MCT542 - Digital Investigation

SchoolCardiff School of Journalism, Media & Cult'l Stud
Department CodeJOMEC0
Module CodeMCT542
External Subject CodeP500
Number of Credits20
LevelL7
Language of DeliveryEnglish
Module Leader Mr Glyn Mottershead
SemesterSpring Semester
Academic Year2016/7

Outline Description of Module

The module draws on Hunter’s Story-Based Inquiry method coupled with Agile methodologies to allow students to develop investigative journalism skills within a community framework. It’s focus is on real-world issues and investigating issues that matter to those communities.

It focuses on the American tradition of Computer-Assisted Reporting (using spreadsheets, database management and interrogation, mapping and other open source tools) to gather and investigate information, including the necessary data handling techniques from information security protocols through to the use and handling of statistical information. These skills have evolved into some of the more technically-demanding skills of data journalism, for example using frameworks such as Python to build scrapers to gather data and then how to visualise it using industry-standard frameworks. These skills will be explored in practical and taught sessions to allow students to develop skills which will be of use in the modern newsroom.

The aim of the module is to allow students to develop skills in inquiry led digital investigation and will allow them to gather and interrogate information, in order to visualise it using tools such as Chartbuilder or d3.js - or to build “stories as a service”, interactive projects such as Trinity Mirror’s schools site, the work of Propublica and the BBC’s house prices app which go beyond storytelling with supporting visualisations or data explainers.

On completion of the module a student should be able to

●  Develop an understanding of the story-based inquiry method and hypothesis driven research

●  Apply and develop storytelling skills from the Digital Journalism module

●  Develop an understanding of core statistical analysis techniques and their application

●   Sources of data

●  Basic data handling

●  Data analysis and statistics

●  Data storage and sharing

●  How to deal with the legal, ethical, licensing and release issues that      will occur in an 
investigation

●  Safety and confidentiality

●  Community engagement and crowd investigation

●  Visualisation techniques

●  Story development

●  An introduction to how data science fits into the newsroom and how     results can be 
translated accurately for media audiences 

How the module will be delivered

The module is taught in weekly lectures/taught sessions - by both module staff and guest lecturers - which will develop understanding of the key elements of the module. The students will be expected to engage in further self-directed study. There is also a weekly whole group supervised session which will allow tutors to support students with clinics in both coding and journalistic elements of the module, to encourage problem solving and building on the assessed projects.

Skills that will be practised and developed

Please see Learning Outcomes

How the module will be assessed

1.          Group investigation project 70%
This is an opportunity for small teams to develop and instigate a real world investigation around an agreed topic. The teams will be expected to record their progress as they use both data and community interviews to source information. Regular releases of information and insights will be shared via a project site, culminating in an in-depth data driven feature or “story as a service”. Links to both the site and substantive project will be submitted to the course team.

2.          Reflective report 30%
Each team member will be expected to keep a reflective journal of their learnings from formal sessions and from the project, which will be submitted as part of a reflective report into the project and its outcomes. 

Feedback

You will receive detailed written comments regarding each assignment. All assessed coursework will be returned within 4 weeks.

Assessment Breakdown

Type % Title Duration(hrs) Period Week
Written Assessment 70
Data Investigation Project
N/A 1 N/A
Written Assessment 30
Project Reflection
N/A 1 N/A

Syllabus content

Week 1 What is digital investigation?
Project planning and setting up virtual environments

Week 2 Digital investigation in the newsroom Story as a service

Week 3 Information gathering - FOIA, scraping and ethics
The ethics of the algorithm and the importance of accuracy

Week 4 Analysis - statistics, interviews and confidence
Analysis of data (t-tests, ANOVA, p-values, confidence-intervals, correlation)

Week 5 Community driven research - on and offline Week 6 Telling stories with words and visualisations Week 7 Building the framework
Week 8 User need and user experience

Week 9 Building the project Week 10 Building the project Week 11 Project showcase

Essential Reading and Resource List

Course resources

Both the syllabus and key resources will be available via a module website. Key code examples will be available on the module Github repository (link to come)

Background Reading and Resource List

Banavar G S, Cohen N H and Narayanaswami C. (2010) “Pervasive Computing: An Application-Based Approach” Wiley-Blackwell

Bradshaw, P., Rohumaa, L., 2011. The online journalism handbook  : skills to survive and thrive in the digital age, 1st ed. ed. Longman, Harlow  ;;Essex  ;;New York.
Bradshaw, P., 2013. Scraping for Journalists, Leanpub
Brand, S., 1988. The Media Lab: Inventing the Future at M. I. T., First Edition. ed. Penguin (Non-Classics).

Benyon, D (2010) “Designing Interactive Systems: A comprehensive guide to HCI and Interaction Design, Pearson
Blastland, M., Dilnot, A., 2008. The Tiger That Isn’t: Seeing Through a World of Numbers. Profile Books Ltd.

Boslaugh, S., Watters, P. A., 2008. Statistics in a Nutshell. O’Reilly
Briggs, M., 2010. JournalismNext: a practical guide to digital reporting and publishing. CQ Press, Washington, D.C.
Cooper, A. (1999) "The Inmates are Running the Asylum", Indianapolis, SAMS Publishing. DeFleur, M.H., 1997. Computer-Assisted Investigative Reporting: Development and Methodology. Lawrence Erlbaum Associates, Inc, New Jersey, USA.
Diakopoulos, N Cultivating the Landscape of Innovation in Computational Journalism. Tow- Knight Center for Entrepreneurial Journalism. April 2012.
Gray, J., Chambers, L., Bounegru, L., 2012. The Data Journalism Handbook. O’Reilly Media. Gynnild, A., 2013. Journalism innovation leads to innovation journalism: The impact of computational exploration on changing mindsets. Journalism.
Hansen C D and Johnson C R. (2004) “Visualization Handbook”, Academic Press
Holmes, T., Hadwin, S., Mottershead, G., 2012. 21st century journalism handbook: essential skills for the modern journalist. Pearson, Harlow.
Huang W, Alem L and Livingston M A. (2013) “Human Factors in Augmented Reality Environments”, Springer, ISBN: 978-1-4614-4204-2 (Print)
Hunter, M.L. (Ed.), 2011. Story-based inquiry a manual for investigative journalists. UNESCO, Paris.
Marshall, S., 2012. How to: bring agile into the newsroom. journalism.co.uk.
McCandless, D., 2012. Information is beautiful. Collins, London.
Meyer, P., 2002. Precision Journalism - A reporter’s Introduction to Social Science Methods, fourth edition. ed. Rowman and Littlefield, Lanham, USA.
Miller, C. 2013. Getting Started With Datajournalism: Writing Data Stories In Any Size Newsroom, Leanpub.
Norman, D. (1990) "The Design of Everyday Things", New York, Doubleday.
Paul, N.M., 1999. Computer-Assisted Research: A Guide to Tapping Online Information. Bonus

Books Inc and The Poynter Institute forMedia Studies, USA.

Raskin, J. (2000) "The Humane Interface", Boston, Addison-Wesley.
Sense about Science, Straight Statistics, 2010. Making sense of statistics. Sense About Science.
Tufte E R. (1990) “Envisioning Information” Graphic Press USA.
Identity Management: Concepts, Technologies, and Systems, Bertino E and Takahashi K, Artech House Publishers,
ISBN 9781608070398
Network Security: Private Communication in a Public World, Kaufman C, Perlman R, and Speciner M. (2nd edition), Prentice Hall , ISBN 0130460192

Security Engineering, 2nd edition, Anderson R J, John Wiley, ISBN 978-0-470-06852-6 “Social Network Analysis for Startups: Finding connections on the social web” by Maksim Tsvetovat and Alexander Kouznetsov, O’Reilly Media, 2007.
“Graph Theory and Complex Networks”by Maarten Van Steen, 2010.

Think Python! (http://greenteapress.com/thinkpython/thinkpython.html)
Learning Python, Lutz, 4th edition, O’Reilly
Python Cookbook, Alex Martelli, 2nd edition, O’Reilly, 2010
Database Systems: A Practical Approach to Design, Implementation and Management, 5th Edition,T. Connolly and C. Begg, Addison-Wesley, 2009.

Data Structures and Algorithms, Michael T. Goodrich, Wiley

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