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MA1MC: Mathematical Communication
Module code: MA1MC
Module provider: Mathematics and Statistics; School of Mathematical, Physical and Computational Sciences
Credits: 20
Level: Level 1 (Certificate)
When you'll be taught: Semester 1
Module convenor: Dr Calvin Smith, email: Calvin.Smith@reading.ac.uk
Module co-convenor: Dr Julia Abery, email: j.abery@reading.ac.uk
Pre-requisite module(s): Before taking this module, you must have at least a grade B in A-Level Mathematics grade B, or equivalent. (Open)
Co-requisite module(s): IN THE SAME YEAR AS TAKING THIS MODULE YOU MUST TAKE MA1CA (Compulsory)
Pre-requisite or Co-requisite module(s):
Module(s) excluded:
Placement information: NA
Academic year: 2024/5
Available to visiting students: Yes
Talis reading list: Yes
Last updated: 21 May 2024
Overview
Module aims and purpose
Students will learn the importance of expressing mathematical concepts and results clearly, logically and concisely and how to implement basic problem-solving strategies. In particular, the module will develop students’ understanding that there is an expectation to work out steps in a proof, and the importance of constructing complete and unambiguous mathematical sentences, making the flow of an argument clear, and the clear introduction of variables and accurate use of mathematical symbols. Students will gain practice in structuring a report/presentation (including introduction, conclusion, signposting). Students will also develop their expertise in problem-solving techniques such as data visualisation and pattern exploration, and in use of appropriate computing languages.
Module learning outcomes
By the end of the module, it is expected that students will be able to:
- Demonstrate comprehension of mathematical arguments, and to construct simple but rigorous mathematical arguments and correctly express statements and proofs of simple mathematics
- Clearly communicate scientific content orally and in writing
- Implement a selection of mathematical problem-solving techniques, and to show competence in information technology related to problem-solving techniques
- Demonstrate familiarity with key concepts of Data Science and analytical skills applied to routine problems
Module content
This module will help students gain familiarity with analysing and constructing mathematical arguments, and the skills required to clearly communicate mathematical concepts and results.
Structure
Teaching and learning methods
Each week will include two lectures and two seminars/practical classes
Study hours
At least 44 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.
 Scheduled teaching and learning activities |  Semester 1 |  Semester 2 | Ìý³§³Ü³¾³¾±ð°ù |
---|---|---|---|
Lectures | 22 | ||
Seminars | 18 | ||
Tutorials | |||
Project Supervision | |||
Demonstrations | |||
Practical classes and workshops | 4 | ||
Supervised time in studio / workshop | |||
Scheduled revision sessions | |||
Feedback meetings with staff | |||
Fieldwork | |||
External visits | |||
Work-based learning | |||
 Self-scheduled teaching and learning activities |  Semester 1 |  Semester 2 | Ìý³§³Ü³¾³¾±ð°ù |
---|---|---|---|
Directed viewing of video materials/screencasts | |||
Participation in discussion boards/other discussions | |||
Feedback meetings with staff | |||
Other | |||
Other (details) | |||
 Placement and study abroad |  Semester 1 |  Semester 2 | Ìý³§³Ü³¾³¾±ð°ù |
---|---|---|---|
Placement | |||
Study abroad | |||
 Independent study hours |  Semester 1 |  Semester 2 | Ìý³§³Ü³¾³¾±ð°ù |
---|---|---|---|
Independent study hours | 156 |
Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.
Semester 1 The hours in this column may include hours during the Christmas holiday period.
Semester 2 The hours in this column may include hours during the Easter holiday period.
Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.
Assessment
Requirements for a pass
Students need to achieve an overall module mark of 40% to pass this module.
Summative assessment
Type of assessment | Detail of assessment | % contribution towards module mark | Size of assessment | Submission date | Additional information |
---|---|---|---|---|---|
Set exercise | Problem sheet | 33 | |||
Oral assessment | Presentation assessment | 33 | |||
Set exercise | Data science assignment | 34 |
Penalties for late submission of summative assessment
The Support Centres will apply the following penalties for work submitted late:
Assessments with numerical marks
- 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 three working days;
- the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
- where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
- where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
Assessments marked Pass/Fail
- where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
- where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.
The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/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.
Formative assessment
Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.
Reassessment
Type of reassessment | Detail of reassessment | % contribution towards module mark | Size of reassessment | Submission date | Additional information |
---|---|---|---|---|---|
Set exercise | Problem sheet | 33 | |||
Oral reassessment | Presentation assessment | 33 | |||
Set exercise | Data science assignment | 34 |
Additional costs
Item | Additional information | Cost |
---|---|---|
Computers and devices with a particular specification | ||
Required textbooks | ||
Specialist equipment or materials | ||
Specialist clothing, footwear, or headgear | ||
Printing and binding | ||
Travel, accommodation, and subsistence |
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.