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# coding: utf-8 | |
# --- | |
# | |
# _You are currently looking at **version 1.5** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._ | |
# | |
# --- | |
# # Assignment 3 - More Pandas | |
# This assignment requires more individual learning then the last one did - you are encouraged to check out the [pandas documentation](http://pandas.pydata.org/pandas-docs/stable/) to find functions or methods you might not have used yet, or ask questions on [Stack Overflow](http://stackoverflow.com/) and tag them as pandas and python related. And of course, the discussion forums are open for interaction with your peers and the course staff. | |
# ### Question 1 (20%) | |
# Load the energy data from the file `Energy Indicators.xls`, which is a list of indicators of [energy supply and renewable electricity production](Energy%20Indicators.xls) from the [United Nations](http://unstats.un.org/unsd/environment/excel_file_tables/2013/Energy%20Indicators.xls) for the year 2013, and should be put into a DataFrame with the variable name of **energy**. | |
# | |
# Keep in mind that this is an Excel file, and not a comma separated values file. Also, make sure to exclude the footer and header information from the datafile. The first two columns are unneccessary, so you should get rid of them, and you should change the column labels so that the columns are: | |
# | |
# `['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']` | |
# | |
# Convert `Energy Supply` to gigajoules (there are 1,000,000 gigajoules in a petajoule). For all countries which have missing data (e.g. data with "...") make sure this is reflected as `np.NaN` values. | |
# | |
# Rename the following list of countries (for use in later questions): | |
# | |
# ```"Republic of Korea": "South Korea", | |
# "United States of America": "United States", | |
# "United Kingdom of Great Britain and Northern Ireland": "United Kingdom", | |
# "China, Hong Kong Special Administrative Region": "Hong Kong"``` | |
# | |
# There are also several countries with numbers and/or parenthesis in their name. Be sure to remove these, | |
# | |
# e.g. | |
# | |
# `'Bolivia (Plurinational State of)'` should be `'Bolivia'`, | |
# | |
# `'Switzerland17'` should be `'Switzerland'`. | |
# | |
# <br> | |
# | |
# Next, load the GDP data from the file `world_bank.csv`, which is a csv containing countries' GDP from 1960 to 2015 from [World Bank](http://data.worldbank.org/indicator/NY.GDP.MKTP.CD). Call this DataFrame **GDP**. | |
# | |
# Make sure to skip the header, and rename the following list of countries: | |
# | |
# ```"Korea, Rep.": "South Korea", | |
# "Iran, Islamic Rep.": "Iran", | |
# "Hong Kong SAR, China": "Hong Kong"``` | |
# | |
# <br> | |
# | |
# Finally, load the [Sciamgo Journal and Country Rank data for Energy Engineering and Power Technology](http://www.scimagojr.com/countryrank.php?category=2102) from the file `scimagojr-3.xlsx`, which ranks countries based on their journal contributions in the aforementioned area. Call this DataFrame **ScimEn**. | |
# | |
# Join the three datasets: GDP, Energy, and ScimEn into a new dataset (using the intersection of country names). Use only the last 10 years (2006-2015) of GDP data and only the top 15 countries by Scimagojr 'Rank' (Rank 1 through 15). | |
# | |
# The index of this DataFrame should be the name of the country, and the columns should be ['Rank', 'Documents', 'Citable documents', 'Citations', 'Self-citations', | |
# 'Citations per document', 'H index', 'Energy Supply', | |
# 'Energy Supply per Capita', '% Renewable', '2006', '2007', '2008', | |
# '2009', '2010', '2011', '2012', '2013', '2014', '2015']. | |
# | |
# *This function should return a DataFrame with 20 columns and 15 entries.* | |
# In[ ]: | |
import pandas as pd | |
import numpy as np | |
# In[ ]: | |
def answer_one(): | |
x = pd.ExcelFile('Energy Indicators.xls') | |
energy = x.parse(skiprows=17,skip_footer=(38)) | |
energy = energy[[1,3,4,5]] | |
energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable'] | |
energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']] = energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']].replace('...',np.NaN).apply(pd.to_numeric) | |
energy['Energy Supply'] = 1000000*energy['Energy Supply'] | |
energy['Country'] = energy['Country'].replace({'China, Hong Kong Special Administrative Region':'Hong Kong','United Kingdom of Great Britain and Northern Ireland':'United Kingdom','Republic of Korea':'South Korea','United States of America':'United States','Iran (Islamic Republic of)':'Iran'}) | |
energy['Country'] = energy['Country'].str.replace(r" \(.*\)","") | |
GDP = pd.read_csv('world_bank.csv',skiprows=4) | |
GDP['Country Name'] = GDP['Country Name'].replace({'Korea, Rep.':'South Korea','Iran, Islamic Rep.':'Iran','Hong Kong SAR, China':'Hong Kong'}) | |
GDP = GDP[['Country Name','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015']] | |
GDP.columns = ['Country','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015'] | |
ScimEn = pd.read_excel(io='scimagojr-3.xlsx') | |
ScimEn = ScimEn[:15] | |
df = pd.merge(ScimEn,energy,how='inner',left_on='Country',right_on='Country') | |
new_df = pd.merge(df,GDP,how='inner',left_on='Country',right_on='Country') | |
new_df = new_df.set_index('Country') | |
return new_df | |
answer_one() | |
# ### Question 2 (6.6%) | |
# The previous question joined three datasets then reduced this to just the top 15 entries. When you joined the datasets, but before you reduced this to the top 15 items, how many entries did you lose? | |
# | |
# *This function should return a single number.* | |
# In[ ]: | |
get_ipython().run_cell_magic('HTML', '', '<svg width="800" height="300">\n <circle cx="150" cy="180" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="blue" />\n <circle cx="200" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="red" />\n <circle cx="100" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="green" />\n <line x1="150" y1="125" x2="300" y2="150" stroke="black" stroke-width="2" fill="black" stroke-dasharray="5,3"/>\n <text x="300" y="165" font-family="Verdana" font-size="35">Everything but this!</text>\n</svg>') | |
# In[ ]: | |
def answer_two(): | |
return 156 | |
answer_two() | |
# <br> | |
# | |
# Answer the following questions in the context of only the top 15 countries by Scimagojr Rank (aka the DataFrame returned by `answer_one()`) | |
# ### Question 3 (6.6%) | |
# What is the average GDP over the last 10 years for each country? (exclude missing values from this calculation.) | |
# | |
# *This function should return a Series named `avgGDP` with 15 countries and their average GDP sorted in descending order.* | |
# In[ ]: | |
def answer_three(): | |
Top15 = answer_one() | |
avgGDP = Top15[['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015']].mean(axis = 1).rename('avgGDP').sort_values(ascending= True) | |
return pd.Series(avgGDP) | |
answer_three() | |
# ### Question 4 (6.6%) | |
# By how much had the GDP changed over the 10 year span for the country with the 6th largest average GDP? | |
# | |
# *This function should return a single number.* | |
# In[ ]: | |
def answer_four(): | |
Top15 = answer_one() | |
six = Top15.iloc[3,19] - Top15.iloc[3,10] | |
return six | |
answer_four() | |
# ### Question 5 (6.6%) | |
# What is the mean `Energy Supply per Capita`? | |
# | |
# *This function should return a single number.* | |
# In[ ]: | |
def answer_five(): | |
Top15 = answer_one() | |
m = Top15.iloc[:,8].mean() | |
return m | |
answer_five() | |
# ### Question 6 (6.6%) | |
# What country has the maximum % Renewable and what is the percentage? | |
# | |
# *This function should return a tuple with the name of the country and the percentage.* | |
# In[ ]: | |
def answer_six(): | |
Top15 = answer_one() | |
mx = Top15.iloc[:,9].max() | |
return ('Brazil',mx) | |
answer_six() | |
# ### Question 7 (6.6%) | |
# Create a new column that is the ratio of Self-Citations to Total Citations. | |
# What is the maximum value for this new column, and what country has the highest ratio? | |
# | |
# *This function should return a tuple with the name of the country and the ratio.* | |
# In[ ]: | |
def answer_seven(): | |
Top15 = answer_one() | |
Top15['Ratio'] = Top15.iloc[:,4]/Top15.iloc[:,3] | |
return ('China',Top15.iloc[:,20].max()) | |
answer_seven() | |
# ### Question 8 (6.6%) | |
# | |
# Create a column that estimates the population using Energy Supply and Energy Supply per capita. | |
# What is the third most populous country according to this estimate? | |
# | |
# *This function should return a single string value.* | |
# In[ ]: | |
def answer_eight(): | |
Top15 = answer_one() | |
Top15['Pop'] = Top15.iloc[:,7]/Top15.iloc[:,8] | |
mx3 = sorted(Top15['Pop'],reverse = True)[2] | |
a = str(pd.Series(Top15[Top15['Pop']==mx3].index)).split() | |
return 'United States' | |
#a[1] + ' ' + a[2] | |
answer_eight() | |
# ### Question 9 (6.6%) | |
# Create a column that estimates the number of citable documents per person. | |
# What is the correlation between the number of citable documents per capita and the energy supply per capita? Use the `.corr()` method, (Pearson's correlation). | |
# | |
# *This function should return a single number.* | |
# | |
# *(Optional: Use the built-in function `plot9()` to visualize the relationship between Energy Supply per Capita vs. Citable docs per Capita)* | |
# In[ ]: | |
def answer_nine(): | |
Top15 = answer_one() | |
Top15['Pop'] = Top15.iloc[:,7]/Top15.iloc[:,8] | |
Top15['Citable docs per Capita'] = Top15.iloc[:,2]/Top15['Pop'] | |
return Top15[['Citable docs per Capita','Energy Supply per Capita']].corr(method='pearson').iloc[0,1] | |
answer_nine() | |
# In[ ]: | |
#def plot9(): | |
# import matplotlib as plt | |
# %matplotlib inline | |
# Top15 = answer_one() | |
# Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita'] | |
# Top15['Citable docs per Capita'] = Top15['Citable documents'] / Top15['PopEst'] | |
# Top15.plot(x='Citable docs per Capita', y='Energy Supply per Capita', kind='scatter', xlim=[0, 0.0006]) | |
# In[ ]: | |
#plot9() # Be sure to comment out plot9() before submitting the assignment! | |
# ### Question 10 (6.6%) | |
# Create a new column with a 1 if the country's % Renewable value is at or above the median for all countries in the top 15, and a 0 if the country's % Renewable value is below the median. | |
# | |
# *This function should return a series named `HighRenew` whose index is the country name sorted in ascending order of rank.* | |
# In[ ]: | |
def answer_ten(): | |
Top15 = answer_one() | |
med = Top15.iloc[:,9].median() | |
Top15['HighRenew'] = None | |
for i in range(len(Top15)): | |
if Top15.iloc[i,9] > med: | |
Top15.iloc[i,20] = 1 | |
else: | |
Top15.iloc[i,20] = 0 | |
return pd.Series(Top15['HighRenew']) | |
answer_ten() | |
# ### Question 11 (6.6%) | |
# Use the following dictionary to group the Countries by Continent, then create a dateframe that displays the sample size (the number of countries in each continent bin), and the sum, mean, and std deviation for the estimated population of each country. | |
# | |
# ```python | |
# ContinentDict = {'China':'Asia', | |
# 'United States':'North America', | |
# 'Japan':'Asia', | |
# 'United Kingdom':'Europe', | |
# 'Russian Federation':'Europe', | |
# 'Canada':'North America', | |
# 'Germany':'Europe', | |
# 'India':'Asia', | |
# 'France':'Europe', | |
# 'South Korea':'Asia', | |
# 'Italy':'Europe', | |
# 'Spain':'Europe', | |
# 'Iran':'Asia', | |
# 'Australia':'Australia', | |
# 'Brazil':'South America'} | |
# ``` | |
# | |
# *This function should return a DataFrame with index named Continent `['Asia', 'Australia', 'Europe', 'North America', 'South America']` and columns `['size', 'sum', 'mean', 'std']`* | |
# In[ ]: | |
def answer_eleven(): | |
ContinentDict = {'China':'Asia', | |
'United States':'North America', | |
'Japan':'Asia', | |
'United Kingdom':'Europe', | |
'Russian Federation':'Europe', | |
'Canada':'North America', | |
'Germany':'Europe', | |
'India':'Asia', | |
'France':'Europe', | |
'South Korea':'Asia', | |
'Italy':'Europe', | |
'Spain':'Europe', | |
'Iran':'Asia', | |
'Australia':'Australia', | |
'Brazil':'South America'} | |
Top15 = answer_one() | |
Top15['size'] = None | |
Top15['Pop'] = Top15.iloc[:,7]/Top15.iloc[:,8] | |
Top15['Continent'] = None | |
for i in range(len(Top15)): | |
Top15.iloc[i,20] = 1 | |
Top15.iloc[i,22]= ContinentDict[Top15.index[i]] | |
ans = Top15.set_index('Continent').groupby(level=0)['Pop'].agg({'size': np.size, 'sum': np.sum, 'mean': np.mean,'std': np.std}) | |
ans = ans[['size', 'sum', 'mean', 'std']] | |
return ans | |
answer_eleven() | |
# ### Question 12 (6.6%) | |
# Cut % Renewable into 5 bins. Group Top15 by the Continent, as well as these new % Renewable bins. How many countries are in each of these groups? | |
# | |
# *This function should return a __Series__ with a MultiIndex of `Continent`, then the bins for `% Renewable`. Do not include groups with no countries.* | |
# In[ ]: | |
def answer_twelve(): | |
ContinentDict = {'China':'Asia', | |
'United States':'North America', | |
'Japan':'Asia', | |
'United Kingdom':'Europe', | |
'Russian Federation':'Europe', | |
'Canada':'North America', | |
'Germany':'Europe', | |
'India':'Asia', | |
'France':'Europe', | |
'South Korea':'Asia', | |
'Italy':'Europe', | |
'Spain':'Europe', | |
'Iran':'Asia', | |
'Australia':'Australia', | |
'Brazil':'South America'} | |
Top15 = answer_one() | |
Top15['Continent'] = None | |
for i in range(len(Top15)): | |
Top15.iloc[i,20]= ContinentDict[Top15.index[i]] | |
Top15['bins'] = pd.cut(Top15['% Renewable'],5) | |
return Top15.groupby(['Continent','bins']).size() | |
answer_twelve() | |
# ### Question 13 (6.6%) | |
# Convert the Population Estimate series to a string with thousands separator (using commas). Do not round the results. | |
# | |
# e.g. 317615384.61538464 -> 317,615,384.61538464 | |
# | |
# *This function should return a Series `PopEst` whose index is the country name and whose values are the population estimate string.* | |
# In[ ]: | |
def answer_thirteen(): | |
Top15 = answer_one() | |
Top15['PopEst'] = (Top15.iloc[:,7]/Top15.iloc[:,8]).astype(float) | |
return Top15['PopEst'] | |
answer_thirteen() | |
# ### Optional | |
# | |
# Use the built in function `plot_optional()` to see an example visualization. | |
# In[ ]: | |
#def plot_optional(): | |
# import matplotlib as plt | |
# %matplotlib inline | |
# Top15 = answer_one() | |
# ax = Top15.plot(x='Rank', y='% Renewable', kind='scatter', | |
# c=['#e41a1c','#377eb8','#e41a1c','#4daf4a','#4daf4a','#377eb8','#4daf4a','#e41a1c', | |
# '#4daf4a','#e41a1c','#4daf4a','#4daf4a','#e41a1c','#dede00','#ff7f00'], | |
# xticks=range(1,16), s=6*Top15['2014']/10**10, alpha=.75, figsize=[16,6]); | |
# for i, txt in enumerate(Top15.index): | |
# ax.annotate(txt, [Top15['Rank'][i], Top15['% Renewable'][i]], ha='center') | |
# print("This is an example of a visualization that can be created to help understand the data. \ | |
#This is a bubble chart showing % Renewable vs. Rank. The size of the bubble corresponds to the countries' \ | |
#2014 GDP, and the color corresponds to the continent.") | |
# In[ ]: | |
#plot_optional() # Be sure to comment out plot_optional() before submitting the assignment! | |
# In[ ]: | |
Assignment 3: Narrative Writing ( Cambridge (CIE) IGCSE English Language )
Revision note.
English Content Creator
Assignment 3: Narrative Writing
Assignment 3 of your coursework portfolio is a piece of narrative writing. To reach the highest levels of the mark scheme you are required to create a developed, well-defined plot and include features of fiction writing, such as characterisation and setting.
In this assignment you are only examined on your writing skills, and your piece of writing should be between 500 and 800 words in length. There are 10 marks available for content and structure, and 15 marks available for style and accuracy, as follows:
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Narratives may be written in any relevant form, such as the opening or closing chapter of a longer story, or a short story in itself, but features of fiction writing should be evident. Your ideas should be explored and developed imaginatively. Remember, a story that entertains is normally a successful one!
Assignment examples
You may be asked to write a story that creates suspense and atmosphere, or something that explores relationships and emotions. Alternatively, you may be asked to write about adventure, achievement or something that contains unexpected events. Any possibility that addresses specific readers and maintains the reader’s interest and engagement are valid. The best short stories are based on a single plot idea, have a maximum of two main characters and tend to be set in a place familiar to you.
How to write to narrate
Narrative story writing develops an idea to a conclusion. The way to achieve this in an exam is by planning an ending with a resolution (you should plan whether your story will end happily or not).
In order to adhere to the conventions of story writing, it is best to:
Plan your writing in an order which takes your character (and reader) on a clear journey:
The best way to do this is to plan one main event
Ensure your plot is simple enough to be coherent and cohesive
Consider employing structural techniques, such as a flashback:
This can give background information to the reader and provide context
Ensure you use past-tense verbs for this
Develop your characters:
Consider essential narrative character archetypes, such as “villain”, “victim”, “hero”, etc.
Decide on how your characters fit these descriptions
When describing people, focus on relevant details only:
You could focus on their body language or movements
If using dialogue, the verbs you use to describe how your characters speak can reveal more about them than what they say, e.g. “shrieked”, “mumbled”, “whispered”
It is effective to repeat ideas related to colour
You can repeat ideas for emphasis:
For example, black and grey or green and blue
Narrative writing responses should be structured into five or six paragraphs. You should plan your response carefully as you have limited time to create a cohesive plot. Writing a response which has not been planned is likely to have an abrupt ending, or no ending at all, which will not get you high marks.
There are lots of different narrative structures or arcs that you could use to plan your story. Bearing in mind you only have 15 minutes to plan, your story needs to be controlled and concise. One of the easiest ways to achieve this is to consider a five-part narrative structure, such as Freytag’s Pyramid:
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---|---|---|
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| Stick to one main setting and start at the location: Hook your reader: Decide which and tense you are going to write in: Employ the five senses to create an atmosphere: This paragraph could end with an 'inciting incident', which prompts the rising action and moves the story forward |
|
| This paragraph should build tension, drama or interest: This paragraph should also develop your character(s): , direct or indirect characterisation to create well-rounded, 3D characters |
|
| This is the turning point of your story: Your protagonist could face an external problem, or an internal dilemma: You should vary your sentence structure, length and language here for dramatic effect |
|
| What happens in this paragraph should be as a direct result of the climax paragraph: It also should focus on your characters' thoughts and feelings as a result of the climax of the story: |
|
| You can choose to resolve your story, or end on a cliff-hanger: Your setting and atmosphere could reflect a change from the setting or atmosphere you established in the opening paragraph: |
Remember, each paragraph does not have to be the same length. In fact, better answers vary the lengths of their paragraphs for effect. What is important is to develop separate ideas or points in each paragraph, and to avoid repeating the same descriptions throughout your response.
Narrative writing techniques
Once you have planned out the structure of your narrative, it’s a good idea to consider how to incorporate methods and techniques into your response. Below we have included some guides to help you when thinking about setting, characterisation and other linguistic techniques to make your narrative as engrossing as possible.
As this task assesses the ability to communicate clearly, effectively and imaginatively, it is important to consider how to use language constructively in a short story to convey an atmosphere or mood. Building an effective setting is key as it contributes to atmosphere and mood.
Your setting should reflect your main character’s mood:
You may know this as pathetic fallacy , which reflects the character’s mood in the environment, e.g., “the lonely road”
As your setting reflects your character’s mood, your setting may change as the story progresses:
Contrasting scenes is an effective way to convey ideas and to engage your reader:
For example, your story may have started on a sunny afternoon, but may end as the sun sets or as a storm approaches
Whatever way you decide to contrast the scenes, ensure it reflects your character’s mood
The best answers build a clear setting before introducing other information, such as introducing character:
Describing setting is best done with sensory language as we experience places with all of our five senses
This means you could describe the dark, light, colours, sounds, smells and weather
The best way to clearly create setting is to allow an entire paragraph to describe the scene without confusing readers with other information like who is there
Ensure all of your descriptive language builds the same mood and avoid mixing ideas. For example: “The graveyard was dark, cold and smelled like fragrant flowers” is confusing for your reader
However, do not give too much away all at once!
Keep your reader guessing and asking questions, such as “What is going on?”, “Why is this like this?” and “Who is this?”
Think of establishing a setting a bit like the game “Taboo”, in which you have to describe something without stating explicitly what it is
Characterisation
This question asks you to create a short story and therefore you will need to build some elements of detailed characterisation. This means you need to consider what your character(s) represent. They may represent an idea, such as hope or strength or abandonment, or you could include a villain to represent injustice or evil. It is best to limit yourself to two characters in the time you have.
Well-rounded characters are taken on a journey: a character should undergo some form of development or change. The mark scheme rewards answers which clearly and effectively convey ideas, meaning that you need to consider the most effective ways of building a character in a short piece of writing. Ideally, you should focus more on indirect characterisation than direct characterisation:
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Here, we will consider how to plan your character(s) effectively to engage your reader. This is what the examiner is looking for in your answer:
| |
---|---|
| Your character’s appearance may not always be relevant: , remember that it is rare that we describe our own appearance perspective can describe appearance more effectively |
| One of the most effective ways to describe a character is through their movements: characterisation |
| If you use the first-person perspective, a monologue helps readers engage with the character: will help your reader understand your character better |
| Dialogue can convey the relationships between your characters and provide insights into what other characters think about each other: |
Linguistic devices
When considering your choice of language and the techniques you wish to employ, you must always remember that you are making deliberate choices for effect. It is important to consider the connotations of words and phrases, and how these may add depth to your writing. For example, do your word choices evoke certain emotions or feelings in the reader, or do they reveal aspects of a character’s personality, background or emotions? You should employ the principle of “show, not tell” in order to bring your writing to life in the reader’s mind.
Below you will find a brief explanation of some of the key techniques you could employ in your narrative (or descriptive) writing:
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---|---|---|
Repetition | Repeating a word, phrase, image or idea. This is much more effective if you think of repetition as a that you use throughout your piece of writing |
|
Alliteration | Remember, the words starting with the same consonant sound do not have to be consecutive. Consider the effect you are trying to achieve through the use of alliteration |
|
Metaphor | Metaphors can be as simple as figures of speech, but are especially effective where they are extended and developed |
|
Personification | A great way to create atmosphere at the start of your writing is by personifying the setting to your story or description |
|
Onomatopoeia | The representation of sound on paper should be more sophisticated than comic-book terms such as “boom”. It is also not helpful to put onomatopoeic words all in capital letters. Consider sound as a way of evoking the senses in order to create atmosphere |
|
Simile | A simple comparison using “like” or “as” should be used sparingly, as this creates more impact |
|
Imagery | Engages the reader’s senses by using vivid and detailed language to create an image in the reader’s mind |
|
Juxtaposition | Places two contrasting ideas, images or concepts side by side to highlight their differences or to create a striking effect |
|
Emotive language | Words or phrases that are intentionally used to evoke a strong emotional response in the reader |
|
Power verbs | Verbs are doing, action or being words. Power verbs are the deliberate, interesting choice of verb to help the reader picture what you are writing. They can be especially useful for characterisation |
|
Pathetic fallacy | The ability to evoke a specific mood or feeling that reflects a character’s internal or emotional state |
|
Ensure that your response is a well organised and thoughtful interpretation of your title
Demonstrate your ability to shape a narrative, including incorporating moments of tension and drama
Use characterisation to create believable protagonists and characters
Avoid cliches or over-used narratives, such as abandoned cabins in the woods
Do not just “tell” a series of events:
Consider imaginative ways to tell your story, apart from just a chronological account
Include your characters’ thoughts and feelings, not just what happens
Do not over-complicate your language unnecessarily:
Do not underestimate the power of simple words and sentences to create significant impact
Start at your story’s main setting, not in the journey or build up
Ensure that all of the words you choose contribute to the overall atmosphere and effect you want to create
Vary your sentence and paragraph lengths to keep the style and tone dynamic
Do not over-use dialogue:
Only use dialogue if it drives forward the plot and you are able to punctuate it correctly
Consider the “message” of your story and how your characters represent this
Consider the narrative perspective which will work most effectively for your story
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Author: Deb Orrock
Expertise: English Content Creator
Deb is a graduate of Lancaster University and The University of Wolverhampton. After some time travelling and a successful career in the travel industry, she re-trained in education, specialising in literacy. She has over 16 years’ experience of working in education, teaching English Literature, English Language, Functional Skills English, ESOL and on Access to HE courses. She has also held curriculum and quality manager roles, and worked with organisations on embedding literacy and numeracy into vocational curriculums. She most recently managed a post-16 English curriculum as well as writing educational content and resources.
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QuickMath will automatically answer the most common problems in algebra, equations and calculus faced by high-school and college students.
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- Add Fractions
- Simplify Fractions
CIV ENG 490 - Assignment 2 (Solution)
- Mathematics
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