| import streamlit as st |
| import pandas as pd |
| import altair as alt |
| import matplotlib.pyplot as plt |
| import seaborn as sns |
|
|
| st.set_page_config( |
| page_title='What makes comprehensible input comprehensible?', |
| page_icon='favicon.svg' |
| ) |
|
|
| |
| st.markdown( |
| """ |
| <style> |
| .dataframe-div { |
| background-color: white; |
| } |
| </style> |
| """, unsafe_allow_html=True |
| ) |
|
|
| |
| @st.cache_data |
| def load_dataframes(): |
|
|
| video_df = pd.read_csv("video_data.tsv", sep="\t") |
| word_coverage_df = pd.read_csv('word_coverage_df_plot.tsv', sep='\t') |
| num_video_df = pd.read_csv('num_video_df.tsv', sep='\t') |
|
|
| return video_df, word_coverage_df, num_video_df |
|
|
| def get_grammar_table(): |
|
|
| data = { |
| 'Complete Beginner': [0.02638719922016275 ,0.0192492959834, 0.00476028625918155, 0.2503071253071253, 0.18554386037363785, 0.01622086690206438, 0.04537920642893019, 0.1203097143691203], |
| 'Beginner': [0.0473047304730473, 0.0266429840142095, 0.005813953488372, 0.2454068241469816, 0.1773049645390071, 0.01384083044982699, 0.02676864244741874, 0.13333333333333333], |
| 'Intermediate': [0.06625719079578135, 0.03514773095199635, 0.0087719298245614, 0.23239271705403663, 0.1587691162151326, 0.010784997932175352, 0.022392603507910194, 0.13379268084136123], |
| 'Advanced': [0.0766787658802177, 0.0373056994818652, 0.0108588351431391, 0.2237101220953131, 0.14922184925236498, 0.009050978304272594, 0.020185708518368994, 0.1364369670430975] |
| } |
| df = pd.DataFrame(data) |
|
|
| row_labels = ['Median Perc. Subordinating Conjunctions', 'Median Perc. Adverbs', 'Median Perc. Determiners', 'Median Perc. Nouns', 'Median Perc. Auxiliaries', 'Median Perc. Numerals', 'Median Perc. Pronouns', 'Median Perc. Verbs'] |
| df.index = row_labels |
|
|
| styled_df = df.style.set_table_styles( |
| { |
| 'Complete Beginner': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(165, 190, 228, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Beginner': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(154, 214, 216, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Intermediate': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(199, 174, 205, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Advanced': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(221, 158, 158, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ] |
| }).set_properties(**{'background-color': 'white'}).format("{:.2%}") |
|
|
| return styled_df |
|
|
| def get_word_origin_table(): |
|
|
| data = { |
| 'Complete Beginner': [0.06999874574159035, 0.8578043261266064, 0.03301790801790795], |
| 'Beginner': [0.0955284552845528, 0.8399311531841652, 0.0279441117764471], |
| 'Intermediate': [0.1165702954621605, 0.8259877335615461, 0.0241447813837379], |
| 'Advanced': [0.1303328645100797, 0.8225274725274725, 0.0157535445475231], |
| } |
| df = pd.DataFrame(data) |
|
|
| row_labels = ['Median Perc. Kango (漢語)', 'Median Perc. Wago (和語)', 'Median Perc. Garaigo (外来語)'] |
| df.index = row_labels |
|
|
| styled_df = df.style.set_table_styles( |
| { |
| 'Complete Beginner': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(165, 190, 228, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Beginner': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(154, 214, 216, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Intermediate': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(199, 174, 205, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| 'Advanced': [ |
| {'selector': 'th.col_heading.level0', 'props': [('background-color', 'rgba(221, 158, 158, 0.45)')]}, |
| {'selector': 'td:hover', 'props': [('background-color', '#e0f7fa')]} |
| ], |
| }).set_properties(**{'background-color': 'white'}).format("{:.2%}") |
|
|
| return styled_df |
|
|
| |
| @st.cache_data |
| def get_wpm_chart(show_medians=False): |
|
|
| line_data = pd.DataFrame({ |
| 'x': [75, 91, 124, 149], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'wpm:Q', |
| bin=alt.Bin(maxbins=20), |
| title='Words per minute', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20 |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,100]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('wpm:Q', title='Words per minute:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| height=500, |
| title=alt.TitleParams( |
| text='Rate of speech in words per minute (WPM)', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median WPM:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.0f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
|
|
| if show_medians: |
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
| else: |
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_wpm_vs_sps_chart(interactive=False): |
|
|
| scatter_plot = alt.Chart(video_df).mark_circle( |
| cursor='pointer', |
| size=80, |
| ).encode( |
| x=alt.X( |
| 'wpm:Q', |
| scale=alt.Scale(domain=[30,215]), |
| title='Words per minute', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20 |
| ) |
| ), |
| y=alt.Y( |
| 'sps:Q', |
| title='Syllables per second', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| ), |
| ), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| orient='right', |
| direction='vertical', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('video:N', title='Video number:'), |
| alt.Tooltip('wpm:Q', title='WPM:'), |
| alt.Tooltip('sps:Q', title='SPS:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.2)), |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Rate of speech: Syllables per second vs. words per minute', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ).configure( |
| background='white' |
| ) |
|
|
| if interactive: |
| return scatter_plot.interactive() |
| else: |
| return scatter_plot |
|
|
| @st.cache_data |
| def get_sentence_length_hist(show_medians=False): |
|
|
| line_data = pd.DataFrame({ |
| 'x': [7.60, 10.45, 16.17, 19.39], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'mean_sentence_length:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Words per sentence', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20 |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,100]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('mean_sentence_length:Q', title='Average sentence length:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Average sentence length (words per sentence)', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median avg. sentence length:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.2f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| if show_medians: |
|
|
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
|
|
| else: |
|
|
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_repetition_hist(show_medians=False): |
|
|
| video_df['average_rel_reps_perc'] = 100.0 * video_df['average_rel_reps'] |
|
|
| sub_video_df = video_df[video_df['average_rel_reps_perc'] <= 2.0] |
|
|
| line_data = pd.DataFrame({ |
| 'x': [0.99, 0.62, 0.37, 0.23], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(sub_video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'average_rel_reps_perc:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Word repetitions (%)', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| ), |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,100]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('average_rel_reps:Q', title='Average repetitions (%):', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Average amount of repetition per word', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| alt.X( |
| 'x:Q' |
| ), |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median avg. repetitions (%):'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)), |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| alt.X( |
| 'x:Q' |
| ), |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.2f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| if show_medians: |
|
|
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
|
|
| else: |
|
|
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_word_coverage_chart(zoom=False): |
|
|
| if zoom: |
| word_coverage_df_sub = word_coverage_df.loc[word_coverage_df['coverage_perc']>=90] |
| else: |
| word_coverage_df_sub = word_coverage_df |
|
|
| line_data = pd.DataFrame({ |
| 'x': [4295, 5606, 6853, 9085], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| line_chart = alt.Chart(word_coverage_df_sub).mark_line( |
| cursor='pointer', |
| point=False, |
| ).encode( |
| x=alt.X( |
| 'rank:Q', |
| scale=alt.Scale(domain=[1000,16000]) if zoom else alt.Scale(domain=[-10,16000]), |
| title='Number of words known', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20 |
| ) |
| ), |
| y=alt.Y( |
| 'coverage_perc:Q', |
| scale=alt.Scale(domain=[90,101]) if zoom else alt.Scale(domain=[0,105]), |
| title='% of words understood', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| ), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| orient='right', |
| direction='vertical', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('word:N', title='Word: '), |
| alt.Tooltip('rank:Q', title="CIJ rank: "), |
| alt.Tooltip('coverage_perc_str:N', title='Word coverage: '), |
| alt.Tooltip('level:N', title='Curve: ') |
| ], |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.2)), |
| strokeWidth=alt.condition(selection | highlight, alt.value(6), alt.value(2)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Word coverage curves', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=4, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Words needed to reach 98%:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.0f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| layered_chart = alt.layer(line_chart, vertical_lines, text_labels, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_ne_spot_hist(show_medians=False): |
|
|
| line_data = pd.DataFrame({ |
| 'x': [3859, 5229, 6698, 7925], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'ne_spot:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Number of words known', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,40]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('ne_spot:Q', title='Vocab size for 98%.:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Vocab size needed for 98% coverage (videos)', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median vocab size needed for 98% cov:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.0f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| |
| if show_medians: |
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
| else: |
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_tfplr_hist(show_medians=False): |
|
|
| line_data = pd.DataFrame({ |
| 'x': [3.82, 4.30, 4.76, 5.21], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'tfp_log_ranks_unique:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Log ranks', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=30, |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,80]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('tfp_log_ranks_unique:Q', title='25th perc. log rank:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='25th percentile word-frequency log ranks', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median 25th perc. log rank:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.2f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| if show_medians: |
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
| else: |
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_sconj_hist(show_medians=False): |
|
|
| video_df['sconj_props_perc'] = 100.0 * video_df['sconj_props'] |
|
|
| line_data = pd.DataFrame({ |
| 'x': [2.64, 4.73, 6.63, 7.67], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'sconj_props_perc:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Percentage of sub. conj.', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=30, |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,50]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('sconj_props_perc:Q', title='Perc. sub. conj:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Frequency of subordinating conjunctions', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median perc. of sub. conj:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.2f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| |
| if show_medians: |
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
| else: |
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def get_kango_hist(show_medians=False): |
|
|
| video_df['kan_props_perc'] = 100.0 * video_df['kan_props'] |
|
|
| line_data = pd.DataFrame({ |
| 'x': [7.00, 9.55, 11.66, 13.03], |
| 'level': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| 'text': ['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'] |
| }) |
|
|
| histogram = alt.Chart(video_df).mark_bar( |
| opacity=0.5, |
| binSpacing=3, |
| stroke='black', |
| strokeWidth=0, |
| cornerRadius=5, |
| cursor="pointer" |
| ).encode( |
| alt.X( |
| 'kan_props_perc:Q', |
| bin=alt.Bin(maxbins=30), |
| title='Percentage of kango', |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=30, |
| ) |
| ), |
| alt.Y( |
| 'count()', |
| title="Num. videos", |
| axis=alt.Axis( |
| labelFontSize=14, |
| titleFontSize=18, |
| titleColor='black', |
| titleFontWeight='normal', |
| titlePadding=20, |
| tickCount=5 |
| ), |
| scale=alt.Scale(domain=[0,40]) |
| ).stack(None), |
| alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['#a5bee4', '#9ad6d8', '#c7aecd', '#dd9e9e']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=alt.Legend( |
| title='CIJ Level', |
| titleFontSize=18, |
| titleFontWeight='bolder', |
| labelFontSize=16, |
| symbolType='circle', |
| symbolSize=200, |
| symbolStrokeWidth=0, |
| orient='right', |
| direction='vertical', |
| fillColor='white', |
| padding=10, |
| cornerRadius=5, |
| ) |
| ), |
| tooltip=[ |
| alt.Tooltip('kan_props_perc:Q', title='Percentage of kango:', bin=True), |
| alt.Tooltip('count()', title='Video count:'), |
| alt.Tooltip('level:N', title='Level:'), |
| ], |
| opacity=alt.condition(selection, alt.value(0.75), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(2), alt.value(1)) |
| ).properties( |
| width='container', |
| height=500, |
| title=alt.TitleParams( |
| text='Frequency of kango', |
| offset=20, |
| fontSize=24, |
| fontWeight='normal', |
| anchor='middle', |
| color='black', |
| subtitleFontSize=15, |
| subtitleColor='gray' |
| ) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| vertical_lines = alt.Chart(line_data).mark_rule( |
| color='red', |
| strokeWidth=6, |
| strokeDash = [10, 2], |
| ).encode( |
| x='x:Q', |
| tooltip=[ |
| alt.Tooltip('x:N', title='Median perc. kango:'), |
| alt.Tooltip('level:N', title='Level:') |
| ], |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'yellow']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| strokeWidth=alt.condition(highlight, alt.value(20), alt.value(1)) |
| ).add_params( |
| selection, |
| highlight |
| ) |
|
|
| text_labels = alt.Chart(line_data).mark_text( |
| align='center', |
| dx=0, |
| dy=-10, |
| fontSize=16, |
| fontWeight='bold' |
| ).encode( |
| x='x:Q', |
| y=alt.value(0), |
| text=alt.Text('x:Q', format='.0f'), |
| color=alt.Color( |
| 'level:N', |
| scale=alt.Scale(range=['red', 'green', 'blue', 'orange']), |
| sort=['Complete Beginner', 'Beginner', 'Intermediate', 'Advanced'], |
| legend=None |
| ), |
| opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)), |
| ) |
|
|
| if show_medians: |
| layered_chart = alt.layer(histogram, vertical_lines, text_labels, background='white') |
| else: |
| layered_chart = alt.layer(histogram, background='white') |
|
|
| return layered_chart |
|
|
| @st.cache_data |
| def render_vanilla_heatmap(): |
|
|
| corr_matrix = num_video_df.corr() |
|
|
| variable_of_interest = 'Level' |
|
|
| sorted_vars = corr_matrix[variable_of_interest].sort_values(ascending=False).index |
|
|
| sorted_corr_matrix = corr_matrix.loc[sorted_vars, sorted_vars] |
|
|
| plt.figure(figsize=(10, 8)) |
| sns.heatmap(sorted_corr_matrix, annot=True, cmap='coolwarm', fmt=".2f") |
|
|
| st.pyplot(plt.gcf()) |
|
|
| @st.cache_data |
| def render_level_row_unordered(): |
|
|
| corr_matrix = num_video_df.drop(['Proportion of determiners', 'Proportion of nouns', 'Proportion of wago', 'Proportion of gairaigo', 'Proportion of verbs', 'Proportion of numerals'], axis=1).corr() |
|
|
| variable_of_interest = 'Level' |
|
|
| sorted_vars = corr_matrix[variable_of_interest].sort_values(ascending=False).index |
|
|
| sorted_vars = sorted_vars.drop(variable_of_interest) |
|
|
| first_row_matrix = corr_matrix.loc[[variable_of_interest], sorted_vars] |
|
|
| plt.figure(figsize=(10, 1)) |
| sns.heatmap(first_row_matrix, annot=True, cmap='coolwarm', fmt=".3f", cbar_kws={'label': 'Correlation'}) |
|
|
| st.pyplot(plt.gcf()) |
|
|
| @st.cache_data |
| def render_level_col_ordered(): |
|
|
| corr_matrix = num_video_df.drop(['Proportion of determiners', 'Proportion of nouns', 'Proportion of wago', 'Proportion of gairaigo', 'Proportion of verbs', 'Proportion of numerals'], axis=1).corr() |
|
|
| variable_of_interest = 'Level' |
|
|
| correlations = corr_matrix[variable_of_interest] |
|
|
| sorted_vars = correlations.abs().sort_values(ascending=False).index |
|
|
| sorted_vars = sorted_vars.drop(variable_of_interest) |
|
|
| sorted_corr_matrix = corr_matrix.loc[[variable_of_interest], sorted_vars] |
|
|
| transposed_corr_matrix = sorted_corr_matrix.T |
|
|
| plt.figure(figsize=(2, 3)) |
| sns.heatmap(transposed_corr_matrix, annot=True, cmap='coolwarm', fmt=".3f", cbar_kws={'label': 'Correlation'}) |
|
|
| st.pyplot(plt.gcf()) |
|
|
| |
| video_df, word_coverage_df, num_video_df = load_dataframes() |
| grammar_table = get_grammar_table() |
| word_origin_table = get_word_origin_table() |
|
|
| |
| selection = alt.selection_point(fields=['level'], bind='legend', on='click') |
| highlight = alt.selection_point(name="highlight", fields=['level'], on='mouseover', empty=False) |
|
|
| |
| |
| |
| st.markdown("Note: this analysis is meant to viewed on a computer and not a phone (sorry!)") |
|
|
| st.markdown("[Code and data can be found [here](https://github.com/joshdavham/cij-analysis)]") |
|
|
| st.markdown("# What makes comprehensible input *comprehensible*?") |
|
|
| st.markdown("**Comprehensible input** (or CI, for short) is a language learning method where teachers provide their students with lots of language “input” that has been adapted to a level that they can understand. It is believed by many that CI is one of the most natural and effective ways to acquire a foreign language.") |
|
|
| st.markdown("…but what exactly is it about comprehensible input that makes it so *comprehensible*?") |
|
|
| st.markdown("To answer this question, we'll be analyzing the videos on \ |
| [cijapanese.com](https://cijapanese.com/) (CIJ), a \ |
| CI platform for learning Japanese.") |
|
|
| |
| |
| |
| st.markdown("## How fast is CI?") |
|
|
| st.markdown("If we measure how fast the teachers speak on CIJ, we find that \ |
| they speak more slowly in videos meant for beginners and more quickly \ |
| in videos meant for advanced learners.") |
|
|
| st.markdown("**(THESE GRAPHS ARE CLICKABLE)**") |
|
|
| if st.checkbox('Show medians', value=True, key='wpm'): |
| layered_chart = get_wpm_chart(show_medians=True) |
| else: |
| layered_chart = get_wpm_chart(show_medians=False) |
|
|
| st.altair_chart(layered_chart, use_container_width=True) |
|
|
| st.markdown("To put the above data into perspective, native Japanese speakers \ |
| can speak at rates of over 200 wpm, meaning that most of the videos \ |
| on CIJ have been adapted to be a lot slower than that!") |
| |
| st.markdown("We can also measure the rate of speech in syllables per second (SPS) \ |
| and compare it to words per minute.") |
|
|
| if st.checkbox('Enable zooming and panning ( ↕ / ↔️ )'): |
| wpm_vs_sps_chart = get_wpm_vs_sps_chart(interactive=True) |
| else: |
| wpm_vs_sps_chart = get_wpm_vs_sps_chart(interactive=False) |
|
|
| st.altair_chart(wpm_vs_sps_chart, use_container_width=True) |
|
|
| |
| |
| |
| st.markdown("## A quick statistics lesson") |
|
|
| st.markdown("Before we continue the analysis, there's some basic things you should know.") |
|
|
| st.markdown("### The data") |
|
|
| st.markdown("The dataset we'll be analyzing comprises of just under 1,000 videos. \ |
| In particular, we'll be analyzing the subtitles of the videos.") |
|
|
| st.markdown('Also, every video has a level: **Complete Beginner**, **Beginner**, \ |
| **Intermediate**, or **Advanced**.') |
|
|
| st.markdown("### The statistics") |
|
|
| st.markdown("The goal of this analysis is to find features in the video data that lead \ |
| to a specific pattern called an \"ordering\".") |
|
|
| st.markdown("We're specifically looking for *any* statistic that can lead to an \ |
| ordering of the levels in either of the two following directions:") |
|
|
| st.markdown("> Complete Beginner < Beginner < Intermediate < Advanced") |
| st.markdown("or") |
| st.markdown("> Complete Beginner > Beginner > Intermediate > Advanced") |
|
|
| st.markdown("For example: if a statistic is small for Complete Beginnner videos, but gets bigger \ |
| for Beginner, Intermediate, then Advanced videos, it suggests \ |
| that this is a good statistic for determining what makes a video comprehensible. \ |
| In fact, we already saw this above when measuring the [words per minute statistic](#how-fast-is-ci).") |
|
|
| st.markdown("Okay! Now we can continue.") |
|
|
| |
| |
| |
| st.markdown("## Sentence length") |
|
|
| st.markdown("Videos meant for beginners tend to have shorter sentences on average.") |
|
|
| if st.checkbox('Show medians', value=True, key='sentence_length'): |
| sentence_length_hist = get_sentence_length_hist(show_medians=True) |
| else: |
| sentence_length_hist = get_sentence_length_hist(show_medians=False) |
|
|
| st.altair_chart(sentence_length_hist, use_container_width=True) |
|
|
| st.markdown("This makes sense because long sentences can be more complex and packed with information \ |
| whereas short sentences are usually simpler.") |
|
|
| |
| |
| |
| st.markdown("## Amount of repetition") |
|
|
| st.markdown("Words are repeated more often in easier videos.") |
|
|
| if st.checkbox('Show medians', value=True, key='repetition'): |
| repetition_hist = get_repetition_hist(show_medians=True) |
| else: |
| repetition_hist = get_repetition_hist(show_medians=False) |
|
|
| st.altair_chart(repetition_hist, use_container_width=True) |
|
|
| st.markdown("If you don't catch a word the first time it's said, there's more opportunities \ |
| in the easier videos to hear that word repeated again.") |
|
|
| |
| |
| |
| st.markdown("## How many words you need to know") |
|
|
| st.markdown("A popular statistic in language learning circles is that you generally \ |
| need to know around 98% of the words in a given piece of content in order to be able to understand it well. \ |
| This statistic is known as 'word coverage' - the percentage of words you know in a given text.") |
|
|
| st.markdown("How many words do you need to know in order to understand 98% of the words in each level?") |
|
|
| st.markdown("If we take all of the words from each of the CIJ videos, count them and then order them from most common to least common, \ |
| we can calculate the word coverage you get at different vocabulary sizes. \ |
| For example, if we learn the top 500 words from CIJ, then we'll know around 80% of the words in the \ |
| Complete Beginner videos. And if we learn the top 4,295 words, then we'll know 98% of the words in the Complete Beginner videos.") |
|
|
| if st.checkbox('Zoom in'): |
| word_coverage_chart = get_word_coverage_chart(zoom=True) |
| else: |
| word_coverage_chart = get_word_coverage_chart(zoom=False) |
|
|
| st.altair_chart(word_coverage_chart, use_container_width=True) |
|
|
| st.markdown("Using this same method of calculating word coverage, \ |
| we can also calculate how many of the top words from CIJ you need to know \ |
| in order to achieve 98% word coverage in each video.") |
|
|
| if st.checkbox('Show medians', value=True, key='ne_spot'): |
| ne_spot_hist = get_ne_spot_hist(show_medians=True) |
| else: |
| |
| ne_spot_hist = get_ne_spot_hist(show_medians=False) |
|
|
| st.altair_chart(ne_spot_hist, use_container_width=True) |
|
|
| st.markdown("In general, easier videos require smaller vocabulary sizes to understand.") |
|
|
| |
| |
| |
| st.markdown("## Word rareness") |
|
|
| st.markdown("Harder videos use rarer words.") |
|
|
| if st.checkbox('Show medians', value=True, key='tfplr'): |
| |
| tfplr_hist = get_tfplr_hist(show_medians=True) |
| else: |
| tfplr_hist = get_tfplr_hist(show_medians=False) |
|
|
| st.altair_chart(tfplr_hist, use_container_width=True) |
|
|
| st.markdown("How common a word is, is known as its 'rank'. The most common word \ |
| in a text would be rank 1 and the fifth most common would be rank 5. \ |
| A word with a low rank is a commonly used word (e.g., 'and', 'work', 'that') whereas a word with a high rank \ |
| is an uncommon or 'rare' word (e.g., 'esoteric', 'gauche', 'opprobrium'). Furthermore, \ |
| a list of word ranks is known as a 'frequency list'.") |
|
|
| st.markdown("The ranks of the words in the videos were compared with a larger, independent frequency list and then scaled with a log function \ |
| before computing the twenty fifth percentile. This was done to make for a better visualization.") |
|
|
| st.markdown("Note: it's okay if the above values don't quite make sense to you - just know that the graph \ |
| demonstrates that easier videos tend to use common words more often whereas \ |
| advanced videos tend to use rarer words more often.") |
|
|
| |
| |
| |
| st.markdown("## Grammar") |
|
|
| st.markdown("Easier videos use less [subordinating conjunctions](https://universaldependencies.org/ja/pos/SCONJ.html) than harder videos.") |
|
|
| if st.checkbox('Show medians', value=True, key='sconj'): |
| sconj_hist = get_sconj_hist(show_medians=True) |
| else: |
| sconj_hist = get_sconj_hist(show_medians=False) |
|
|
| st.altair_chart(sconj_hist, use_container_width=True) |
|
|
| st.markdown("We also notice differences in the use of other types of words.") |
|
|
| st.markdown( |
| '<div class="dataframe-div">' + grammar_table.to_html() + "</div>" |
| , unsafe_allow_html=True) |
|
|
| |
| |
| |
| st.markdown("## Word origin") |
|
|
| st.markdown("There are three main categories of words in Japanese:") |
| st.markdown("(1) Wago (和語), (2) Kango (漢語) and (3) Gairaigo (外来語)") |
| st.markdown("Wago are native Japanese words, Kango are Chinese words and Gairaigo are foreign words.") |
|
|
| st.markdown("Harder videos use more kango than easier videos") |
|
|
| if st.checkbox('Show medians', value=True, key='kango'): |
| kango_hist = get_kango_hist(show_medians=True) |
| else: |
| kango_hist = get_kango_hist(show_medians=False) |
|
|
| st.altair_chart(kango_hist, use_container_width=True) |
|
|
| st.markdown("In Japanese, kango are somewhat analogous to French words in English. \ |
| These words tend to be more technical or sophisticated than other words.") |
|
|
| st.markdown("We also notice orderings when counting the percentage of Wago and Gairaigo as well.") |
|
|
| st.markdown( |
| '<div class="dataframe-div">' + word_origin_table.to_html() + "</div>" |
| , unsafe_allow_html=True) |
|
|
| |
| |
| |
| st.markdown("## Which factors matter the most?") |
|
|
| st.markdown("We've just found a number of statistics that lead to orderings in the data, \ |
| but which statistics matter the most?") |
|
|
| st.markdown("To answer this, we can look at a correlation heatmap between each of the variables \ |
| and observe which statistics correlate the most strongly with the video's level. \ |
| In particular, we'll want to look at the first row (or first column) of the heatmap.") |
|
|
| st.markdown("**(If the plots below don't load, try refreshing the page.)**") |
|
|
| render_vanilla_heatmap() |
|
|
| st.markdown("In case you're not familiar with stuff like this, numbers close to 1 or -1 \ |
| represent a high level or correlation while numbers close to 0 represent a low level of correlation. \ |
| Positive numbers represent a positive relationship between the variables and negative numbers represent a \ |
| reverse relationship between the variables.") |
|
|
| st.markdown("If we use a statistics rule of thumb and remove all of the variables that have correlations \ |
| weaker than 0.3 (and more than -0.3), we can identify the variables with the strongest correlations.") |
|
|
| if st.checkbox('Flip and sort by correlation strength'): |
| render_level_col_ordered() |
| else: |
| render_level_row_unordered() |
|
|
|
|
| st.markdown("To summarize (and simplify), the factors that correlate the most with the difficulty level are:") |
|
|
| st.markdown("1. Rate of Speech") |
| st.markdown("2. Sentence length") |
| st.markdown("3. Amount of repetition of words") |
| st.markdown("4. How rare the words are") |
| st.markdown("5. Amount of subordinating conjunctions") |
| st.markdown("6. Vocabulary size") |
| st.markdown("7. Amount of pronouns") |
| st.markdown("8. Amount of adverbs") |
| st.markdown("9. Amount of auxiliaries") |
| st.markdown("10. Amount of Chinese words") |
|
|
| st.markdown("In other words, as the videos get harder, the speech gets faster, the sentences get longer, words are repeated *less* \ |
| and so on and so forth!") |
|
|
| st.markdown("## Dicussion / Conclusion") |
|
|
| st.markdown("I find comprehensible input absolutely fascinating. The fact that\ |
| at any stage of the language acquisition process, the language can\ |
| be made into a form that anyone can understand, even without formal instruction.") |
|
|
| st.markdown("In the above analysis, we saw that there exist a number of patterns that help \ |
| explain what CI is made of and the various factors that change \ |
| when CI is targeted at new vs. experienced learners.") |
|
|
| st.markdown("The findings in this analysis are not meant to be conclusive or to tell CI educators\ |
| how to teach their students, but rather just to get us thinking more analytically about the factors\ |
| that help or hurt comprehensibility. Most of us know intuitively that slow speech is easier to understand than fast\ |
| speech, but how many of us think about the importance of repetition when trying to make ourselves understood? \ |
| I think it's interesting and important to think about these things as both language learners and educators.") |
|
|
| st.markdown("## Thanks for reading ✌️") |
|
|
| st.markdown("Thanks also to Ben, Russ Simmons and Yuki Kimura for looking at early drafts of this analysis.") |
|
|
| st.markdown("If you're a Japanese learner and you're interested in learning Japanese the natural way, then I'd highly recommend getting a membership \ |
| at [cijapanese.com](https://cijapanese.com). You'll get access to nearly 1,000 videos with new videos being added each week!") |
|
|
| st.markdown("---") |
|
|
| st.markdown("#### Further discussion for hardcore nerds") |
|
|
| st.markdown("- No tests of statistical significance were conducted. This was purely meant as an EDA. \ |
| However, you can get the data from the repo linked at the top and conduct tests yourself if you'd like. \ |
| I'd recommend starting with non-parametric tests like Kruskal-Wallis and moving on to pairwise tests \ |
| with a bonferonni correction if there's a significant result. Parametric tests may also be interesting.") |
|
|
| st.markdown("- For those interested in modelling difficulty/proficiency level, I'd recommend checking out the [jreadability python package](https://github.com/joshdavham/jreadability) \ |
| then following the links. The model is very simple but should serve as a useful starting point for those interested in building their own models.") |
|
|
| st.markdown("- While CIJ classifies their videos into discrete proficiency levels, it should be noted that language proficiency \ |
| is likely better modelled as a continuous variable. Evidence of this is partly observed by the amount of statistical overlap between \ |
| the videos of the various level groups.") |
|
|
| st.markdown("- Technically, I computed 'moras per second' - not syllables per second. I'm aware that this \ |
| is technically linguistically incorrect, but it still serves as a close approximation and is easier \ |
| to understand for readers unfamiliar with Japanese linguistics.") |
|
|
| st.markdown("- The Mecab and Sudachi parsers (through Fugashi and Spacy) were used to analyze the transcripts. These parsers are not always 100% accurate.") |
|
|
| st.markdown("- When computing the statistics for repetition, word coverage and word frequency, lemmas were used rather than tokens.") |
|
|
| st.markdown("- Of the parsed words, while I did remove punctuation, I didn't otherwise verify that each token was an actual word. \ |
| There is likely some amount of noise in the data such as mis-parses, etc.") |
|
|
| st.markdown("- I am slightly abusing the 98% statistic in this analysis. The original research applies \ |
| mainly to written text whereas the content on CIJ is mainly meant to listened to rather than read.") |
|
|
| st.markdown("- If you're like me, the word coverage plots also probably evoked a resemblance to Heap's Law. \ |
| More research would need to be done, but I suspect one may be able to find a link between word coverage and Heap's Law.") |
|
|
| st.markdown("- The frequency list used to calculate the word ranks was created from over 4,000 Japanese TV episodes and movies on Netflix. \ |
| Furthemore, the 25th percentile was computed on the ranks of unique words in each video's subtitles. Getting a decent visualization for \ |
| something like this is actually a bit tricky due to the highly exponential nature of word-frequency distributions which are power laws.") |
|
|
| st.markdown("- One should bare in mind that the learner levels were labelled by a small group of experts and not a large number of learners. \ |
| In other words, the difficulty levels are not objective, but rather an approximation of difficulty / natural acquistion order.") |
|
|
| st.markdown("- There were a number of statistics I also tried but didn't get orderings from:") |
|
|
| st.markdown("1. **Audibility** - My hypothesis was that the teachers would speak more clearly in easy videos and less clearly in harder videos. \ |
| To test this, I generated whisper transcripts for each video's audio file, converted both the whisper transcript \ |
| and the original transcript to katakana and compared the character error rate. I found no differences in the levels. \ |
| Furthermore I can't tell if this moreso invalidates my original hypothesis or if whisper is just that good.") |
|
|
| st.markdown("2. **Word length** - At least in English and French (the languages I know best), longer words are generally considered harder. \ |
| My hypothesis was that the easier videos would use shorter words while the harder videos would use bigger words. \ |
| To test this, I parsed the transcripts and converted all words to katakana \ |
| to get a measure of how long the words were orally. I found no differences between the levels.") |
|
|
| st.markdown("3. **Range of vocabulary** - I suspected that easier videos may limit themselves to a smaller range of vocabulary than harder videos. \ |
| To measure this, I calculated unique word occurences / total word occurences but I found no ordering in the levels.") |
|
|
| st.markdown("4. **Other parts of speech** - I did test for orderings between the levels for other parts of speech such as: \ |
| proportion of adjectives, adpositions, coordinating conjunctions, interjections, particles and proper nouns \ |
| but ultimately didn't find any obvious orderings.") |
|
|
| st.markdown("5. **Other word frequency metrics** - You can probably guess from reading '25th percentile log rank', that this was not the first statistic I tried.\ |
| I also tried computing the un-logged ranks, the mean, median, 75th percentile and non-unique (repeated) word ranks from the videos, and while some of these led to\ |
| orderings, they were generally not very nice to visualize. I'm certain that there's got to be a nicer statistic for representing how rare the overall vocabulary in a text is. \ |
| But Zipf's law makes this a challenge.") |
