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MA1MC: Mathematical Communication

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:

  1. Demonstrate comprehension of mathematical arguments, and to construct simple but rigorous mathematical arguments and correctly express statements and proofs of simple mathematics
  2. Clearly communicate scientific content orally and in writing
  3. Implement a selection of mathematical problem-solving techniques, and to show competence in information technology related to problem-solving techniques
  4. 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  Summer
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  Summer
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  Summer
Placement
Study abroad

Please note that the hours listed above are for guidance purposes only.

 Independent study hours  Semester 1  Semester 2  Summer
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: https://www.reading.ac.uk/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.

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