Postgraduate Course: Work-based placement (Data-Driven Innovation) (HEIN11070)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 60 |
ECTS Credits | 30 |
| Summary | Learners will undertake a supervised work-based placement, building on skills and knowledge previously acquired from the foundational 120 credits of academic study that forms the final stage of the MSc Data Science for Health and Social Care programme.
The work-based project is designed to develop students' academic skills and ability to use and apply a range of data science skills, theories, concepts and principles relating to data-driven innovation in health and/or social care in a real-world setting. Students will develop innovative responses to problems and issues and communicate data-related issues in the health and social care sector and undertake an independent piece of work, scholarly or creative, demonstrating the ability to work independently under supervision.
The work-based placement is agreed-upon between all participating stakeholders: student, UoE academic supervisor and work-based mentor. A learning contract will be designed prior to the placement to ensure all stakeholder needs are met.
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| Course description |
The work-based placement allows the student to put theory into practice within a ¿real-world¿ setting in an industry setting or public sector organisation. Students will develop their skills and knowledge, both specialist and transferrable, enhancing their employability. The work-based project will provide students with the opportunity to build their networks by connecting and working with professionals in industry or public sector. Crucially, it will develop commercial, professional or situational awareness, developing knowledge of the current local and global business landscapes, industries, organisations and specific roles. Working under the guidance of an academic supervisor (based at UoE) and work mentor (based at placement setting) students will be offered the opportunity to work on an industry/public sector-based project to solve practical problems that require an application-oriented thinking.
Students will gain transferable skills through applied, practical experience with host organisations. Host organisations may be based in Scotland, rest of the UK or internationally and include industry, public sector, charities, third sector, think tanks, charities, NGOs and community-based groups. All work-based projects will be offered as virtual placements with no requirement to visit the UK.
A list of pre-arranged work-based projects will be presented to students in Year 2. Alternatively, students may choose to arrange their own placement. Input from the Programme Director will be necessary to confirm that the scope of the proposed project is suitable for a work-based dissertation.
The work based project takes nine months part-time to complete. The work base placement course supports students with a dedicated virtual learning environment, including online tools such as informal and formal asynchronous discussion boards and an e-portfolio. Supplementary (optional) online tutorials and expert guest lectures will also feature utilising synchronous web applications whenever possible. Students will be allocated a UoE supervisor with relevant expertise and a work based mentor. Supervisors will hold a minimum of 10 timetabled meetings with the student and will be available for consultation throughout the placement. A record of supervisory meetings will also be logged. Supervisor support can take on many forms and may be provided face-to-face, or remotely, via e-mail, or telephone, zoom/teams. Supervisory support will be supplemented by the detailed work based placement handbook. Regular community learning sessions will provide students with the opportunity to reflect on the experience of their peers and identify opportunities for learning while building a strong sense of academic community within and outwith the programme.
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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
600
(
Seminar/Tutorial Hours 10,
Dissertation/Project Supervision Hours 10,
Programme Level Learning and Teaching Hours 12,
Directed Learning and Independent Learning Hours
568 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Reflective Blog
Presentation
Dissertation |
| Feedback |
Feedback is information provided to the students about their learning relative to learning outcomes. Feedback is also important to identify areas for improvement; for example, course feedback surveys will be an integral component of course development. The two main types of feedback are formative and summative. Formative feedback involves feedback given during an assessment, while summative feedback is provided after an assessment has been completed. Feedback focuses on the student's current performance. On the other hand, feedforward offers constructive guidance on how to do better in the future.
We will use a combination of feedback and feedforward to ensure that students achieve the five learning outcomes from this course. A balance of formative feedback and feedforward will be provided throughout the course, for example, during live question and answer sessions and on discussion boards, supervisor meetings and work-based environment. Formative tasks will be offered before the student submit their summative assessed coursework. All components of summative assessment will be marked, and feedback will be provided.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of theories, concepts and principles relating to data-driven innovation in health and/or social care.
- Apply a range of data science skills, theories, practices, and creativity to produce a significant research, investigation, or development project.
- Critically review, consolidate, and extend knowledge, skills, practices, and thinking in data science to extract value from health and social care data.
- Develop innovative responses to problems and issues and communicate data-related issues in the health and social care sector.
- Exercise autonomy and reflexivity to contribute to change, development and/or new thinking within the health and social care sector.
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets: Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in data science in health and social care. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to data-related issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth
and breadth of their experiences to expand their potential and identify areas they wish to develop and grow. Students will also be encouraged to understand their responsibility within and contribute positively, ethically and respectfully to the health and social care community while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context, to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
2) Skills: Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate the literature and their learning. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and supervisors from an open-minded and reasoned perspective.
Personal effectiveness
Students will be effective and proactive learners that can articulate what they have learned and have an awareness of their strengths and limitations and a commitment to learning and reflection to complete this course successfully.
Commercial / Professional / Situational awareness
Students will display commercial/situational acumen and knowledge of the current local and global business landscapes, industries, organisations and specific roles. They will have the ability to work collaboratively with colleagues both internally and externally building and maintaining relationships. Students will aquire basic understanding of the key drivers for success in the current landscape and situations and understand the importance of innovation and taking calculated risks
Enterprise and Entrepreneurship
Students will broadly, have an ability to demonstrate an innovative approach, creativity, collaboration and risk taking. The work placements will require inventive thinking¿adaptability, managing complexity and self-direction and be commercially/professionally/situationally aware, creative and entrepreneurial
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the dissertation courses will reinforce and develop these skills.
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| Special Arrangements |
This course will be supported online using the Learn virtual learning environment. Work base learning placements will be organised by the programme team or students can suggest their own work placement. |
| Keywords | Work-based learning,experiential learning,reflection,industry,data-driven innovation |
Contacts
| Course organiser | Dr Sophie Marion De Proce
Tel:
Email: sophie.mariondeproce@ed.ac.uk |
Course secretary | Mrs Laura Miller
Tel: (0131 6)51 5575
Email: Laura.Miller@ed.ac.uk |
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