Can we learn English from movies? A text analysis

Graduation ThesisPresented to

the Faculty of the Department ofEnglish Language and LiteratureNotre Dame Seishin University

In Partial Fulfillmentof the Requirement for the Degree

Bachelor of Arts

byMadoka Hioka

2015

1

Contents

Chapter One: Introduction

1.1 Introduction

1.2 Learning English through movies

1.2.1 Listening skills

1.2.2 Speaking skills

1.2.3 Vocabulary

1.2.4 Summary

1.3 Ways to assess whether students can learn English from

movies

1.3.1 Listen and test

1.3.2 Interviews

1.3.3 Text analysis

1.4 Analyzing language texts

1.4.1 What coverage rate is best for language learning from

movies?

1.4.2 How to analyze texts

1.5 Previous research on text analysis of movies

1.6 What is the vocabulary size of Japanese students?

1.7 Research Questions

1.8 Conclusion

Chapter Two: The movie analysis

2.1 Introduction

2.2 The experiments

2.2.1 Introduction to the experiment

2.2.2 Method

2.2.2.1 The texts

2.2.2.2 The analyzing the text

2.3 Results

2.3.1 Coverage

2.3.2 Vocabularies

2.4 Summary

Chapter Three: Discussion

3.1 Introduction

3.2 Summary of the results

3.3 Discussion of the results

3.4 Answering the research questions

3.5 Implications from the data

3.6 Limitations of the Experiment

3.7 Further research

3.8 Conclusion

References

Appendices

ii

Abstract

Many researchers said that movies are good way to learn English

especially to improve speaking skills, listening skills, vocabulary

knowledge. This thesis found out it is true or not in the lexical point of

view. 16 movies’ script were downloaded from the Internet and edited to

analyze texts. Texts were analyzed using AntWord profiler (Anthony,

2013) which determines the frequency of each word. The comparison

wordlist was based on the Extensive Reading Foundation scale to

determine the percentage of words at each headword level.

Previous research said that leaners need 95%-98% coverage of

movies to be able to guess the meaning of an unknown word successfully.

The aim of this study was to find out how many words do learners need to

reach 95% or 98%.

The results of text analysis showed that learners would have only

93.13% coverage of one movie of average of all 16 movies if learners

know all 2,500 words from level 1 to level 16. Looking at all movies one by

one, the only 4 movies could reach 95% coverage. Learners could know

about over 60% of 750 words which are from level 1 to level 6 if they

watch all 16 movies. In terms of all 16 levels, they could be known 38% of

2,500 words on the same condition. The top 25 highest ranking words

used in the 16 movies were shown in this study. Those words are also

frequent in daily life.

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In this study, we found that we can’t say learning English from

movies are useful for everyone because the difficulty is depend on

learners’ knowledge of vocabularies. Learners who have high knowledge

of the words could guess the meaning of the words from the movies’

contexts with pictures.

iv

Chapter 1: Learning English from movies 1.1 Introduction

Japan is the country in which most people only need Japanese and

we don’t need to use other languages like English, French, or Spanish to

live in Japan. Some countries, however, have more than one official

language. For example, the official languages in Canada are English and

French, and in Belgium are French, Dutch, and German. Therefore,

Japanese students find it hard to realize the necessity of learning other

languages even though the Course of Study in Japan has determined that

English is as a compulsory subject from fifth grade elementary school

since the school year of 2011.

Braj Kachru advocated a circle of World Englishes which are shown

in concentric circle (see below). The inner circle represents the countries

where people originally speak English as

mother tongue such as United States of

America, United Kingdom, Australia, and

Canada. The outer circle stands for the

countries where many people learn and speak

English as lingua franca such as India, Nigeria,

and Malaysia. The expanding circle shows the

countries where people have their own language and learn English as a

foreign language such as Japan, Russia, China, and Korea.

1

Nowadays English is learned by many people all over the world as

the universal language. We can communicate with foreigners, collect

information from other countries, have fun through using English if we

learn English and use it. It feels like our world has expanded.

There are many ways to learn English in the world. For example,

reading books, listening to music, watching movies, etc. All these ways to

learn English have good point of something. This thesis is going to focus

on learning English through movies.

Many people say watching movies is a great way to improve the

English skills, especially listening and speaking skills. Movies are made for

native speakers not English learners and also they made these movies it

gives English learners opportunities to hear natural spoken English. This

means it may be possible to learn a lot of new words and expressions

from movies. This thesis will explore this question.

1.2. Learning English through movies

In Japan we often hear people claim that watching movies is a great

way to learn English. Ding (2007, p. 275) found that among the

participants in his study, all eight of the prize winners in national level

speech contents in China regarded watching movies and television series

as one of the most effective ways of improving their English. We often

hear about this on the internet and in classrooms too.

1.2.1 Listening skills

One advantage of movies is that learners can hear natural way to

speak English through movies. They can meet English the natural speed of

speaking by native speakers and help learners get used to hearing it. And

2

also learners can hear informal words or slang they cannot find in books

or dictionaries.

1.2.2 Speaking skills

Learners can improve their fluency of speaking. It helps learners to

learn how to put the words together and how to pronounce them. English

tends to link the words when they pronounce and so this is a good

opportunity to hear it in practice. Learners hear them and practice

mimicking it many times.

1.2.3 Vocabulary

Japanese English learners tend to learn new vocabularies and their

spelling with vocabulary lists in their school but it’s good to remember the

meaning and spelling but is not good for how to use the words. It should

be assumed that learning from a word list or word cards means that the

words are learned forever, nor does it mean that all knowledge of a word

has been learned (Paul and Rob However, learners can find many new

words or phrases, especially idioms or collocations through watching

movies. Movies are really helpful to understand and remember these

words because of the context. Learners get better understanding how to

use these words like when they are happy, surprised, sad, or angry

through the contexts of watching interesting and engaging movies.

1.2.4 Summary

In this section we have seen many claims for the idea that learning

from movies is very powerful, however there has been little or no research

3

to validate that this is actually the case. Is this true or not? We are going

to find out.

1.3 Ways to assess whether students can learn English from

movies

Above we saw the claims for the power of learning from movies, but

this is not yet verified and needs to be assessed. This section will discuss

the ways to measure uptake from watching movies.

1.3.1 Listen and test

Students watch movies and then take tests which ask the context of

movies, or dictation or even test their vocabulary knowledge. We can

know whether students can listen and understand what the characters

said properly.

1.3.2 Interviews

One way to assess if learners are learning from movies is we can

conduct interviews to ask questions such as “How did you feel learning

English from movies?”, “Did you like the movie or not? and Why?”, and

“Did you think you could learn something from movies?” after watching

movies.

1.3.3 Text analysis

This involves analyzing the script from the movie with software to

find out what words are in the texts. This allows us to generate a profile of

4

the percentage of high and low frequency words. If there are many low

frequency words it means the movie is likely to be difficult. If we also

know the likely acquisition uptake rate (see below) we can then ascertain

the likelihood of whether the movie will be in the right coverage range

which can lead to uptake.

1.4 Analyzing language texts

A different and standard way to find out how difficult a text is, is to

analyze the vocabulary in it. Software programs can count how many

times each word appears in the text to tell us

a) how many words were used in each text and as a total

b) which words were used and how many times

The data from this can be analyzed to find out how difficult a text is. If

there are a lot of high frequency words it is likely that the movie will be

easy, but if there are many low frequency words, it will make the movie

hard to understand.

The text analysis can also tell us how easy it is for a student to learn

new words from the movies. Research shows that students need to

a) meet each words many times before it is learnt

b) know about 98% of the surrounding text for the student to be able

to have a high chance of guessing an unknown word successfully.

1.4.1 What coverage rate is best for language learning from

movies?

5

Before we can ascertain whether students can understand a movie,

we need to know how many of the words they need to know to actually

understand it. Is it 80%, 90% or more? Brown, Waring, Donkaewbua

(2008) compared three types of input methods to know which one is good

for learning more English vocabulary, which are reading, reading-while-

listening, and listening. Subjects were tested and they answered by

multiple choice and translation. The result showed that reading-while-

listening was the best way to learn vocabulary. It also showed that only

listening was much harder than reading or reading-while- listening.

This suggests that students listening to movies will need a higher

level of coverage than the 95% or 98% often stated for reading. This level

might be 98% or 99% of the words met in the movies to be known for

fluent understanding. If the subject knows fewer than 98-99% of the

words they meet, then they may not pick up much and thus very little will

be learnt. We are assuming here they are only watching the movie not

also reading the subtitles in English.

However, a difference between listening to a book and watching a

video is that a lot of the contents of a video are visual rather than

auditory. This means that it’s likely a lower percentage than 99% might be

suitable for comprehension. This has not been researched until the

present time but it may be a rate similar to that of reading - possibly 95-

98%.

1.4.2 How to analyze texts

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The texts were put into a software program called AntConc

(Anthony, 2013) which determines the frequency of each word against a

wordlist. The comparison wordlist was based on the 20-level Extensive

Reading Foundation scale (www.erfoundation .org ) to determine what

percentages of words at each headword level were. It can determine the

number of words in each text outside the 95% and 98% coverage rates

needed for fluent comprehension.

1.5 Previous research on text analysis of movies

Webb and Rodgers (2009) analyzed 318 movies which have

2,841,887 running words to find out the vocabulary size that need to

comprehend 95% and 98% of the words in movies. The movies

distinguished between American and British English, and divided into 11

genres; action, animation, comedy, suspense/crime, drama, horror,

romance, science fiction, war, western, and classic.

The result showed that learners were likely to understand 95.76% of

the words in movies if they know the most frequent 3,000 word families

plus proper nouns and marginal words, and they are likely to understand

98.15% of the words in movies if they know the most frequent 6,000 word

families plus proper nouns and marginal words.

The results of 95% coverage of American and British movies were

the same as above, but the results of that of 98% have the gap of 1,000

words.

Looking at the genres, the results have a wide range. Learners need to

7

understand 95% of the words in movies, which provide 3,000 to 4,000

word families plus proper nouns and marginal words, and 98% of the

words in movies, which provide 5,000 to 10,000 word families plus proper

nouns and marginal words.

1.6 What is the vocabulary size of Japanese students?

Research shows that the average Japanese college student knows

about 2,000 words by the time they graduate from high school. The

question is whether this is enough words to be able to cover 95% or 98%

of the words in movies?

1.7 Research Questions

In the next chapter, we will find out the answers to the following

three research questions by analyzing the texts.

1. How many words do learners need when the coverage of movies

reach 95% or 98%?

2. Are there any differences from age or ability?

3. What words were used in the movies at each level?

1.8 Conclusion

In this chapter, we looked at the advantage of learning English

through movies, word coverage of movies, and analyzing text. In the next

chapter, we will look at the experiment to find out watching movies are

8

good for learning English by analyzing text.

9

Chapter 2: The Movie Analysis

2.1 Introduction

In Chapter One, we looked at learning English from movies from

various points of view. In this chapter, we are going to look at how to

analyze the texts more detail, and the results of analyzing texts to see if

indeed typical college age English majors can learn much new language

from watching movies without subtitles.

2.2 The experiment

2.2.1 Introduction to the experiment

16 movie scripts were edited and analyzed to find out what words

were used at each level in each movie and what vocabulary size learners

would need to reach 95% or 98% coverage of movies to understand the

whole movie.

2.2.2 Method

This section explains the method of the text analysis.

2.2.2.1 The texts

The 16 texts were downloaded from some sites on the Internet. All

of movies were English language movies which were produced by Walt

Disney Animation Studios and Pixar Animation Studios;

Snow White and the Seven Dwarfs (1937),

Pinocchio (1940),

Cinderella (1950),

10

Alice in Wonderland (1951),

Peter Pan (1953),

Sleeping Beauty (1954),

The Little Mermaid (1989),

Beauty and the Beast (1991),

Aladdin (1992),

The Lion King (1994),

Toy Story (1995),

Finding Nemo (2003),

Cars (2006),

Enchanted (2007),

Tangled (2010),

Frozen (2013).

2.2.2.2 The analyzing the text

Words which were not spoken in the movies such as stage

commands, story line, and speaker’s names were removed from the

scripts. Only the spoken words in the movies were analyzed.

The number of times each word was used was counted using AntWord

Profiler (Anthony, 2013) and put against several wordlists from 200

headwords going up to 2500 headwords. The aim was to see what

percentage of the words in the movies come from various frequency

bands. If most of the words are high frequency ones then it means the

movies should be easy to understand. Alternatively, if there are many low

frequency words it means the movies are difficult. In Chapter One we

11

showed that students need to know about 95-98% of the surrounding

words to be able to guess the meaning of an unknown word successfully.

However, the raw texts were not clean in the sense that they had

names, narration excerpts and so on (in pink below) which needed to be

removed before analysis. Only the spoken words from the movies were

analyzed. Contractions and connected speech were changed to the form.

For example, wanna, gotta, and somethin’ were changed want to, have to,

and something.

Below is an example edit from Aladdin. The items in pink were removed.

ALADDIN: THE COMPLETE SCRIPT COMPILED BY BEN SCRIPPS

<34RQNPQ@CMUVM.CSV.CMICH.EDU> (Portions Copyright (c) 1992

The Walt Disney Company)

PEDDLER: Oh I come from a land From a faraway place Where the

caravan camels roam Where they cut off your ear /Where it's flat

and immense If they don't like your face /And the heat is intense It's

barbaric, but hey--it's home! When the wind's at your back And the

sun's from the west And the sand in the glass is right Come on

down, Stop on by Hop a carpet and fly To another Arabian night!

Arabian nights Like Arabian days More often than not Are hotter

than hot In a lot of good ways

12

Arabian nights 'Neath Arabian moons A fool off his guard Could fall

and fall hard Out there on the dunes.

Ah, Salaam and good evening to you worthy friend. Please, please,

come closer--(Camera zooms in hitting peddler in face) Too close, a

little too close. (Camera zooms back out to CU)There.Welcome to

Agrabah. City of mystery, of enchantment, and the finest

merchandise this side of the river Jordan, on sale today, come on

down! Heh, heh. Look at this! Yes! Combination hookah and coffee

maker--also makes Julienne fries. Will not break (taps it on table),

will not--(it falls apart)--it broke. Ooohhh! Look at this! (Pulls out

Tupperware) I have never seen one of these intact before. This is

the famous Dead Sea Tupperware. Listen. (Pries it open, makes

raspberry sound.) Ah, still good. (Camera begins to pan to right.

PEDDLER hurries to catch it.) Wait, don't go! (Stop pan.) I can see

that you're only interested in the exceptionally rare. I think then,

you would be most rewarded to consider...this. (PEDDLER pulls the

MAGIC LAMP out from his sleeve.) Do not be fooled by its

commonplace appearance. Like so many things, it is not what is

outside, but what is inside that counts. (Another pan, this one

slower to left. Again, PEDDLER rushes to catch up.) This is no

ordinary lamp! It once changed the course of a young man's life. A

young man who liked this lamp was more than what he seemed. A

diamond in the rough. Perhaps you would like to hear the tale?

(PEDDLER pours shiny sand from the lamp into his hand.) It begins

on a dark night (PEDDLER throws sand into the sky, where it forms a

13

starry nightscape.) , where a dark man waits, with a dark purpose.

(Camera tilts down to find JAFAR sitting on his horse and IAGO on his

shoulder. GAZEEM comes riding up to the pair.)

JAFAR: You...are late. GAZEEM:A thousand apologies, O patient one.

JAFAR: You have it, then? GAZEEM:I had to slit a few throats to get

it. (Pulls out half of the medallion. JAFAR reaches out for it, but

GAZEEM yanks it back.) Ah, ah, ahhh! The treasure! (IAGO

squawks as he flies by and grabs the medallion.) Ouch! JAFAR:

Trust me, my pungent friend. You'll get what's coming to you. IAGO:

What's coming to you! Awk!

(JAFAR pulls out the second half of the medallion. He connects

them, and the insect medallion begins to glow. Finally, it flies out of

JAFAR's hand, scaring the horses, and is off towards the dunes.)

JAFAR: Quickly, follow the trail!

After edit

Oh I come from a land From a faraway place Where the caravan

camels roam Where they cut off your ear /Where it's flat and

immense If they don't like your face /And the heat is intense It's

barbaric, but hey--it's home! When the wind's at your back And the

sun's from the west And the sand in the glass is right Come on

14

down, Stop on by Hop a carpet and fly To another Arabian night!

Arabian nights Like Arabian days More often than not Are hotter

than hot In a lot of good ways

Arabian nights 'Neath Arabian moons A fool off his guard Could fall

and fall hard Out there on the dunes.

Ah, Salaam and good evening to you worthy friend. Please, please,

come closer--Too close, a little too close. There. Welcome to

Agrabah. City of mystery, of enchantment, and the finest

merchandise this side of the river Jordan, on sale today, come on

down! Heh, heh. Look at this! Yes! Combination hookah and coffee

maker--also makes Julienne fries. Will not break, will not--it broke.

Ooohhh! Look at this! I have never seen one of these intact before.

This is the famous Dead Sea Tupperware. Listen. Ah, still good.

Wait, don't go! I can see that you're only interested in the

exceptionally rare. I think then, you would be most rewarded to

consider...this. Do not be fooled by its commonplace appearance.

Like so many things, it is not what is outside, but what is inside that

counts. This is no ordinary lamp! It once changed the course of a

young man's life. A young man who liked this lamp was more than

what he seemed. A diamond in the rough. Perhaps you would like

to hear the tale? It begins on a dark night, where a dark man waits,

with a dark purpose.

You...are late.

15

A thousand apologies, O patient one.

You have it, then?

I had to slit a few throats to get it.

Ah, ah, ahhh! The treasure!

Ouch!

Trust me, my pungent friend. You'll get what's coming to you.

What's coming to you! Awk!

Quickly, follow the trail!

2.3 Results

2.3.1 Coverage

This section will show the number of running words of the texts at each of

the 16 levels.

16

Table 1: The total number of words in the 16 movie scripts by level

Level Number of headwords

(cumulative)

Totals

1 100 (100) 54,7362 100 (200) 12,4523 200 (400) 15,8794 100 (400) 8,1205 100 (600) 3,4786 150 (750) 3,2507 150 (900) 2,4318 150 (1,050) 1,4929 150 (1,200) 1,43310 150 (1,350) 98711 200 (1,550) 1,16512 200 (1,750) 1,11313 150 (1,900) 60314 200 (2,100) 80315 200 (2,300) 75516 200 (2,500) 605

Out of level 8,065Total 117,367

Table 1 shows that the total number of words in the 16 movies’

scripts by level. It didn’t included proper names in movies because people

can know that it’s name of the character while they listen to it many

times. Neither does the above include words outside these 2,500

headwords.

17

Table 2 shows the percentage of words from that level which

appeared in the movies.

Table 2: The average coverage rate for the 16 movie scripts

Level Number of headwords

(cumulative)

Rate Cumulative rate

1 100 (100) 46.64% 46.64%2 100 (200) 10.61% 57.25%3 200 (400) 13.53% 70.78%4 100 (400) 6.92% 77.69%5 100 (600) 2.96% 80.66%6 150 (750) 2.77% 83.43%7 150 (900) 2.07% 85.50%8 150 (1,050) 1.27% 86.77%9 150 (1,200) 1.22% 87.99%10 150 (1,350) 0.84% 88.83%11 200 (1,550) 0.99% 89.82%12 200 (1,750) 0.95% 90.77%13 150 (1,900) 0.51% 91.29%14 200 (2,100) 0.68% 91.97%15 200 (2,300) 0.64% 92.61%16 200 (2,500) 0.52% 93.13%

Out of level

6.87% 100.00%

Table 2 shows that the average coverage rate for the 16 movie

scripts. 46.64% of the running words of 16 movies could be understood if

18

learners know the words of the level 1. 57.25% of that could be

understood if learners know the words of the level 1 and 2. Level 16

provides 93.13% coverage of 16 movies. That is if the subjects have a

listening vocabulary of 2,500 words, they would know 93.13% of the

words in these movies not including proper nouns.

19

Table 3: The coverage cumulative rate for the 16 movie scripts

leve

l

Alad

din

Alice

in

Won

derl

and

Beau

ty

and

the

Beas

t

Cars

Cind

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la En

chan

ted Fr

ozen

Find

ing

Nem

o

Pete

r Pa

n

Pino

cchi

o Slee

ping

Be

auty

Snow

W

hite

Tang

led

The

Lion

king

The

Little

m

erm

aid

Toy

Stor

y

145.81

%44.34

%47.63

%47.60

%44.05

%50.47

%45.82

%49.86

%43.02

%48.29

%41.91

%43.84

%44.14

%48.27

%40.99

%45.98

%

255.09

%56.04

%58.00

%58.06

%54.84

%61.81

%58.21

%60.42

%52.69

%58.34

%53.04

%53.23

%55.30

%58.82

%50.20

%56.96

%

368.20

%70.31

%72.11

%70.64

%68.58

%75.10

%72.27

%73.99

%66.61

%72.90

%67.80

%67.88

%68.33

%70.05

%63.47

%70.79

%

475.22

%76.71

%79.50

%77.34

%75.82

%82.17

%79.30

%79.89

%73.24

%80.28

%74.84

%75.75

%76.46

%77.49

%69.79

%76.61

%

578.28

%79.95

%82.11

%80.02

%79.32

%85.80

%82.22

%81.79

%76.14

%83.48

%78.32

%78.61

%80.48

%80.14

%72.55

%79.39

%

681.21

%82.52

%84.94

%82.26

%81.49

%88.51

%85.81

%84.35

%79.54

%86.04

%81.04

%81.02

%84.08

%82.89

%75.79

%81.96

%

783.24

%84.72

%87.13

%84.20

%83.99

%90.65

%88.15

%85.87

%81.65

%87.65

%84.36

%82.71

%86.45

%84.84

%77.80

%83.95

%

884.45

%85.93

%88.81

%85.41

%84.87

%91.79

%89.78

%86.75

%82.88

%89.10

%85.87

%84.28

%87.68

%85.81

%78.97

%85.37

%

985.65

%87.41

%89.97

%86.73

%85.92

%92.66

%90.91

%87.63

%83.59

%90.46

%87.52

%84.95

%88.56

%87.58

%79.65

%87.67

%

1086.61

%88.39

%90.94

%87.72

%86.66

%93.24

%91.61

%88.29

%84.80

%91.71

%87.96

%85.50

%89.42

%88.52

%80.81

%88.47

%

1187.82

%89.58

%92.06

%88.31

%87.40

%94.11

%92.67

%88.96

%86.02

%92.54

%89.82

%87.95

%90.47

%89.09

%81.96

%89.29

%

1289.73

%91.23

%92.69

%89.03

%88.74

%94.90

%93.44

%89.59

%86.71

%93.09

%91.61

%88.83

%91.40

%89.77

%82.62

%90.02

%

1390.40

%91.62

%93.12

%89.71

%89.23

%95.05

%94.00

%90.07

%87.96

%93.34

%91.98

%89.46

%91.81

%90.21

%83.81

%90.56

%

1490.87

%92.42

%94.04

%90.26

%89.60

%95.54

%94.44

%90.81

%89.98

%93.92

%92.36

%90.24

%92.44

%90.95

%85.74

%91.11

%15 91.61 92.75 94.75 91.00 90.38 95.98 95.03 91.52 90.37 94.58 93.24 90.68 93.28 91.54 86.11 92.06

20

% % % % % % % % % % % % % % % %

1692.03

%93.11

%95.08

%91.73

%90.86

%96.27

%95.49

%92.19

%91.07

%95.56

%93.73

%90.94

%93.67

%92.00

%86.77

%92.80

%

21

Table 3 shows that the cumulative coverage of each movie at each

level. It showed that any movies don’t reach 98% coverage and only

Enchanted are reached 95% at level 13, Frozen reached it at level 15, and

Beauty and the Beast and Pinocchio are reached it at level 16.

22

Table 4: The coverage rate for the 16 movie scripts at each level

Alad

din

Alice

in

Won

der

land

Beau

ty

and

the

Beas

t

Cars

Cind

ere

lla

Ench

ante

d

Froz

en

Find

ing

Nem

o

Pete

r Pa

n

Pino

cch

ioSl

eepi

ng

Beau

ty

Snow

W

hite

Tang

led

The

Lion

king The

Little

m

erm

ai

Toy

Stor

y

145.8

%44.3

%47.6

%47.6

%44.1

%50.5

%45.8

%49.9

%43.0

%48.3

%41.9

%43.8

%44.1

%48.3

%47.5

%46.9

%

2 9.3%11.7

%10.4

%10.5

%10.8

%11.3

%12.4

%10.6

% 9.7%10.1

%11.1

% 9.4%11.2

%10.5

%10.6

%10.1

%

313.1

%14.3

%14.1

%12.6

%13.7

%13.3

%14.1

%13.6

%13.9

%14.6

%14.8

%14.7

%13.0

%11.2

%13.4

%13.8

%4 7.0% 6.4% 7.4% 6.7% 7.2% 7.1% 7.0% 5.9% 6.6% 7.4% 7.0% 7.9% 8.1% 7.4% 7.2% 5.8%5 3.1% 3.2% 2.6% 2.7% 3.5% 3.6% 2.9% 1.9% 2.9% 3.2% 3.5% 2.9% 4.0% 2.6% 3.0% 2.8%6 2.9% 2.6% 2.8% 2.2% 2.2% 2.7% 3.6% 2.6% 3.4% 2.6% 2.7% 2.4% 3.6% 2.7% 2.9% 2.6%7 2.0% 2.2% 2.2% 1.9% 2.5% 2.1% 2.3% 1.5% 2.1% 1.6% 3.3% 1.7% 2.4% 2.0% 2.0% 2.0%8 1.2% 1.2% 1.7% 1.2% 0.9% 1.1% 1.6% 0.9% 1.2% 1.4% 1.5% 1.6% 1.2% 1.0% 1.6% 1.4%9 1.2% 1.5% 1.2% 1.3% 1.0% 0.9% 1.1% 0.9% 0.7% 1.4% 1.6% 0.7% 0.9% 1.8% 1.0% 2.3%

10 1.0% 1.0% 1.0% 1.0% 0.7% 0.6% 0.7% 0.7% 1.2% 1.2% 0.4% 0.6% 0.9% 0.9% 0.7% 0.8%11 1.2% 1.2% 1.1% 0.6% 0.7% 0.9% 1.1% 0.7% 1.2% 0.8% 1.9% 2.5% 1.1% 0.6% 0.9% 0.8%12 1.9% 1.6% 0.6% 0.7% 1.3% 0.8% 0.8% 0.6% 0.7% 0.5% 1.8% 0.9% 0.9% 0.7% 1.0% 0.7%13 0.7% 0.4% 0.4% 0.7% 0.5% 0.2% 0.6% 0.5% 1.2% 0.2% 0.4% 0.6% 0.4% 0.4% 0.4% 0.5%14 0.5% 0.8% 0.9% 0.5% 0.4% 0.5% 0.4% 0.7% 2.0% 0.6% 0.4% 0.8% 0.6% 0.7% 0.5% 0.6%15 0.7% 0.3% 0.7% 0.7% 0.8% 0.4% 0.6% 0.7% 0.4% 0.7% 0.9% 0.4% 0.8% 0.6% 0.5% 1.0%16 0.4% 0.4% 0.3% 0.7% 0.5% 0.3% 0.5% 0.7% 0.7% 1.0% 0.5% 0.3% 0.4% 0.5% 0.3% 0.7%Not in 8.0% 6.9% 4.9% 8.3% 9.1% 3.7% 4.5% 7.8% 8.9% 4.4% 6.3% 9.1% 6.3% 8.0% 6.5% 7.2%

Total　100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%100.0

%

23

Figure 1: The coverage rate for the 16 movie scripts at each level

24

Table 4 and Figure 1 showed the coverage of each script at each level. Enchanted which reached 95% coverage at level 13 and used fewer words from out of level and more words from the upper level than other movies.

Table 5: The average coverage of the 16 levels for one movie

Level Number of headwords (cumulative)

Rate

1 100 (100) 67%2 100 (200) 56%3 200 (400) 59%4 100 (400) 66%5 100 (600) 56%6 150 (750) 45%7 150 (900) 31%8 150 (1,050) 29%9 150 (1,200) 21%10 150 (1,350) 20%11 200 (1,550) 18%12 200 (1,750) 15%13 150 (1,900) 11%14 200 (2,100) 11%15 200 (2,300) 13%16 200 (2,500) 10%

total average

2,500 33%

Table 5 shows the average percentage of the words at each level that

25

have been used in one movie of the 16 movies. One movie used 67% of the first 100 words at level 1, and 56% of the 100 words at level 2. Bottom of level 11, one movie used under 20% of the words. On the whole, learners will meet only 33% of the words at these 16 levels.

Table 6: The proportion of the words using more than 10 times at each level

Level Number of headwords

(cumulative)

Rate

1 100 (100) 79%2 100 (200) 67%3 200 (400) 74%4 100 (400) 83%5 100 (600) 75%6 150 (750) 59%7 150 (900) 36%8 150 (1,050) 34%9 150 (1,200) 20%10 150 (1,350) 19%11 200 (1,550) 16%12 200 (1,750) 13%13 150 (1,900) 7%14 200 (2,100) 10%15 200 (2,300) 13%16 200 (2,500) 6%

total average

2,500 38%

26

Table 6 showed that the percentage of the words which learners are

likely to be known after watching all 16 movies if we assume that they need

to meet the words at least 10 times for them to be ‘known’. 38% of the

words in the 16 movies are likely to be known after watching all 16 movies.

27

2.3.2 Vocabulary

Table 7: The top 25 words at levels 1 to 8

Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Level 81 I we a will dream would dear might2 be but can so ever still should fix3 see for they if guy fly once scare4 the all now little world swim poor land5 to come well find heart even save song6 this here one could move through sweet rise7 it get some life hurry hold lady stick8 not know right true child funny crazy deal9 and just let wish last marry forever perhaps10 do with think course real wonder actually present11 no look time place may kill hang shoot12 he why way stay forget around certainly trust13 oh about wait than free kiss upon human14 what love never happen close whole fairly guest15 of back as leave own enough pass blow16 have every take turn left matter promise ahead17 go any tell bring wonderful wake perfect freeze18 in down from sing soon star return wood

28

19 she really thing believe surprise ride cool taste20 you help then fine fall road death search21 on very mean most while bit point shut22 yes only day light chance order space warn23 there stop too follow stand power beat escape24 out sorry need ready another yet prepare attention25 hello dad or guess mind alive exact roll

Table 8: Top 25 words at levels 9 to 16 and those ‘out of list’.

Level 9 Level 10 Level 11 Level 12 Level 13 Level 14 Level 15 Level 16Out of list

1 king string queen prince pan captain waged race heighho2 kid ocean magic princess lightning hook whale excuse majesty3 shall loose dig roses monster beast grace party pretty4

toy brave castle slippercommand hi rust

conscience sire

5 master chase fool soul pig scar rat bubble ooh6

baby fit shinegentleman shark merry royal pirate ain

7 cause spot fault crew thunder palace bolt fairy heh8 nose awake glad interpret float whistle brute lead eh9 speed beg pardon harm faith crown heal elastic kingdom10 smoke chief bite tune adapt mate sting chop mm1 impress race bell grab colony sand polar blink aye

29

112 wing steady honor nonsense dumb tank bump ring idea13 entire count gift treasure standard alphabet demon fossil ohh14 gas drama darling tower sweep tub peculiar

sympathize hah

15 indeed economy dust trap carpet

assignment claw charge gonna

16 force pleasure sail charm combine bless crust deceive sharkbait17 gate practical spell honey dive mask gram fuss dude18 honest define celebrate joy forbid pal hail laser hoo19 lip guard cough quit formal process utilize moisture ahem20 mirror hunt bet tale inspire curse wax someday ahh21 ought diamond block bow row glory bounce chew papa22 rough chip cute butter tick lantern immune heap piston23 case feed dare ideal victim sew lord hybrid blah24 propose focus evil planet bride turtle upper mark ee25 direction

fortunately forgive assess constant battle

collaborate moist ugh

30

Table 7 and 8 show the top 25 highest ranking words used in the 16

movies. We can what words we could learn from movies when we watch all

16 movies. These also would be high frequency words in the real world,

especially words from level 1 to level 3. Words out of list had a lot of

onomatopoeia such as eh, ahem, and ugh.

2.4 Summary

In Chapter Two, we have looked at many tables that showed the

numbers and percentages. These data showed that the average of the 16

movies doesn’t equal the 95% or 98% word coverage needed for

comprehension and thus were unlikely to lead to much learning. In the next

chapter, we will discuss this in more detail.

31

Chapter 3: The Discussion 3.1 Introduction

In Chapter One and Two, we looked at the analysis the text for learning

English from movies, and the results of analyzing the text. In this last

chapter, we are going to look at the discussion of the results, and answer the

research questions.

3.2 Summary of the results

All 16 movies totaled 117,367 running words. Table 2 showed that

learners would have only 93.13% coverage of one movie of all 16 movies

even if learners know all 2,500 words from level 1 to level 16. Table 3

showed that the only 4 movies could reach 95% coverage. Table 6 showed

that learners could know about over 60% of 750 words which are from level

1 to level 6 if they watch all 16 movies. In terms of all 16 levels, they could

be known 38% of 2,500 words on the same condition. Table 7 and 8 showed

that the words we can learn if we watch all 16 movies.

3.3 Discussion of the results

Table 2 showed that level 16 provided 93.13% of coverage of 16

movies. Table 3 showed that not one of the movies had 98% coverage movie

looking at each movie and only 4 of 16 movies, which were Beauty and the

Beast, Enchanted, Frozen, and Pinocchio, which reached 95% coverage.

32

Enchanted has 95.05% coverage if learners know 1,900 words at level 13,

Frozen has 95.03% coverage if learners know 2,300 words at level 15, and

Beauty and the Beast and Pinocchio have 95.08% and 95.56 % coverage if

learners know 2,500 words at level 16. That means Enchanted is the easiest

movie but looking at the average, it is difficult for learners who have small

knowledge of the words to learn English from movies so learning English

depends on learners’ age or competence.

Table 7 and 8 showed that almost all of the words from the movies

looked very frequent but some words are not frequent as the level is getting

high because it depends on the movie we analyzed in this study. Combined

with Tables 3, 4, 7, 8, and figure 1, Words of level 1 to 3 take up about 70-80

% of all, which are the most frequent words.

3.4 Research questions

1. How many words do learners need when the coverage of movies

reach 95% or 98%?

This study showed that learners need more than 2,500 words when the

coverage of movies reaches 95% or 98%. Unfortunately, the specific

numbers of the words they actually know wasn’t a research question in this

study. However, we can say that as a learner’s listening vocabulary is about

40-50% of their reading vocabulary and so as we saw in Chapter One a

typical college student may have 2,000-2,500 knowledge of written words,

not spoken words and thus may know about 1,000-1,300 words only which

33

will mean that none of the movies come close to meeting the 95% minimum

requirement to be able to guess unknown words from context. This means

learning English from movies is likely to be all but impossible for learners

with a listening vocabulary of less than 3,000 words.

2. Are there any differences from age or ability?

Learning English from movies requires high level of knowledge of the

words that learners can understand by listening, as well as reading. The data

suggest that a no learners were at vastly different ages or levels, we could

not assess this.

3. What words were used in the movies at each level?

Table 6 and 7 answered this question. The words used in movies can

often be seen or heard in daily life.

3.5 Implications

We looked at the results of this study and we found out learning

English from movies is difficult for us because we need 95-98% coverage of

the movies to understand them. If learners have the enough knowledge of

the vocabulary, using movies for learning would be very helpful. The words

at level 1 to 3 in movies and its percentage are 70-80% which means

learners can learn frequent vocabularies by watching movies many times.

However, if the learners watch the movies many times eventually they will

34

understand them.

3.6 Limitations of the Experiments

There are two limitations of the experiments in this study.

(1) the number of the movies and genres

(2) focused on only literal words

First, the movies we use in this study were chosen randomly so we might get

the different results we got if the movies we use were changed, or more

variety.

Secondly, we focused on only the words literal word not the sounds or the

words or the visual images in this study. The percentage this study may be

lower if we also focus on including sound of the words. This means learners

don’t have the same knowledge of the words that learners can understand

both looking and listening. Learners should have the words that they can

understand if they see them but they can’t understand if they only listen to

it.

3.7 Further researchThis study focused on vocabulary, so it will get much better

information if we looked at listening point, and grammar point.

35

In terms of listening, we can discuss the particularity of learning

through movies. Listening with pictures gives us better understanding than

listening only. We can also discuss the effect of the subtitles and its

difference between watching movies in English with English/Japanese

subtitles or without subtitles, and in Japanese with English/Japanese subtitles

or without subtitles.

From the point of view from grammar, we will analyze what grammar

the characters use in their discourse. Learning grammar through discourse in

movies could provide aural input of natural ways to speak and then we can

know about what types of grammar are used most frequently according to

the situations.

3.8 Conclusion

In this study, we looked at the coverage of the movies and it showed

the difficulty of learning English from movies. It is a good way for learners

who have high knowledge of the words to learn with movies. The

vocabularies used in movies are useful. This study showed different results

from the results we expected. Many researchers said that learning English

through movies are good way but it depends on the learners.

36

References

Anthony, L. 2013. AntWord Profiler. Waseda Computer Science Labs.

Brown, R. & Waring, R. & Donkaewbua, S. 2008. Incidental vocabulary acquisition from reading, reading-while-listening, and listening stories, Reading in a Foreign Language, 20 (2): 136-163

Nation, P. & Waring, R. Vocabulary size, text coverage and word lists. www.fltr.ucl.ac.be/fltr/germ/etan/bibs/vocab/cup.html Accessed October 12, 2014

Webb, S. & Rodgers, M. P. H. 2009. The lexical coverage of movies. Applied Linguistics, 30 (3): 407-427

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