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2020/21 Taught Postgraduate Module Catalogue

MECH5565M Medical Engineering Experimental Design and Analysis

15 creditsClass Size: 40

Module manager: Dr Louise Jennings
Email: L.Jennings@leeds.ac.uk

Taught: 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan) View Timetable

Year running 2020/21

Pre-requisite qualifications

Either an undergraduate degree in a physical, biological science or one of the cognate disciplines (min 2.2) or a medical degree or allied subject with a background in orthopaedics.

Module replaces

MECH 5550M Research Methods

This module is not approved as an Elective

Objectives

This module aims to equip students with the core skills and awareness required to undertake research in the area of medical engineering.

Learning outcomes
On completion of this module students will:

1. Recognise key issues when designing experiments and collecting data, concerning objectivity, reliability and repeatability;
2. Understand how to plan an experiment to answer a specific question;
3. Develop an understanding of research data analysis and statistical analysis;
4. Show awareness of data visualisation techniques and demonstrate the ability to present data in a meaningful and succinct manner;
5. Demonstrate an understanding of key imaging and image analysis methods.
6. Demonstrate an understanding of computer modelling methods in the context of medical engineering and tissue engineering research;
7. Demonstrate an understanding of key biological methodologies relevant to medical engineering and tissue engineering research;
8. Demonstrate an understanding of mechanical testing methodologies relevant to medical engineering and tissue engineering research.


Upon successful completion of this module the following UK-SPEC learning outcome descriptors are satisfied:

A comprehensive understanding of the relevant scientific principles of the specialisation (SM1m, SM7M)
Knowledge and understanding of mathematical and statistical methods necessary to underpin education in medical engineering and to enable them to apply a range of mathematical and statistical methods, tools and notations proficiently and critically in the analysis and solution of engineering problems (SM2m)
Ability to apply and integrate knowledge and understanding of other engineering disciplines to support study of study of medical engineering and the ability to evaluate them critically and to apply them effectively (SM3m)
Awareness of developing technologies related to medical engineering (SM4m)
A comprehensive knowledge and understanding of mathematical and computational models relevant to the medical engineering discipline, and an appreciation of their limitations (SM5m)
Understanding of concepts relevant to the discipline, some from outside engineering, and the ability to evaluate them critically and to apply them effectively, including in engineering projects (SM6m, SM9M)
Understanding of engineering principles and the ability to apply them to undertake critical analysis of key engineering processes (EA1m)
Ability to identify, classify and describe the performance of systems and components through the use of analytical methods and modelling techniques (EA2)
Ability both to apply appropriate engineering analysis methods for solving complex problems in engineering and to assess their limitations (EA3m, EA6M)
Ability to use fundamental knowledge to investigate new and emerging technologies in medical engineering (EA5m)
Ability to extract and evaluate pertinent data and to apply engineering analysis techniques in the solution of unfamiliar problems in medical engineering (EA6m, EA6M)
Understanding of appropriate codes of practice and industry standards (P6)
Ability to work with technical uncertainty (P8)
Apply their skills in problem solving, communication, information retrieval, working with others, and the effective use of general IT facilities (G1)
Ability to collect and analyse research data and to use appropriate engineering analysis tools in tackling unfamiliar problems, such as those with uncertain or incomplete data or specifications, by the appropriate innovation, use or adaptation of engineering analytical methods (EA7M)


Syllabus

This module will acquaint students with basic generic skills required for experimental and computational research in terms of test design.

The module will cover:
- Experimental design;
- Statistics and measurement
- Data interpretation and visualisation
- Ethics
- Mechanical laboratory techniques(overview)
- Theoretical / computational techniques (overview)
- Imaging techniques (overview)
- Biological techniques (overview)

Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Delivery typeNumberLength hoursStudent hours
Lecture111.0011.00
Seminar112.0022.00
Private study hours117.00
Total Contact hours33.00
Total hours (100hr per 10 credits)150.00

Private study

Private study includes gathering information on applications and details of the experimental and computational techniques which are covered, as well as the specific information relevant to the experiment designed for the coursework. Approximately 50 hours for background reading on taught material and 67 hours for assignment preparation.

Opportunities for Formative Feedback

An online discussion board will be monitored during specified times each week.
A weekly ‘office hour’ in which students can seek 1-2-1 support.

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information


Coursework
Assessment typeNotes% of formal assessment
ReportProject Report - Executive Summary20.00
PortfolioExperimental Design30.00
Total percentage (Assessment Coursework)50.00

Coursework marks carried forward and 50% resit exam OR 100% exam


Exams
Exam typeExam duration% of formal assessment
Online Time-Limited assessment48 hr 50.00
Total percentage (Assessment Exams)50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 10/08/2020 08:42:19

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