APP DATA 1010 - Ethics and Data Management I
North Terrace Campus - Semester 1 - 2023
General Course Information
Course Code APP DATA 1010 Course Ethics and Data Management I Coordinating Unit School of Physical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 6 hours per week Available for Study Abroad and Exchange Y Incompatible APP DATA 7010 Course Description The course develops a framework to understand the ethical issues of collecting and managing data sets. We examine how data are ethically used in society, and the impact of unethical practice. The course covers aspects of data ownership, privacy, anonymity, validity and fairness. The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) are introduced as a management strategy for thinking about sharing data in a way that will enable maximum use and reuse.
Course Coordinator: Professor Graham Heinson
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course, students will be able to:
1 Aware of the ethical issues of collecting and managing data sets 2 Understand how data can be used in an ethical ways to make decisions 3 Aware of basic tenets of data management, including the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) 4 Investigate discipline-based data-analytic issues that have major ethical implications 5 Develop communication skills using blogs, web-pages, embedded videos and podcasts
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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesMade by Humans: The AI Condition, by Ellen Broad, Melbourne University Press
Recommended ResourcesWeapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O'Neill, 2016, Crown Publishing Group (NY)
Learning & Teaching Activities
Learning & Teaching ModesThis course consists of:
- Up to 1 hour lecture per week
- 4 hour workshop per week
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.A student enrolled in a 3 unit course, such as this, should expect to spend, on average 12 hours per week on the studies required. This includes both the formal contact time required to the course (e.g., lectures and practicals), as well as non-contact time (e.g., reading and revision)
Learning Activities SummaryWeek 1: What are data ethics?
Week 2: Data ownership
Week 3: Privacy
Week 4: Anonymity
Week 5: Validity
Week 6: Fairness
Week 7: Social consequences
Week 8: FAIR data management principles
Week 9-12: Individual project and interview (3 workshops)
Week 13: Presentations and interviews
Specific Course RequirementsAttendance at all workshops is compulsory
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 Task Task Type
Percentage of total
assessment for grading purposes
Hurdle (Yes/No) Learning Outcome
Approximate timing of
(week of teaching period
Workshop report 1
Formative & Summative 11% N 1,2,5 Week 3 Workshop report 2 (weeks 3-4) Formative & Summative 11% N 1,2,5 Week 5 Workshop report 3 (weeks 5-6) Formative & Summative 11% N 1,2,5 Week 7 Workshop report 4 (weeks 7-8) Formative & Summative 11% N
Week 9 Major project (weeks 9-12)
Formative & Summative 38% N 1,4,5 Week 13 Quizzes Formative & Summative 18% N 1,2,5 Weeks 2,3,4,5,6,7
Assessment DetailWorkshop Reports 44% (4 reports X 11%)
Reports 1 to 4 are undertaken individually and are designed to introduce students to concepts of data ethics. Students will undertake research and develop a short report in various formats.
Project 38% (30% on report, 8% on interview)
The project is a larger exercise involving research and critical analysis of a major discipline-based data-ethics issue. The students will produce a significant report and present their outcomes in an online interview.
Six online quizzes, with a mixture of mulitple choice and short answer questions.
SubmissionSubmission of Assigned Work
Instructions on submission of work will be available on MyUn
Extensions for Assessment Tasks
Extensions of deadlines for assessment tasks may be allowed for reasonable causes. Such situations would include compassionate and medical grounds of the severity that would justify the awarding of a supplementary examination. Evidence for the grounds must be provided when an extension is requested. Students are required to apply for an extension to the Course Co-ordinator before the assessment task is due. Extensions will not be provided on the grounds of poor prioritising of time.
The assessment extension application form can be obtained from: http://www.sciences.adelaide.edu.au/current/
Late submission of assessments
If an extension is not applied for, or not granted then a penalty for late submission will apply. A penalty of 10% of the value of the assignment for each calendar day that the assignment is late (i.e. weekends count as 2 days), up to a maximum of 50% of the available marks will be applied. This means that an assignment that is 5 days late or more without an approved extension can only receive a maximum of 50% of the marks available for that assignment.
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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