57152 Investigative Research in the Digital Environment
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particular session, location and mode of offering is the authoritative source
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Subject handbook information prior to 2020 is available in the Archives.
Credit points: 8 cp
Result type: Grade, no marks
There are course requisites for this subject. See access conditions.
This subject introduces advanced skills and methods for doing investigative research in the electronic environment, often referred to as computer-assisted research, not only for advanced information retrieval, but also for data mining and data and information analysis. Students develop their capacity to use contemporary computer-based methods of investigation in combination with other methods of inquiry and analysis to produce new knowledge and insights in relevant fields of practice that include information and knowledge management, journalism and other forms of social research. As well, students develop PIM (personal information management) techniques, including the creation of databases and digital repositories and explore how these can be used in the writing of research briefings for clients, reports for a range of publics, or investigative stories for audiences. This subject is designed for postgraduate students who already have basic information discovery and retrieval skills developed in information management, journalism, business, or other relevant field of practice.
Subject learning objectives (SLOs)
|a.||Explain the capacity of computer-assisted research to contribute the creation of new knowledge|
|b.||Apply advanced knowledge of information retrieval across business and government and non-government sectors|
|c.||Develop and use a range of advanced computer tools for mining and analysing data and information|
|d.||Disseminate new knowledge in the form of new information products and stories for audiences|
|e.||Manage and use their own data|
|f.||Explain the role of professional media and information professionals in making accessible complex information to a range of publics|
Course intended learning outcomes (CILOs)
This subject engages with the following Course Intended Learning Outcomes (CILOs), which are tailored to the Graduate Attributes set for all graduates of the Faculty of Arts and Social Sciences:
- Graduates are able to use advanced knowledge of professional practice to solve complex information and knowledge management problems in diverse organisational and cultural environments (1.1)
- Graduates are able to work with a high level of personal autonomy and accountability as well as collaboratively with peers, clients and the community at large (1.2)
- Analyse information and knowledge production flows and processes across a range of complex organisational environments (1.3)
- Locate, gather, organise and synthesise information across diverse platforms to guide their understanding of the relationships between people and organisations (2.1)
- Independently research contemporary issues and technologies in information/knowledge management to apply innovative solutions in a substantial project (2.2)
- Graduates are able to synthesise complex information and communicate it effectively to specialist and non-specialist audiences across a wide variety of media formats (6.1)
Teaching and learning strategies
This subject can be delivered in a range of modes including face-to-face weekly delivery as well as other flexible delivery, including with block face-to-face and online tutorials and discussions. Face-to-face classes and workshops will include classes in computer laboratories. The course incorporates a range of teaching and learning strategies including presentations, videos, exercises, practitioners’ presentation, project consultations and case studies. Resources will also be available online.
Topics to be covered include
- advanced information discovery and retrieval techniques
- databases and the hidden Web resources
- the development of personal information management systems
- monitoring techniques, including following trends and tracking news
- environmental scanning
- corporate investigations and competitive intelligence
- tools for data mining
- analysing and merging databases
- designing an investigation.
Assessment task 1: Advanced information retrieval techniques, and tools for data mining and analysis
b, c and e
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Assessment task 2: Presentation of a case study investigation
a, b, c and f
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Assessment task 3: Independent project using computer assisted research
a, b, c, d and e
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Classes are based on a collaborative approach that involves essential work-shopping and interchange of ideas with other students and the tutor.
In this subject assessment tasks are cumulative so that each task builds understanding and/or skills, informed by formative feedback. Consequently, all assessments must be submitted in order for you to receive feedback. Students who do not submit all assessments will not pass the subject.
There is no set text book for this subject.
Meirelles, I. 2013, Design for Information, Rockport Publishers, MA [available as an E book]
Tufte, E. 2001, The visual display of qantitative information, 2nd edn Graphics Press, Cheshire, Conn.
Wong, D. 2013, The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures, W.W. Norton & Co, New York
Azzam, T. et al. (eds) 2013, Data visualization, Part 1: New Directions for Evaluation, Jossey-Bass (eBook)
Benoit, G. 2019, Introduction to information visualization: Transforming data into meaningful information, Rowman & Littlefield, New York (eBook)
Krum, R. 2014, Cool Infographics: Effective Communication with Data Visualization and Design, John Wiley & Sons.
Chen, C. 2006, Information Visualization: Beyond the Horizon, Springer-Verlag, London [e-Resource]
Few, S. 2013, Information Dashboard Design: Displaying Data for at-a-Glance Monitoring, Analytics Press, Burlingame, CA.
Hammerbacher, J. & Segaran, T. (eds) 2009, Beautiful data: the stories behind elegant data solutions, O'Reilly Media, Sebastopol, CA (eBook).
Henninger, M. 2008, The hidden web: finding quality information on the net, 2nd edn, UNSW Press, Sydney NSW.
Gray, J., Chambers, L. & Bounegru, L. (eds), 2012, The Data journalism handbook: How journalists can use data to improve the news [online], O'Reilly Media, <http://datajournalismhandbook.org/>; copy also in UTS Library
Kerren, A., Stasko, J., Fekete, J. & North, C. (eds), 2008, Information visualization: human-centered issues and perspectives, Springer, New York
Markey, K. 2015, Online Searching: A guide to finding quality information efficiently and effectively, Rowman & Littlefield, Lanham, Maryland.
Segel, E. & Heer, J. 2010, 'Narrative visualization: Telling stories with data', IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1139-1148.
Shander, B. 2016, Data Visualization Storytelling Essentials, lynda.com, Carpenteria, CA.
Tufte, E.R. 1997, Visual explanations: images and quantities, evidence and narrative, Graphics Press, Cheshire, Conn.
———1990, Envisioning information, Graphics Press, Cheshire, Conn.
Walkenbach, J., 2015, Microsoft® Excel® 2016 Bible [ebook], Wiley, Indianapolis, Ind.
Ware, C. 2013, Information visualization: perception for design, 3rd edn, Morgan Kaufmann, San Francisco.
Yau, N. 2012, Visualize this!, John Wiley & Sons, Indianapolis, Ind. (eBook)
LinkedIn Learning (via the library) Tutorials (Excel, Tableau)
Additionally, specific readings will be assigned on a week-by-week basis and available via UTS eReadings or UTS Library Databases