How do university students spend their time for study and sleep are we getting enough?

How do university students spend their time for study and sleep are we getting enough?

The following statements are based on a weekly study, students were asked to document how much time was spent during the week conducting activities. The two areas of focus in this data visualisation will look into time spent sleep and conducting university related activities. University attendance and study times are based on students with a full-time university workload a 40 credit point semester.



49 to 64 Hours per week

7 to 9 Hours per day


40 Hours per week

5.7 Hours per day

A survey in the United States covered by Washington Post states college students miss the study goal by half.

So just how much time does the our class spend?

Class Total Hours


As a class of 35 students in one week we slept a total of 2054 hours and 898 hours for university which totals 2952 hours for these two categories alone.

Total Hours in a week 5880 hours for the whole class, 2952 is spent sleeping and university


Activities spent overtime

August 17 Wednesday


August 18 Thursday


August 19 Friday


August 20 Saturday


August 21 Sunday


August 22 Monday


August 23 Tuesday


As a class our sleeping pattern is quite consistent 9am to 10am is a peak time for most to get up in the morning. Going to bed is a similar story peaking at 9pm with Saturday moving to 10pm. University attendance and study peak the moment we get up in the morning. Study times start persistent throughout the day until it is time for bed.

How do we individually spend our time?

Weekly Student Total Hours


Most hours totalled 111 hours

Least hours totalled 59 hours

Sleep Weekly Total Hours



Most hours totalled 69.5 hours

Least hours totalled 42.5 hours

Average Sleep Time per week 58.7 hours

University Weekly Total Hours


Most hours totalled 59 hours

Least hours totalled 12 hours

Average Sleep Time per week 25.7 hours


As a class our we get up at a reasonable time around 9am to 10 am sleeping well with range of the recommended sleep time. Though the amount of time we sleep takes a large portion of the day away from University. As student we work consistently throughout the day slowing growing with a few dips here are there until it is time for sleep. The amount of time we allocate towards University falls short unfortunately whilst is it close to the statement made by the Washington post as a class average we only out on 14.3 hours of the 40 we’re expected to conducted. I believe that this statement holds some truth but is not as bad as what it is perceived to be with our class as Western Sydney University.


Shepherd, J. (2012). University students spend no more time with lecturers than six years ago.the Guardian. Retrieved 17 October 2016, from

Vise, D. (2012). Is college too easy? As study time falls, debate rises. Washington Post. Retrieved 17 October 2016, from


Week 6 – The Beauty of Data Visualisation

Week 6 – The Beauty of Data Visualisation

In this talk we get insight to how data visualisation impact on storytelling through the use of facts, statistics and numbers. The connections made has the ability to impact views and perceptions of viewers.


Ballon Race (TedGlobal, 2010)

David McCandless talks about many different visualisations that he has created, one of the visualisations discussed was on the topic of health. It was an analysis of supplements in relation to the evidence surrounding its benefits they provide. The higher up a bubble the more evidence supporting evidence there was available, the size of the circle indicates the amount of google hits it has received essentially how times has the topic been visited.

What I liked about this was how it was able to achieve and compress all the data available into a visualisation that is simple and easy to use. The incorporation of relative figures easily leads the viewer to rethink about supplement choices.

Design information so that it makes sense tells a story or only focuses that information that is important – David McCandless

What I took away from this lecture was that data visualisation can be beautiful but it also has the ability to tell stories and change perspectives. Viewers to easily digest information and quickly find the answer to their questions much more efficiently.


TedGlobal,. (2010). David McCandless: The beauty of data visualization. Retrieved from

Week 5 – Data Journalism

Week 5 – Data Journalism

What is data Journalism?

The use if key information sets, key data key references elements to inform a story

Professor Nigel Shabolt

“It’s not the existences data not just obtaining it it’s the processing that foes into it to work out what it tells you. You have to ask the right questions to get the right answers”

James Ball

  • Not confirmed by tent because you’re a newspaper
  • Interactive map
  • Clear pictures
  • Creates something understandable/enjoyable to the user

Professor Christoreoes Anagnostopoulos

“Recognition and power of measurement in helping conversations”

History of Data Journalism The Guardian


(History of Data Journalism at The Guardian, 2013)

1821 First edition “Manchester Guardian”

1901 Graphic made out of type (letters)

1916 October The battle of the somme

1938 The Manchester Guardian Commercial – Stacked proportional line chart

1943 The Manchester Guardian – Use of symbols represent 101

1957 November 10 The Observer -Paper becomes more visual, use of pictures and diagrams

2013 Guardian Datablog

The London Olympics


Data journalism in action: the London Olympics (The Guardian, 2013)

This interactive data visualisation was design by Garry Blight the piece tracked how richer countries could be penalised grabbed a lot of attention. With the need to be constantly updating day to day the use of google spreadsheets was implemented comparing all the different countries.

After viewing this weeks lectures it was interesting to see the development and history behind Data Visualisation through the Guardians early beginnings to where it is now. The insights of what Data Visualisation gain from the interviews was also particularly interesting as each had their slightly own take on it purpose of what it meant to them.


History of Data Journalism at The Guardian. (2013). Retrieved from

The Guardian,. (2013). Data journalism in action: the London Olympics. Retrieved from

Week 4 – Data Presentation Styles: Why use Graphs

Week 4 – Data Presentation Styles: Why use Graphs

Our brain and eyes are better at comparing a singular dimension such as strength but it is much harder for use to calculate surface area (height x width)screen-shot-2016-10-19-at-3-33-25-pm

You might want to compare area of a circle but they will generally calculate height and width the area of circles is harder to distinguish compared to squares. Using circles levels always leads to underestimated size difference

Three most common charts in use:

Time Chart – Stock market

Bar Chart – one dimensional

Scatter Plot – variables on each axis

What they’re used for

Bar Chart


(Are Movie Sequels Profitable?, 2011)

  • Used a lot
  • East to use
  • Familiar
  • Quick to compare information
  • Nominal data splits into different categories
  • Comparing data across categories

Line Chart


(Line Graph, n.d.)

  • Its popular as bar charts
  • Used to compare individual numerical data points
  • A sequence of values
  • Primary use to displaying trends (stock price)

Pie Chart


(Pie Chart, 2015)

  • Used to show relative proportions or percentages of information
  • If company lots od data use bar chart not a pie chart
  • Comparing two pie charts together creates to much work for the viewer
  • Limit the number of wedges to your pie chart to 6
  • More than six you should consider a bar chart

An important note in this lecture points out how we as designers often design based on aesthetic which creates design that it is good; but for design to be great must be able to make comparisons easier.


Are Movie Sequels Profitable?. (2011). Retrieved from

Line Graph. Retrieved from

Pie Chart. (2015). Retrieved from

Week 3 – Historical and Contemporary Visualisation

Week 3 – Historical and Contemporary Visualisation

In this lecture we discover historical developments in the field of data visualisation.

Napoleon’s Invasion of Russia 1812

Napoleon’s Russian Invasion in 1812 Napoleon’s grand army of over 400,000 men journey to Moscow to a town that had already been abandoned. They would have to journey back and they found it difficult to supply the army on the journey back due the harsh weather. The lacking in supplies ultimately took its toll on the men and their horses which carried the supplies leaving the army to trek on foot.

Charles Joseph Minard published an info-graph on this campaign. The left is the polish border on the right is Moscow. The thickness of the line indicates the strength of the army starting at 422,000 men finishing in Moscow with 100,000 men. The darker line indicates the army going back finishing with 10,000 men. Along the bottom we can see the difference.


Napoleon’s 1812 Russian campaign army (Minard, 1869)

Florence Nightingale Crimean War 1958

Soldiers were dying from malnutrition, poor sanitation and lack of activity Nightingale strived to improve the living conditions of the troops keeping records of every patient turning those records into graphs to create an argument to the British commanders.


Coxcomb diagram on mortality in the army (Nightingale, 1858)

Otto Neurath

The starting point for Neurath’s graphical development was in Museum of Economy and Society. The mission was to create social and economic relationships understandable for the uneducated the means of the exhibition was though the means of visualisation education through the eye

He developed a system called the Isotype – International System of Typographic Picture Education One of the innovations he made was the serialisation of images popularising the use of multiples of the same size


The World’s Motor Car Industry in 1929 (Neurath, 2011)

Here each car represents a production of 100,000 cars

Why we visualise?

Not just pretty pictures but to gain insight and understand complex issues

Looking at the worlds population growth we look to find out the trends in Rich countries in comparison of developing countries.

This graph shows all the countries in the world but is visually hard to distinguish.


If we take that same graph and add visual hierarchy taking into account a few countries from rich and developing countries we can see visually see a more clearer answer.


The amount of Information being presented now if far greater than in comparison to those earlier years. Information you show can be as important as what you hide. A goal of any graphic is to be for your eyes and brain to perceive what lies beyond their natural reach

If you don’t present your data to be able to read it explore it and analyse it you need to convince them or give you need the information to convince them.

What I found to be important note in this lecture was how we can visualise data to help educate viewers based on the story we want to show and tell. It it not just about telling your story but also providing audiences with a tool to create their own understanding grasping the idea and ultimately make the analysis themselves as well.



Minard, C. (1869). Napoleon’s 1812 Russian campaign army. Retrieved from

Neurath, O. (2011). The World’s Motor Car Industry in 1929. Retrieved from

Nightingale, F. (1858). Coxcomb diagram on mortality in the army. Retrieved from

Week 2 – Data Types

Week 2 – Data Types

In this lecture we discover four different data types Nominal, Ordinal, Interval and Ratio and their meanings.

Nominal Data

Nominal data is the Latin word for Nomen – pertaining to names essentially meaning named categories and is unordered. It is also important that this type of data cannot be average An example would be the section a certain type of food came from whether that be (canned, frozen, produce and dairy).




Ordinal is all about order. With no true numerical value meaning even when numbers are assigned they are used for data analysis. An example of this would be calculating which line is the quickest to get out of the store; these are then broken down into (short lines, medium lines and long lines).



Interval Data

Interval data refers to the amount of time between each given point. This type of data is numeric. It is also important that it doesn’t have a meaningful zero point 0am doesn’t mean the absence of time it just means the start of a new day 12:15 to 12:30pm and 12:30 to 12:45 are both consider interval data with both having equal value.


Ratio Data

Ratio data is numeric with a meaningful 0 point. Zero is considered to be the absence of data that is being measured you don’t have anything of that type. Some examples would be age, money, weight and height.


The most important part of the lecture I believe to be “Do data types matter?” Simply the answer is Yes, it is vital that we label our different data types as this could very easily lead to mistakes and wrong calculations through analysis work. Without it you could average out the numbers of postcodes, which could be easily avoided.

Week 1 – Introduction to Data Visualisation

Week 1 – Introduction to Data Visualisation

Lecture Podcast

The lecture discussed some interesting points on what is data visualization and its purpose. Essentially data visulisation is a medium in which it speaks to an audience as visual communicators it is an essential part in our process to communicate stories and messages.

The amount of data consumed was said to be 23 exabytes (1 exabyte = 1 billion gigabytes) of data was recorded and replicated in 2002 (UC Berkeley’s School of Information Management and Systems, 2003).

In comparison of today we now consume that amount of data within seven days.

“There is a tsunami of data that is crashing onto the beaches of the civilised world. This is a tidal wave or unrelated, growing data formed in bits and bytes, coming in an unorganised, uncontrolled, incoherent cacophony of foam. None of it is easily related, none of it comes with any organisation methodology…”.  Richard Saul Wurman (1997)

With the growth of data, many developments and strategies have arisen to deal with issues presented. Our political, environmental and social systems of today are trying to be understood through the use of data.

The visualisation of data it involves the creation of the visual representations of data through the use of graphs and images. Data on its own carries no meaning for data to be considered information it must be interpreted and take on a meaning. Not all information visualisations are based on data, but all data visualisation are information visualisations

The most important point I think made in the lecture would have to be the following

“Data on its own carries no meaning for data to be considered information it must be interpreted and take on a meaning.”

The reason why I believe this to be important as it makes a point about the importance of interpretation as without it, data is consider meaningless. As visual communicators we can give data purpose and meaning, when bring it together into the form of graphs and images which speaks to an audience.


The 4X4 Model for Winning Knowledge Content

The 4X4 Model is a guide to getting a group or individuals to your website and or other online content. The model discusses a strategy  that should be implement to retain audiences through the use of informative and engaging media.

The Water Cooler

Often in the form of a headline, tweet or ad, the Water Cooler grabs attention, engages and brings back users to wanting more. These moments should be found throughout a website.

The Cafe

A place of gather to discuss a subject in length often a blog post of video. They explain ideas and need to tell a interesting story that is easily relatable

The Research Library

Users here are seeking for scholarly content that provides the research and baked up data. Content should include at least one of the following and executive summary or chapter introduction or some other Café content.

The Lab

Users can interact with data found from the Library. It is considered to be the rarest form of data but give the opportunity for users to develop their own interpretation of data.

The model consists of four components

  1. Visualisation
  2. Story-Telling
  3. Interactivity
  4. Shareability
4X4 A model for Knowledge Content
4X4 A model for Knowledge Content



The 4X4 Model for Winning Knowledge Content Online. (2011). Inspired Magazine. Retrieved 20 July 2016, from

UC Berkeley’s School of Information Management and Systems,. (2003). How much Information?. University of California, USA. Retrieved from


International Civilian Air Traffic in 2009. (2009). Retrieved from

KA Connect,. (2014). The 4X4 Model for Winning Knowledge Content. Retrieved from