GV2MU-Managing Uncertainty

Module Provider: Geography and Environmental Science
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Liz Stephens
Email: elisabeth.stephens@reading.ac.uk

Type of module:

Summary module description:

This module will equip students with the skills to interpret, critique and discuss quantitative geographical approaches and data, as well as developing an understanding of the different sources of uncertainty and the implications for communicating findings and decision-making. The module will use seminars and computer-based lab practicals to enable students to engage with research datasets that reflect contemporary geographical challenges such as forecasting of natural hazards, understanding climate change, and predicting socio-demographic changes.

Aims:

The aim of this module is to develop a student’s ability to interpret, critique and discuss quantitative geographical approaches and data. Students will be taught numerical analysis skills in ‘R’ through which they can visualise, interpret and interrogate geographical datasets related to predicting natural hazards, understanding climate change and understanding socio-demographic data.Ìý

Assessable learning outcomes:

By the end of this module, students should be able to:Ìý

• Recognise and describe different sources of uncertainty


• Visualise data in a number of different ways in ‘R’


• Calculate summary statistics in ‘R’


• Critically appraise quantitative geographical research

Additional outcomes:

This module will provide the opportunity to develop the following transferable skills:

• Teamwork


• Data handling


• Data presentation


• Written presentation


• Critical thinkingÌý

Outline content:

Introductory lectures will lead into computer-lab based practicals through which students will engage with issues such as identifying trends in data, addressing uncertainties in sample size and measurement uncertainty, extrapolation and correlation versus causation.

Brief description of teaching and learning methods:

Introductory lectures for each topic, followed by hr guided computer-lab practicals to address a topic / key issue each week. A one-hour introduction to the project will be provided as well as lectures on writing an executive summary / communicating to stakeholders.ÌýÌý

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures	5
Project Supervision	4
Practicals classes and workshops	12
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (directed)	6
Ìý Ìý Preparation of practical report	12
Ìý Ìý Carry-out research project	55
Ìý Ìý Reflection	6
Ìý	Ìý	Ìý	Ìý
Total hours by term	100	0	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Report	40
Project output other than dissertation	60

Summative assessment- Examinations:

N/A

Summative assessment- Coursework and in-class tests:

Assessment is designed to enable students to develop skills in data analysis, and academic and professional-style delivery of the results to end users. The write-ups of the week’s practicals will consist of a final figure, figure caption, and approximately 250 words of text. These will be assessed weekly. Students will only be able to produce these if they have successfully undertaken earlier tasks (e.g. downloading data, opening data, converting formats, calculating statistics) so though this a short write-up it will reflect a significant body of work.

The weekly write-ups carry a higher rating than the final report for the final module mark because it is vital that students engage with the topic every week. The students can achieve a reasonable pass just through undertaking the practical, but students who wish to go further outside of the practical have scope to achieve much higher marks.

For the final project the students will be given a dataset and will be required to replicate the skills they have learnt using these new data. They will also be required to write a clear executive summary of the findings (1000 words + multiple figures) for a designated end-user. Assistance will be provided through two drop-in sessions.

Six practical write-ups – 250 words each plus illustrations

Report – 1000 words.

Formative assessment methods:

Peer assessment and feedback on practical write-ups.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

Resubmission of written report.

Additional Costs (specified where applicable):

1. Required text books: None


2. Specialist equipment or materials:ÌýNone


3. Specialist clothing, footwear or headgear:ÌýNone


4. Printing and binding:ÌýNone


5. Computers and devices with a particular specification:ÌýNone


6. Travel, accommodation and subsistence:ÌýNone

Last updated: 30 March 2023