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Programme Information & PLOs
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This document forms part of the Programme Design Document and is for use in the roll-out of the York Pedagogy to design and capture new programme statement of purpose (for applicants to the programme), programme learning outcomes, programme map and enhancement plan. Please provide information required on all three tabs of this document.
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Title of the new programme – including any year abroad/ in industry variants
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MSci & BSc Mathematical Bioscience
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Level of qualification
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Please select:Level 7
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Please indicate if the programme is offered with any year abroad / in industry variants Year in Industry
Please select Y/N		No
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Year Abroad
Please select Y/N		Yes
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Department(s):
Where more than one department is involved, indicate the lead department
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Lead Department Natural Sciences
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Other contributing Departments: Biology, Mathematics
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Programme leadership and programme team
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Please name the programme leader and any key members of staff responsible for designing, maintaining and overseeing the programme.
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Katherine Selby (Programme Leader), Katherine Selby (Chair, Board of Studies) Jamie Wood & George Constable (Pathway Leader), Eric Dykeman (Mathematics)
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Particular information that the UTC working group should be aware of when considering the programme documentation (e.g. challenges faced, status of the implementation of the pedagogy, need to incorporate PSRB or employer expectations)
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With few exceptions the modules which make up any of the Natural Sciences programmes are drawn from the corresponding contributing single subject degree programmes. Local pedagogical practices and modes of assessment are honoured in Natural Sciences unless there is evidence that such practices would not be pedagogically sound. Therefore, given the nature of the Natural Sciences programmes parts of this document draw liberally from, or make reference to, the corresponding documentation from the contributing departments. This documentation should therefore be considered in parallel with the corresponding proforma for the single subject degree programmes of the contributing departments.

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Who has been involved in producing the programme map and enhancement plan? (please include confirmation of the extent to which colleagues from the programme team /BoS have been involved; whether student views have yet been incorporated, and also any external input, such as employer liaison board)
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The people listed in 14 item have primarily been responsible for the programme map and enhancement plan. At all stages the BoS has had free access to and been invited to comment on the documentation. Student input has been fed into the YP process in a focus group, through the Staff/ Student Liaison committee and via the Board of Studies.
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Purpose and learning outcomes of the programme
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Statement of purpose for applicants to the programme
Please express succinctly the overall aims of the programme as an applicant facing statement for a prospectus or website. This should clarify to a prospective student why they should choose this programme, what it will provide to them and what benefits they will gain from completing it.
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Mathematical Bioscience is a field where the cross application of knowledge and skills between the disciplines of mathematics and biology is key. As research aimed at understanding interrelated ecosystems and ecological systems has become more important to society, so using modelling to predict outcomes, clarify questions, and allow experiments to be undertaken has also become more important. As a Mathematical Bioscience student, you will have the chance to learn about the scope and possibilities of Mathematics when applied to biological and ecological problems; to use mathematical techniques to understand the dynamics of the natural world, with an emphasis on ecology; and to understand the appropriate mathematical tools, techniques and methodologies for solving real ecologically motivated problems.

The York Mathematical Bioscience programme has been constructed using modules from Biology and Mathematics by experts who are actively researching the very topics that you will study. Therefore a successful Mathematical Bioscience graduate will be prepared for a career using mathematical modelling to interpret biological and ecological scenarios and will be a cross discipline scientist with a skill set that goes beyond the boundaries of their programme.

As a student on the MSci programme you will achieve all the above, but your skills will be developed even further and to a deeper level as you undertake an extended final year research project that will move you towards the research frontier in Mathematical Bioscience, giving you the expertise, skills and experience necessary to pursue graduate level research both within and outside academia.
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Programme Learning Outcomes
Please provide six to eight statements of what a graduate of the programme can be expected to do.
Taken together, these outcomes should capture the distinctive features of the programme. They should also be outcomes for which progressive achievement through the course of the programme can be articulated, and which will therefore be reflected in the design of the whole programme.
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PLOOn successful completion of the programme, graduates will be able to:
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1 BScConfidently identify mathematical problems that can be analysed or resolved by standard mathematical techniques, and be able to apply those techniques successfully
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1 MSciConfidently identify complex mathematical problems that can be analysed or resolved by standard mathematical techniques, and be able to apply those techniques successfully with a high level of sophistication
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2 BScIdentify and apply relevant mathematical, numerical or statistical tools, techniques and methodologies to solve real world mathematical modelling problems in a biological, ecological or environmental sciences context
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2 MSciIdentify and apply relevant and contemporary mathematical, numerical or statistical tools, techniques and methodologies to solve real world mathematical modelling problems in a specialised biological, ecological or environmental sciences context
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3 BScDemonstrate breadth and depth of understanding of the fundamentals of genetics, ecology, evolution and the theoretical basis for ecological science, including a critical understanding of the relevant scientific literature.
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3 MSciDemonstrate breadth and depth of understanding of the fundamentals of genetics, ecology, evolution and the theoretical basis for ecological science, including critical appraisal of research at the forefront of the discipline.
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4 BScIdentify and critically evaluate analytical and quantitative techniques and methods through knowledge and first-hand practical experience in laboratories and the field, including the creation of comprehensive laboratory and field reports
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4 MSciIdentify and critically evaluate state-of-the-art experimental, analytical and quantitative techniques and methods at the forefront of the discipline through knowledge and first-hand practical experience in laboratories and the field, including the creation of comprehensive laboratory and field reports of a professional standard
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5 BScWork individually and in groups to solve modelling problems rooted in the biological and environmental sciences by applying logical reasoning, lateral thinking, and mathematical and numerical methodology to develop and implement safe, ethical and socially responsible solutions that benefit humankind
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5 MSciWork individually, in teams and in collaborative groups as a leader or member, to solve complex/unpredictable modelling problems rooted in the biological and environmental sciences by applying logical reasoning, lateral thinking, and mathematical and numerical methodology to develop and implement safe, ethical and socially responsible solutions that benefit humankind
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6 BScCommunicate, in a variety of media, the importance of mathematical, biological or ecological issues to an inter-disciplinary and specialist audience with arguments that are backed up by rigorous data analysis and robust mathematical modelling techniques.
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6 MSciCommunicate, with clarity and precision and in a variety of media, the importance of mathematical, biological or ecological issues to an interdisciplinary, specialist or non specialist audience with arguments that are backed up by rigorous data analysis and robust mathematical modelling techniques.
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7 BSc
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7 MSci
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8 BSc
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8 MSci
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Programme Learning Outcome for year in industry (where applicable)
For programmes which lead to the title ‘with a Year in Industry’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year in industry b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year in industry by alteration of the standard PLOs.
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NA
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Programme Learning Outcome for year abroad programmes (where applicable)
For programmes which lead to the title ‘with a Year Abroad’ – typically involving an additional year – please provide either a) amended versions of some (at least one, but not necessarily all) of the standard PLOs listed above, showing how these are changed and enhanced by the additional year abroad or b) an additional PLO, if and only if it is not possible to capture a key ability developed by the year abroad by alteration of the standard PLOs.
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PLO 7 Be inspired by and articulate the advantages of successfully studying in a non-UK academic environment through broadening your perspectives and developing adaptability, flexibility, resilience and drive.
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Explanation of the choice of Programme Learning Outcomes
Please explain your rationale for choosing these PLOs in a statement that can be used for students (such as in a student handbook). Please include brief reference to:
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i) Why the PLOs are considered ambitious or stretching?
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To fully meet the PLOs given a student will need to embrace both the biological and mathematical aspects of their course, link them together, develop sound experimental techniques and a detailed toolbox for dealing with the outcomes of these experiments, communicate their findings and by the end of degree be able to work at the frontier of mathematical biology.
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ii) The ways in which these outcomes are distinctive or particularly advantageous to the student:
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The juxtaposition of biology and mathematics at the undergraduate level is not a common one and the PLOs ensure that a student who engages with their programme will have acquired a deep insight into both subjects and the links that exist between them. To do so the student will necessarily have to move between the two subjects seamlessly and this will naturally promote lateral thinking. This is certainly distinctive when compared to a single subject student in either discipline where exposure to the other areas is often left to examples or the odd module. The stated PLOs are designed so that this cross disciplinary thinking is woven into the fabric of the programme and that the links are identified from the outset and serve as motivation for and throughout the programme.
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iii) How the programme learning outcomes develop students’ digital literacy and will make appropriate use of technology-enhanced learning (such as lecture recordings, online resources, simulations, online assessment, ‘flipped classrooms’ etc)?
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Students on Mathematical Bioscience will experience a programme that has technology at its core being a synthesis of biology and mathematics. The practice of mathematical modelling will require a student to use various computer packages to collect, process and analyse data. This data will then be disseminated through various digital media using the appropriate software for the task. Students will further benefit from immersion in two different teaching centres and will have exposure to the technology used in those centres to, amongst other things, deliver lectures and lecture material, setup, circulate and feedback on assessments, encourage students to use digital resources to research topics at the module level and beyond when students engage in the final year research project and communicate with their peers and academics using VLE fora. (PLO 2, 4 & 6 are explicit examples).
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iv) How the PLOs support and enhance the students’ employability (for example, opportunities for students to apply their learning in a real world setting)?
The programme's employability objectives should be informed by the University's Employability Strategy:
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http://www.york.ac.uk/about/departments/support-and-admin/careers/staff/
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All the Natural Science programmes have been designed with employability in mind. This is not only as a factor of the design of the programmes themselves, which have had engagement with the University's employability strategy as a given since the early design phases of the programme. But also as a factor of the embedded skills that the contributing departments have built into their modules. Modules which form the bulk of the teaching on this degree programme. Many of the skills listed in the PLOs are generic and will equip the student with a highly transferable skill set. As an example: PLOs 5 & 6 revolve around such transferable skills as programming, communication skills and data analysis techniques which are applicable beyond the problems addressed in the programme.
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vi) How will students who need additional support for academic and transferable skills be identified and supported by the Department?
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Students who need support will generally self identify at admission or early in the Stage 1 and standard University protocols will then be followed. If this isn't the case and a student is identified as needing extra support later in the programme then the student will discuss the matter with their personal supervisor who will advise in accordance with University guidance. Students are assigned a supervisor in one of the contributing departments and have access to a subject facilitator in both contributing departments. The student can approach their supervisor for advice in accordance with University guidelines and seek more specialist advice on a particular discipline from the subject facilitator. Module level issues are handled with the department to which the module belongs and a student can avail themselves off all feedback and quality control mechanisms that the department offers.
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vii) How is teaching informed and led by research in the department/ centre/ University?
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The very existence of this programme, like all the Nat. Sci. interdisciplinary programmes, is due to the fact that research undertaken in the Departments of Biology and Mathematics has demonstrated a need for graduates who have the skills and techniques to tackle problems of current global significance. The whole programme has being designed by experts actively engaging in research on these very problems, to equip graduates with the necessary skill set to tackle these critical real world problems. The journey that the student will undertake in developing and using these skills culminates in the final year project which will push students to the frontiers of research in the area of Mathematical Bioscience. Therefore research not only informs the teaching on this program, it is the reason that the programme is offered as part of the York Natural Science portfolio.
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Stage-level progression
Please complete the table below, to summarise students’ progressive development towards the achievement of PLOs, in terms of the characteristics that you expect students to demonstrate at the end of each year. This summary may be particularly helpful to students and the programme team where there is a high proportion of option modules.

Note: it is not expected that a position statement is written for each PLO, but this can be done if preferred (please add information in the 'individual statement' boxes). For a statement that applies across all PLOs in the stage fill in the 'Global statement' box.
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Stage 0 (if your programme has a Foundation year, use the toggles to the left to show the hidden rows)
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Stage 1
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On progression from the first year (Stage 1), students will be able to:
Appreciate the interdisciplinary nature of Mathematical Bioscience through exposure to the mathematical and biological concepts which make up the program and have developed the core learning strategies needed to work across different departments, have a solid grounding in the foundations of Mathematical Bioscience, have the core experimental skills necessary to progress further and have begun building a skill set that will allow a student to solve problems using appropriate tools and know how to effectively communicate their findings.
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PLO 1PLO 2PLO 3PLO 4PLO 5PLO 6PLO 7PLO 8
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Individual statements
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Stage 2
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On progression from the second year (Stage 2), students will be able to:Developed further their understanding of mathematics and biology, expanded upon their knowledge base, have enhanced experimental and communication skill sets allowing them to solve increasingly difficult and challenging problems in Mathematical Bioscience, have become more confident independent learners.
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PLO 1PLO 2PLO 3PLO 4PLO 5PLO 6PLO 7PLO 8
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Individual statements
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Stage 3
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(For Integrated Masters) On progression from the third year (Stage 3), students will be able to:At this stage a Mathematical Bioscience student will have the knowledge, skills and understanding to satisfy all the BSc PLOs and will be equipped to move forward into a more intensely research driven final year.
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PLO 1PLO 2PLO 3PLO 4PLO 5PLO 6PLO 7PLO 8
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Individual statements
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Programme Structure
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Module Structure and Summative Assessment Map
Please complete the summary table below which shows the module structure and the pattern of summative assessment through the programme.

‘Option module’ can be used in place of a specific named option. If the programme requires students to select option modules from specific lists these lists should be provided in the next section.

From the drop-down select 'S' to indicate the start of the module, 'A' to indicate the timing of each distinct summative assessment point (eg. essay submission/ exam), and 'E' to indicate the end of the module (if the end of the module coincides with the summative assessment select 'EA') . It is not expected that each summative task will be listed where an overall module might be assessed cumulatively (for example weekly problem sheets).

If summative assessment by exams will be scheduled in the summer Common Assessment period (weeks 5-7) a single ‘A’ can be used within the shaded cells as it is understood that you will not know in which week of the CAP the examination will take place.
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Stage 0 (if you have modules for Stage 0, use the toggles to the left to show the hidden rows)
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Stage 1
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CreditsModuleAutumn TermSpring Term Summer Term
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CodeTitle123456789101234567891012345678910
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10BIO00007CGeneticsSEA
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10BIO00009CGenetics and EvolutionSEAAA
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10BIO00010CMicrobiologySEA
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20MAT00003CIntroduction to Applied MathematicsSEAAA
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20MAT00007CMathematics for the Sciences 1SEA
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20MAT00008CMathematics for the Sciences 2SEAAA
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30BIO00012CAnimal and Plant BiologySAEAAA
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Stage 2
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CreditsModuleAutumn TermSpring Term Summer Term
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CodeTitle123456789101234567891012345678910
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20BIO00053IEcology of Animals, Plants and MicrobesSEA
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20BIO00056IGenes, Genomes, Evolution and PopulationsSEA
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10BIO00047IBig Data BiologySEA
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10BIO00057ITutorialsSAEA
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10MAT00027IMathematical Skills IISAEA
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30MAT00033IStatistics OptionSAEAAA
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10MAT00041ILinear Algebra for Natural SciencesSEAAAA
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10MAT00030IVector CalculusSEA