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57152 Investigative Research in the Digital Environment

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2020 is available in the Archives.

UTS: Communication: IKM and Digital Studies
Credit points: 8 cp
Result type: Grade, no marks

There are course requisites for this subject. See access conditions.

Description

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.

Content (topics)

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

Assessment task 1: Advanced information retrieval techniques, and tools for data mining and analysis

Objective(s):

b, c and e

Weight: 30%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
effectiveness of tools and techniques 30 b 2.1
depth of analysis 25 c 1.3
coherency of argument 20 c 1.3
awareness of the literature 10 e 6.1
depth of critical reflection 15 e 1.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 2: Presentation of a case study investigation

Objective(s):

a, b, c and f

Weight: 30%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
relevance of methodology 25 a 1.2
critical analysis 35 b 1.3
coherency of argument 25 c 6.1
clarity of presentation 15 f 6.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Assessment task 3: Independent project using computer assisted research

Objective(s):

a, b, c, d and e

Weight: 40%
Criteria linkages:
Criteria Weight (%) SLOs CILOs
relevance to user or audience 10 a, d 6.1
appropriateness and range of research techniques 15 b 2.1
accuracy and analysis of data 20 c 1.3
depth of research 10 b 2.1
effectiveness of problem solving 15 d 2.2
clarity of presentation 15 d 6.1
citation of sources 5 e 1.1
reflective practice 10 e 1.1
SLOs: subject learning objectives
CILOs: course intended learning outcomes

Minimum requirements

Attendance is essential in this subject. Classes are based on a collaborative approach that involves essential work-shopping and interchange of ideas with other students and the tutor. A roll will be taken at each class. Students who have more than two absences from class will be refused final assessment (see Rule 3.8).?

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.

Required texts

There is no set text book for this subject.

Recommended texts

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

References

Azzam, T. et al. (eds) 2013, Data visualization, Part 1: New Directions for Evaluation, Jossey-Bass (eBook)

Krum, R. 2013, 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]

Dalgleish, D. 2007, Excel pivot tables recipe book: A Problem-solution approach [electronic resource], Apress, Berkeley, CA.

Few, S. 2013, Information Dashboard Design: Displaying Data for at-a-Glance Monitoring, Analytics Press, Burlingame, CA.

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.

Segaran, T. & Hammerbacher, J. (eds) 2009, Beautiful data: the stories behind elegant data solutions, O'Reilly Media, Sebastopol, CA.

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. 2004, Information visualization: perception for design, 2nd edn, Morgan Kaufmann, San Francisco.

Yau, N. 2012, Visualize this!, John Wiley & Sons, Indianapolis, Ind. (eBook)

Other resources

Online Resources

Lynda.com (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