Whole Class Feedback Script Example IV - Presentation
Chris Elvin
6/14/2021
library(quanteda)
library(quanteda.textstats)
library(readtext)
library(tidyverse)
library(lubridate)
x <- list.files() %>% readtext %>% corpusSome Common Words
x %>% tokens() %>% dfm(remove_punct=T) %>%
dfm_remove(stopwords("en")) %>%
textstat_frequency()%>%
filter(frequency>5) %>%
summarise(word = feature, frequency) word frequency
1 food 76
2 culture 51
3 people 34
4 eat 28
5 ceremony 28
6 coming-of-age 26
7 talk 25
8 many 25
9 first 22
10 like 21
11 japanese 20
12 fashion 20
13 tea 20
14 costumes 19
15 also 18
16 introduce 18
17 dishes 17
18 called 16
19 french 15
20 south 14
21 day 14
22 means 14
23 japan 13
24 know 13
25 one 13
26 folk 13
27 costume 13
28 history 13
29 next 12
30 popular 12
31 finally 12
32 eating 12
33 america 12
34 famous 12
35 national 12
36 thai 12
37 taiwanese 12
38 italy 11
39 manners 11
40 can 11
41 around 11
42 new 11
43 think 11
44 british 11
45 long 10
46 typical 10
47 made 10
48 fast 10
49 foods 10
50 way 10
51 said 10
52 cultures 10
53 countries 10
54 presentation 10
55 thailand 10
56 traditional 9
57 women 9
58 held 9
59 era 9
60 world 8
61 different 8
62 make 8
63 lot 8
64 skirt 8
65 wear 8
66 age 8
67 uk 8
68 afternoon 8
69 italian 7
70 region 7
71 differences 7
72 time 7
73 cheese 7
74 various 7
75 words 7
76 meal 7
77 spread 7
78 tell 7
79 germany 7
80 water 7
81 north 6
82 climate 6
83 breakfast 6
84 everyone 6
85 parts 6
86 events 6
87 turkey 6
88 table 6
89 three 6
90 taste 6
91 france 6
92 event 6
93 white 6
94 women's 6
95 clothing 6
96 addition 6
97 origin 6
98 prefectures 6
99 changed 6
100 victorian 6
101 korea 6Five-Word Collocations
x %>% textstat_collocations(size=5) %>% filter(count>2) %>%
arrange(-count) %>%
summarise(collocation, count) collocation count
1 i would like to introduce 7
2 origin of the coming-of-age ceremony 5
3 would like to introduce the 4
4 i'd like to talk about 4
5 prefectures in the coming-of-age ceremony 3
6 differences between prefectures in the 3
7 history of the coming-of-age ceremony 3
8 between prefectures in the coming-of-age 3
9 about the food culture of 3
10 the origin of the coming-of-age 3Short Collocations
x %>% textstat_collocations(size=2) %>% filter(count>3) %>%
head(30) %>%
arrange(-count, collocation) %>%
summarise(collocation, count) collocation count
1 in the 44
2 of the 42
3 food culture 34
4 it is 27
5 talk about 24
6 coming-of-age ceremony 23
7 there are 23
8 is a 22
9 i will 20
10 on the 19
11 such as 15
12 like to 14
13 to be 12
14 will talk 12
15 are many 10
16 you about 10
17 you know 8
18 do you 7
19 folk costumes 7
20 french food 7
21 folk costume 6
22 i think 6
23 national costumes 6
24 not only 6
25 other countries 6
26 will introduce 6
27 differences between 5
28 national costume 5
29 between prefectures 4
30 has its 4Read feedback form and subset for day of lesson.
y <- read.csv("~/Documents/Documents - MacBook Pro/Data_Analysis/2021/data analytical solutions for the language teacher/whole_class_feedback/whole_class_feeedback_presentation/x1.csv") %>% filter(Timestamp>= as.Date("2021-06-14"))What was good about the presentation?
y %>% select(`What.did.you.like.about.her.speech.`) %>% table %>% sort %>% rev %>% head(10).
Grammar, Vocabulary, Content
18
Fluency, Grammar, Vocabulary, Pronunciation, Content
16
Grammar, Vocabulary
6
Fluency, Pronunciation, Content
6
Content
6
Fluency, Vocabulary, Content
5
Grammar, Vocabulary, Pronunciation
4
Fluency, Grammar, Pronunciation, Content
4
Pronunciation, Content
3
Fluency, Vocabulary, Pronunciation
3 A few comments…
set.seed(1066)
z <- y %>% select(Comment..in.English.or.Japanese..) %>%
sample_n(40, replace=F)
z$Comment..in.English.or.Japanese.. %>% str_squish %>% as.factor [1] Your pronunciation was good, it was easy to listen. There were many thing that I hadn't known.
[2] It was easy to understand because it was organized point by point.
[3] The slides about food culture of America were to the point and easy to understand for me by using the images.
[4] タイの文化の特徴をわかりやすくまとめていて、聞きやすかったです。
[5] 成人式が中国に由来していたことや日本では埼玉県から広まったことに驚いた。
[6] There were many information that I didn't know, so it was fun to watch. Also, she compared with Japan, and it made me to understand the differences clearly.
[7] The slides about European food culture were to the point and easy to understand by using the images.
[8] I was able to learn a lot about Thailand from her presentation.
[9] 地域によって、式を夏やゴールデンウイークに行うことを知りとても驚きました。内容が濃くとても面白かったです。
[10] I was reminded of the wonderfulness of Japanese food. I think the delicacy, quality, and health consciousness of Japanese food are all wonderful elements that represent Japan. Her slide was very good and easy to understand.
[11] Miu researched German food culture in detail. So, I could know German food culture well. Miu spoke using inflection. She emphasized the points.
[12] It had a map on it, which was easy to follow, but I thought it could do with a bit more intonation.
[13] I didn't know much about tea, so I was interested in its history.
[14] The content was very interesting because I didn't know anything about history of coming-of-age ceremony! I'm glad to know one of my county's event.
[15] イラストや写真があり、それぞれの国の伝統的な服の特徴が分かりやすかったです。フィンランドの服が特にかわいいと思いました。
[16] The voice volume and vocabulary were good, but it's better to use more slides.
[17] The map was used to show the location and was easy to understand.
[18] She introduced about what she's going to say in the beginning, so I could understand her contents and points well.
[19] She spoke clearly and slowly, so it was easy to hear. Also, content was interesting and I was surprised Taiwanese don't eat breakfast.
[20] I think it was a good summary of the content.
[21] Her pronunciation was very clear and fluent.
[22] スライドがとても工夫されているなと感じました。また、台湾の歴史から食文化そして伝統衣装など沢山の情報を簡潔にわかりやすくまとめられていてべんになりました。
[23] The layout of slides was good. Although I thought there were too much letters.
[24] イタリアやフランスのテーブルマナーは意外で、日本ではあまり普及していないと思った。
[25] She pronounced each word clearly and it was easy to hear.
[26] Listening to her presentation, I was able to learn more about the food culture of Italy.
[27] I found it easy to understand as the content was summarized with pictures.
[28] Her slides had many pictures so I could understand in detail and also it was fun to see
[29] Het topic was very interesting because I didn't know why Japanese food is popular. Also, I could understand easily because she talked using animation effect.
[30] The costume was interesting. Especially, I didn't know each color has a meaning. The Lantern festival is held every summer in Enoshima, Japan, but the original Taiwan is still more beautiful. I was glad she gave us a summary's slide and I was able to check it again.
[31] I found it good that there was a variety of information and that words that I didn't understand were explained well.
[32] Enya research world folk costumes in detail. So, I could know them well. I didn't know folk costumes in Ghana, Brazil, and so on.
[33] I was able to learn about costumes from many different countries.
[34] This was the first time I learned that sausage is a famous German food.
[35] It was so easy to understand presentation. Her speaking speed, slide, and contents were all thought about us. I want to follow her and make use of it in next presentation. I want to give full marks to her that great effort.
[36] She talked the British fashion type after she explain the each era. It was interested for me.
[37] I thought it was easy to understand because it used a number of pictures.
[38] I found the content interesting. Some parts were summarized in tables, so it was easy to understand.
[39] I was able to learn about healthy foods.
[40] Her presentation included some traditional clothes that I had never seen, and I could understand there are various traditional clothes in the world.
40 Levels: Enya research world folk costumes in detail. So, I could know them well. I didn't know folk costumes in Ghana, Brazil, and so on. ...Word Count
df <- data.frame(token = ntoken(x),
type = ntype(x))
df %>% ggplot(aes(x=token)) +
geom_density() +
labs( x = "Words",
y = "") +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank())
Read Flipgrid CSV file into R.
setwd("~/Documents/Documents - MacBook Pro/school_2021/shirayuri_2021/r_scripts/period1/report6")
y <- read.csv("responses.csv")
y$Minutes <- y$Video.Length %>% ms() %>% as.numeric
y %>% ggplot(aes(x=Minutes)) +
geom_density() +
labs(title = "Presentation Time",
y = "") +
theme(axis.ticks.y = element_blank(),
axis.text.y= element_blank()) +
scale_x_time(labels = function(x) strftime(x, "%M:%S"))
Rare Words
x %>% tokens() %>% dfm(remove_punct=T,
remove_numbers=T) %>%
dfm_remove(stopwords("en")) %>%
textstat_frequency()%>%
filter(frequency>1 & frequency<=5) %>%
summarise(word = feature) %>%
arrange(word) word
1 accepted
2 according
3 actually
4 adult
5 adults
6 ages
7 agriculture
8 along
9 american
10 ami
11 another
12 appearance
13 areas
14 arisa
15 asia
16 asian
17 autumn
18 available
19 aware
20 baby
21 back
22 baji
23 baked
24 bao
25 based
26 basically
27 beans
28 beautiful
29 became
30 become
31 beef
32 big
33 black
34 blouse
35 blue
36 body
37 born
38 bread
39 bring
40 buddihism
41 bulging
42 business
43 carne
44 celebrate
45 central
46 century
47 chain
48 characteristic
49 characteristics
50 characterized
51 chicken
52 children
53 chili
54 chima
55 china
56 christmas
57 city
58 class
59 cloth
60 clothes
61 coffee
62 cold
63 color
64 colors
65 coming
66 common
67 companies
68 con
69 connected
70 considered
71 continent
72 contrast
73 cooking
74 country
75 cultural
76 curry
77 custom
78 decorated
79 delicious
80 depending
81 depth
82 design
83 detailed
84 difference
85 dinner
86 dish
87 divide
88 divided
89 dress
90 dresses
91 drink
92 east
93 eaten
94 edwardian
95 elegant
96 enjoy
97 enya
98 etc
99 ethnic
100 europe
101 european
102 even
103 ever
104 example
105 examples
106 exists
107 experience
108 explain
109 families
110 family
111 fan
112 feature
113 features
114 festival
115 flat
116 flavor
117 flour
118 focus
119 fork
120 formal
121 fried
122 furisode
123 genpuku
124 german
125 get
126 ghana
127 give
128 go
129 going
130 good
131 grass
132 great
133 green
134 greetings
135 gua
136 hamburger
137 hanbok
138 hand
139 harvest
140 hat
141 hats
142 head
143 healthy
144 hello
145 heritage
146 hey
147 hi
148 hold
149 holiday
150 hope
151 hot
152 house
153 however
154 important
155 include
156 india
157 indigenous
158 influenced
159 ingredients
160 inside
161 instance
162 intangible
163 interesting
164 introduced
165 jacket
166 jeogori
167 kanrei
168 kimchi
169 kind
170 kinds
171 kingdom
172 known
173 korean
174 krathong
175 kung
176 lamb
177 lanterns
178 left
179 less
180 let
181 life
182 listening
183 local
184 located
185 look
186 lou
187 love
188 low
189 luck
190 lunar
191 lunch
192 making
193 maori
194 may
195 meals
196 meat
197 megumi
198 men
199 men's
200 middle
201 mild
202 milk
203 mind
204 misaki
205 modern
206 money
207 morning
208 much
209 nakagawa
210 narrow
211 need
212 noble
213 northern
214 now
215 often
216 old
217 olives
218 ones
219 onion
220 order
221 originally
222 others
223 outfit
224 outside
225 overseas
226 parents
227 part
228 particular
229 party
230 pasta
231 patterns
232 period
233 pho
234 pizza
235 place
236 plain
237 pork
238 potatoes
239 prefecture
240 put
241 puts
242 races
243 reason
244 reasons
245 red
246 registered
247 relatives
248 religion
249 republic
250 researched
251 reuben
252 rice
253 ritual
254 roasted
255 round
256 salad
257 sally
258 sand
259 sandwiches
260 sashimi
261 sauce
262 sausages
263 say
264 schnitzel
265 sea
266 seafood
267 seasoning
268 second
269 secondly
270 seems
271 sense
272 served
273 set
274 seven
275 shape
276 shiori
277 shiraishi
278 show
279 since
280 singapore
281 skirts
282 something
283 sophisticated
284 sound
285 soup
286 south-east
287 southern
288 spaghetti
289 spices
290 spicy
291 spoon
292 stalls
293 stands
294 started
295 states
296 stewed
297 still
298 stores
299 street
300 style
301 summer
302 sweets
303 take
304 talked
305 techniques
306 tend
307 thais
308 thank
309 therefore
310 thing
311 third
312 thomas
313 thought
314 times
315 toast
316 today
317 told
318 tom
319 tomatoes
320 took
321 top
322 topography
323 tradition
324 tribe
325 tuan
326 twining
327 two
328 types
329 unesco
330 unique
331 united
332 unlike
333 use
334 used
335 uses
336 using
337 usually
338 varies
339 vegetables
340 version
341 vietnam
342 visit
343 waist
344 want
345 war
346 wearing
347 well
348 western
349 wheat
350 winter
351 wish
352 without
353 worn
354 wrap
355 wrapped
356 xiao
357 year
358 year's
359 years
360 yu
361 yum