MDIA 3307 - Surveillance and Big Data
North Terrace Campus - Semester 1 - 2018
General Course Information
Course Code MDIA 3307 Course Surveillance and Big Data Coordinating Unit Media Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact 3 hours per week Available for Study Abroad and Exchange N Prerequisites MDIA 1002, MDIA 2301 or MDIA 2306 Course Description This course will explore the implications of the new intensive data gathering arising from the ubiquitous media technologies that surround us. It will cover theories of surveillance and then look at the practices of data gathering and consider the impact on areas such as freedom of expression, privacy, and identity. The course will consider the governance of surveillance and then compare current policy regimes towards big data use in Australia, the US, the UK and the EU. The prevalence of predictive algorithms and the uses to which they are being put will be interrogated, and the possibilities for future policy directions explored, with an emphasis on the areas of transparency and accountability.
Course Coordinator: Dr Sal Humphreys
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
- Understand the role of digital media in shaping new practices around surveillance and privacy.
- Understand theories about how surveillance works in society.
- Understand the role of law in creating mechanisms of transparency and accountability .
- Understand the role of policy in creating mechanisms of transparency and accountability.
- Understand different regulation schemes in place across the globe.
- Understand the ways everyday practices are translated into data used by commercial and government organisations.
- Understand how the performance of identity is shaped through practices of surveillance.
- Understand how the practices of everyday media use are articulated with larger policy and law regimes.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4,5,6,7,8 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,3,4,5,6,7,8 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1,2,3,4,5,6,7,8 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3,4,5,6,7,8 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,2,3,4,5,6,7,8 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesThere will be no set text books, but a reader will be created and students will need to access online videos outside of class times.
Online LearningThe course will operate a number of exercises through MyUni.
Learning & Teaching Activities
Learning & Teaching ModesLectures supported by problem solving tutorials with focus on small group discovery learning experiences.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.1 x 3 hour workshop per week
6 hours reading per week
2 hours research per week
2 hours assignment preparation per week
Learning Activities SummaryWeekly content will be announced at the start of the semester. It will cover selected areas from the list below.
The Data-based Self: social media and identity, the 'Quantified Self'
Theories of surveillance 1: Foucault
Theories of surveillance 2: Deleuze
Big Data: predictive algorithms. Case study on predictive policing
Big Data: data retention and analysis – policies and practices
Privacy and privacy law
Surveillance and Big Data in Popular Culture and Media texts
Corporations, consumers and data: impacts of targeting
Surveillance and governance: NSA, 5 eyes, and Edward Snowden
Surveillance and governance: Drones
Looking forward: responses and solutions
Small Group Discovery ExperienceSmall Group Discovery Experience is embedded throughout this course. All seminars will be run with periods of time spent in small groups, where students will work on problems and exercises together under the supervision and in discussion with the Course Coordinator.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment SummaryWeekly Online Multiple Choice tests 20%
Online wiki (1500 words) 20%
Tutorial attendance and participation 10%
3000 word essay 40%
Assessment Related RequirementsAttendance in workshops is compulsory.
Multiple Choice Tests: There will be 10 weekly tests based on course readings, each worth 2% and cumulatively 20% weighting.
1500 word wiki entry: an online entry made with relevance to the subject area, which may include embedded multi-media material and links- 20% weighting.
Workshop participation: students engage in interaction in class activities and the cooperative sharing of materials and information - 10% weighting.
3000 word essay: students will be required to write a 3000 word research essay on a question relating to surveillance and data which will be theoretically framed and draw on research in the fields of media, policy and law. - 40% weighting.
No information currently available.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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