 1 205. foreignness 1

206. formal 1 207. fortune 1 208. founded 1 209. frame 1 210. free 1 211. gain 1 212. general 1 213. goal 1 214. graduate 1 215. grammar 1 216. group 1 217. hank 1 218. harm 1 219. hear 1 220. high 1 221. honour 1

159. set 1 160. shall 1 161. since 1 162. steal 1 163. step 1 164. strong 1 165. study 1 166. style 1 167. sure 1 168. talk 1 169. town 1 170. turn 1 171. two 1 172. young 1

1 163.

disconsolate 1 164. distil 1 165. duck 1 166. dude 1 167. eat 1 168. either 1 169. evil 1 170. fail 1 171. fame 1 172. fanged 1 173. far 1 174. fashion 1 175. ferocious 1 176. finger 1 177. finish 1 178. flag 1 179. flame 1 180. floor 1 181. focus 1 182. foot 1 183. force 1 184. forget 1 185. freeze 1 186. fun 1 187. fur 1 188. game 1 189. generate 1 190. girl 1 191. grab 1 192. grow 1 193. half 1 194. hat 1 195. heart 1 196. hero 1 197. hill 1 198. hit 1 199. home 1 200. hunger 1 201. impatient 1 202. indeed 1 203. inspire 1 204. instead 1 205. introduce 1 206. invincible 1

207. island 1 208. ivory 1 209. janitor 1 210. jerk 1 211. joy 1 212. keep 1 213. knockout 1 214. legacy 1 215. legend 1 216. lesson 1 217. lightning 1 218. literal 1

159. longing 1 160. luck 2 161. lunch 2 162. map 2 163. marvel 1 164. master 10 165. match 1 166. maybe 1 167. meditate 1 168. mid 1 169. mighty 2 170. mind 4 171. miss 2 172. monster 7 173. month 2 174. moon 1 175. mouth 1 176. must 3 177. muster 1 178. name 1 179. never 1 180. night 3 181. none 1 182. oh 2 183. old 2 184. once 1 185. only 2 186. orphanage 4 187. ounce 1 188. pack 1 189. pain 1 190. pants 5 191. pathetic 1 192. patient 4 193. plan 3 194. play 2 195. please 2 196. poison 5 197. poke 1 198. poor 1 199. potent 1 200. precise 1 201. pretty 1 202. problem 1 203. proof 2 204. protect 1 205. quick 1 206. quite 1 207. ready 4 208. red 1 209. remain 1 210. require 1 211. rhino 2 212. ribbon 2 213. run 1 214. sad 2 215. savage 1 216. save 1 217. scaly 1

86

222. hotel 1

223. humour 1 224. hybrid 1 225. include 1 226. instruct 1 227. interfere 1 228. interpret 1 229. isn’ 1

230. journey 1 231. judge 1 232. kill 1 233. king 1 234. knight 1 235. large 1 236. lead 1 237. lecture 1 238. legal 1 239. less 1 240. liberal 1 241. likely 1 242. listen 1 243. lot 1 244. main 1 245. major 1 246. marry 1 247. message 1 248. model 1 249. moral 1 250. morning 1 251. mother 1 252. narrate 1 253. negative 1 254. news 1 255. observe 1 256. official 1 257. open 1 258. origin 1 259. outstanding 1

260. overstate 1 261. parachute 1 262. particular 1 263. past 1

264. percent 1 265. permit 1 266. phrase 1 267. pilgrim 1 268. plagiarise 1 269. practical 1 270. practise 1 271. precede 1 272. presence 1 273. present 1 274. pretend 1 275. previous 1 276. primary 1 277. principles: 1

278. privacy 1

219. longing 1 220. marvel 1 221. match 1 222. maybe 1 223. meditate 1 224. mid 1 225. moon 1 226. mouth 1 227. muster 1 228. name 1 229. never 1 230. none 1 231. once 1 232. ounce 1 233. pack 1 234. pain 1 235. pathetic 1 236. poke 1 237. poor 1 238. potent 1 239. precise 1 240. pretty 1 241. problem 1 242. protect 1 243. quick 1 244. quite 1 245. red 1 246. remain 1 247. require 1 248. run 1 249. savage 1 250. save 1 251. scaly 1 252. seat 1 253. settle 1 254. shake 1 255. size 1 256. smack 1 257. sorry 1 258. starve 1 259. state 1 260. steady 1 261. strength 1 262. summon 1 263. supply 1 264. sweep 1 265. tactic 1 266. tail 1 267. tame 1 268. temper 1 269. test 1 270. thunder 1 271. tigress 1 272. ton 1 273. tooth 1 274. top 1 275. toward 1 276. tradition 1 277. trance 1

218. seat 1 219. secret 8 220. self 2 221. settle 1 222. shake 1 223. show 2 224. size 1 225. skinny 4 226. slow 2 227. smack 1 228. small 3 229. smile 3 230. snake 3 231. soon 3 232. sorry 1 233. starve 1 234. state 1 235. stay 3 236. steady 1 237. strength 1 238. summon 1 239. supply 1 240. sweep 1 241. tactic 1 242. tail 1 243. tame 1 244. technique 4 245. temper 1 246. terrible 2 247. test 1 248. than 4 249. thank 2 250. thunder 1 251. tigress 1 252. tiny 2 253. tip 2 254. ton 1 255. too 7 256. tooth 1 257. top 1 258. total 9 259. touch 2 260. tough 3 261. toward 1 262. tradition 1 263. train 5 264. trance 1 265. trap 4 266. treat 2 267. tremble 2 268. trouble 1 269. twenty 1 270. uncool 1 271. unless 1 272. until 2 273. valley 1 274. venom 4 275. village 5 276. viper 1

87

279. probable 1

280.

pronunciation 1 281. public 1 282. question 1 283. real 1 284. regard 1 285. relation 1 286. report 1 287. responsible 1

288. rhyme 1 289. royal 1 290. same 1 291. schoolmen 1 292. sentence 1 293. separate 1 294. serious 1 295. service 1 296. seven 1 297. significant 1

298. situate 1 299. society 1 300. soldier 1 301. sort 1 302. sound 1 303. source 1 304. standard 1 305. store 1 306. succeed 1 307. suppose 1 308. surprise 1 309. syllable 1 310. system 1 311. talent 1 312. teenage 1 313. text 1 314. that’ 1 315. there’ 1 316. they’re 1 317. thirty 1 318. thousand 1 319. timeline 1 320. topic 1 321. travel 1 322. under 1 323. understand 1 324. university 1 325. vernacular 1 326. version 1 327. virus 1 328. website 1 329. websites 1 330. what’ 1 331. while 1 332. wide 1 333. worldwide 1 334. worth 1

278. trouble 1 279. twenty 1 280. uncool 1 281. unless 1 282. valley 1 283. viper 1 284. wake 1 285. wear 1 286. wee 1 287. west 1 288. wolf 1 289. wood 1 290. worry 1

277. wait 15 278. wake 1 279. warn 2 280. warrior 4 281. wear 1 282. wee 1 283. west 1 284. when 3 285. why 5 286. win 2 287. wolf 1 288. wood 1 289. wool 2 290. worry 1

88

335. york 1