Postgraduate Course: Working with Big Data to Improve Children's Safety and Wellbeing (fusion online) (EFIE11340)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | In this course, students will learn the fundamental principles of big data, opportunities for big data in child protection, and the challenges associated with the ethics, capture, mining, storage, sharing and analysis of big data particularly for the safety and wellbeing of children and young people globally. The course will also cover big data ethical debates as they relate to children and young people. |
| Course description |
The course will be taught in a hybrid format with a two week pre-intensive part of the course leading up to a two-day intensive sessions followed by the two-week post intensive sessions.
In the pre-intensive part of the course (2 weeks) students will engage with experts in the field through pre-recorded guest lectures and case studies that present big data challenges and opportunities as they relate to children and young people's wellbeing and safety. Students will also engage with literature on the technical elements of big data capture, storage and analysis.
In the two-day intensive, student will see hands on examples of big data computational methods and will walk through a workflow of big data from challenge question to answer and all the elements required in between (including ethics). Students will also engage in team debates and discussions around big data issues during the intensive session.
During both the intensive and post-intensive sessions, students working in teams will work on a big data workflow assessment - worth (100%) - mapping the process of a big data study from challenge to solution.
Edinburgh Futures Institute (EFI) - Online Fusion Course Delivery Information:
The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.
As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: 5 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 2,
Supervised Practical/Workshop/Studio Hours 14,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
82 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Summative Assessment:
The course will be assessed by means of the following assessment components:
1) Completion of Big Data Study Workflow (100%)
Choosing from a list of big data study challenge questions (students can also develop their own), students will work in teams to develop a miro board workflow (utilising a template) to envision the step-by-step process of conducting a big data study from challenge question to solution/finish including ethical considerations for each step in the workflow. Students will present their workflows in a 10-minute recorded presentation and will have the opportunity to view other team's presentations. |
| Feedback |
Formative Feedback:
- Intensive session activities during course will receive feedback from peers and staff.
Summative Feedback:
- Workflow miroboard and presentation - facilitator and peer feedback. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an advanced understanding of the challenges and opportunities with big data to address wellbeing and safety issues among children and young people.
- Critically engage in ethical issues surrounding big data and children and young people.
- Demonstrate a comprehensive understanding of the workflow processes of a big data study from start (challenge question) to finish (solution).
- Understand elements of the technical approach to big data capture and analysis.
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Reading List
Indicative Reading List:
Essential Reading:
Berman, G., and Albright, K.(2017) Children and the Data Cycle: Rights and Ethics in a Big Data World. Florence: UNICEF https://www.unicef-irc.org/publications/907-children-and-the-data-cyclerights-and-ethics-in-a-big-data-world.html
Montgomery KC, Chester J, Milosevic T. Children's Privacy in the Big Data Era: Research Opportunities. Pediatrics. 2017 Nov;140(Suppl 2):S117-S121. doi: 10.1542/peds.2016-1758O. PMID: 29093045.
Munro, E., (2019) Predictive analytics in child protection. https://www.durham.ac.uk/media/durham-university/research-/research-centres/humanities-engaging-sci-and-soc-centre-for/K4U_WP_2019_03.pdf
Goldberg, J. and Pierson, L. Managing Big Data Workflows: BMC Software Edition.
Recommended Reading:
The Foster Care System Turns to Big Data: Promising or Profiling? https://imprintnews.org/child-welfare-2/the-foster-care-system-turns-to-big-data-promising-or-profiling/62359
Further Reading:
Algorithms of Oppression by Dr Safiya Umoja Noble |
Additional Information
| Graduate Attributes and Skills |
- Enquiry and lifelong learning.
- Outlook and engagement..
- Research and enquiry.
- Personal and intellectual autonomy.
- Personal effectiveness.
- Communication. |
| Keywords | Big Data,Children,Young People,Child Protection,Children's Wellbeing,EFI,Level 11,PG |
Contacts
| Course organiser | Ms Deborah Fry
Tel: (0131 6)51 4796
Email: Debi.Fry@ed.ac.uk |
Course secretary | Miss Yasmine Lewis
Tel:
Email: yasmine.lewis@ed.ac.uk |
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