Students
MECO826 – Data Journalism
2017 – S2 Day
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Margie Borschke
Y3A 159
by appointment
Alex Mesker
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|---|---|
| Credit points |
Credit points
4
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| Prerequisites |
Prerequisites
Admission to MFJ or MCreIndMFJ
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
This unit focuses on innovative approaches to finding, reporting, producing and interacting with media stories through the use of digital technologies and data structures. Students will critically analyse and gain practical experience in data-driven reporting techniques, the principles of information design and the production of data visualisations. The unit also covers user experience, gamification, collaboration and the potential uses and limitations of 'big' data for journalistic practice.
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Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
General Assessment Information
Late Submissions Tasks: Students who submit late work without an extension will receive a penalty of 10% per day. This penalty does not apply for cases in which an application for Disruption to Studies is made and approved.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Portfolio: Deconstructed Data | 35% | No | September 15, 2017 23:59 |
| Data Story Pitch and Plan | 25% | No | October 11, 2017 |
| Data Journalism Project | 40% | No | November 3, 2017 23:59 |
Portfolio: Deconstructed Data
Due: September 15, 2017 23:59
Weighting: 35%
You will submit:
- a Portfolio that is made up of three blog posts from the Community of Inquiry Blog (apx equiv 500 words each; weeks 1-7).
- a 250 word overview/introduction that reflects on the work completed and contextualises the work within key debates and discussions
Format: Submit your portfolio as a word doc with text and links to your MECO 826 blog posts (Meco826.ltc.mq.edu.au) via Turnitin box on iLearn.
Detailed instructions will be issued each week to guide your posts.
- Analysis: Demonstrate that you can identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history
- Case Studies: Your ability to choose appropriate examples to analyse and evaluate concepts and approaches to news gathering and storytelling
- Context: Your post will demonstrate that you can situate literacies in journalism within debates about journalism and its social function.
- Presentation: Clarity of expression and appropriate use of the platform.
Assessment Style & Feedback:
This is a formative assessment. You will receive informal feedback on your posts each week. Your formal feedback will include a grade out of 100, a qualitative rubric and written comments.
On successful completion you will be able to:
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Data Story Pitch and Plan
Due: October 11, 2017
Weighting: 25%
Each Student will pitch their data-driven story idea to the class in week 9. You will have 10 minutes to present and another 10 minutes for discussion.
Students should post all relevant presentation materials to your Blog prior to class including links to relevant data sets, tools and back up research. You will use this post to talk the class through your idea and plan. (You may use presentation platforms or powerpoints but it is not required.)
You should also acknowledge and discuss any existing pieces of Data Journalism or Visualizations that inspired your approach.
Assessment Criteria:
- Research: The breadth and depth of your background research and your ability to identify relevant datasets
- Reporting: Demonstrate that you have developed a workable plan to report your story
- Storytelling: Show that you understand how you use data to find stories and/or how you use data to tell stories.
- Innovation: Your plan includes suitable use of the tools and products of data journalism
- Presentation and Persuasion: Quality of your pitch as a form of persuasive communication
On successful completion you will be able to:
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Data Journalism Project
Due: November 3, 2017 23:59
Weighting: 40%
A data driven feature or series of stories that should include a headline, an introduction and three-four original visualizations* including links to all relevant data sets and sources. (*You may storyboard any ideas that you are unable to produce due to technical limitations but please discuss this approach with your lecturer ahead of time.) (apx 1500 words)
A reflection that explains your process and decisions and critically assesses the benefits, challenges and limits of data driven journalism. Also discuss how you would promote your project via social and online media. Please cite relevant literature. (apx. 750 words)
You should post your project to the blog (or a link to it if you have posted it at an external site) and submit a copy as a word doc via the turnitin link on the iLearn site.
Assessment Criteria:
- Research: The quality of the research you conducted including your background research, your ability to identify suitable datasets or gather suitable data
- Reporting: The quality and relevance of your reporting including your choice of sources and selection of facts and quotes
- Visualizations/Interactivity: The success of your visualizations or interactives as a form of effective non-fiction storytelling
- Reflection and context: Show that you can contextualise your project within the key debates of the field including ethics, audience engagement, effective storytelling and efficiency.
- Social Media: Demonstrate an understanding of how to promote and redistribute your feature in social media channels.
This is a summative assessment: Feedback will include a grade out of 100, a qualitative rubric and a comment about your project.
On successful completion you will be able to:
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Delivery and Resources
A collaborative learning space can be found at meco826.ltc.mq.edu.au. Students will need their oneID to login. Information about weekly readings and learning activities will be available via iLearn.
Policies and Procedures
Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:
Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html
Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html
Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html
Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Student Code of Conduct
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Results
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Student Services and Support
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
Student Enquiries
For all student enquiries, visit Student Connect at ask.mq.edu.au
IT Help
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
MMCCS Session Re-mark Application http://www.mq.edu.au/pubstatic/public/download/?id=167914
Information is correct at the time of publication
Graduate Capabilities
PG - Capable of Professional and Personal Judgment and Initiative
Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project
PG - Discipline Knowledge and Skills
Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project
PG - Critical, Analytical and Integrative Thinking
Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project
PG - Research and Problem Solving Capability
Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project
PG - Effective Communication
Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project
PG - Engaged and Responsible, Active and Ethical Citizens
Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues
This graduate capability is supported by:
Learning outcomes
- Identify and critically analyse the practice of data journalism, its products (including visualizations and interactive reporting) and its history.
- Demonstrate an understanding of how to gather, analyse and interpret data as a journalist.
- Apply advanced reporting and storytelling techniques to find and produce stories that inform, educate and entertain.
- Identify and analyse new literacies in journalism.
- Situate the focus on data and visualization within debates about journalism and its social function.
Assessment tasks
- Portfolio: Deconstructed Data
- Data Story Pitch and Plan
- Data Journalism Project