SCIENTIFIC INVESTIGATION SKILLS - 2019/0

Module code: PHY1035

Module Overview

This module covers a wide range of generic skills important in scientific investigation. These skills cover data handling, statistical analysis, laboratory skills, scientific writing, ethics (including academic misconduct), group working covering problem-solving, an oral presentation and public communication, plus library-based information research skills including information retrieval and referencing.

Module provider

Physics

Module Leader

FAUX DA Dr (Physics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 4

JACs code: X210

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

Pre-university level education to Advanced Level standard.  

Module content

Indicative content includes:


Laboratory
The laboratory experiments include: Ball bouncing; Focal length of a lens; Spring; Ionizing radiation; Load matching; Mass of Jupiter.
Research Skills
Probability: Discrete and continuous distributions, expectation values, Binomial, Gaussian and Poisson distributions.  The Central Limit Theorem.
Statistics: Mean, standard deviation, standard error in mean.
Data Analysis: Propagation of errors, least-squares fitting; c2-distribution
Spreadsheets: Excel spreadsheets including calculations, numerical simulation and graphs
Computer Algebra: Introduction to some features of MathCAD
Ethics: ethical scientific conduct, issues of plagiarism and proper referencing in science
Using the University Library, including the different types of resource available, how to search the library catalogue, understanding different types of citations, appropriate referencing, and searching for authoritative information on the web.
Communicating scientific work appropriately for the relevant audience through writing, press statements, oral presentation to peers and through a broadcast suitable for the general public.
Workshop on team working, students contribute to a team oral presentation, and work together in problem-solving activities.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework DATA HANDLING 20
Practical based assessment LABORATORY ASSESSMENT 20
Coursework ESSAY 20
Coursework TEAM WORK 40

Alternative Assessment

The laboratory diaries and class test UoA may be assessed through one laboratory diary, one lab report and a class test. The Essay UoA may be assessed by essay only. The Group work UoA may be assessed by solo problem-solving activity (Problem 1) and the broadcast activity.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate


laboratory practical skills
ability to perform statistical analysis of data
professional scientific communication skills


Thus, the summative assessment for this module consists of:

·         Online Data Handling test

·         A short (500-800 word) essay  (week 8) and peer review exercise (week 9)

·         Practical Laboratory test (end of semester). The Laboratory unit of assessment has a qualifying mark of 40%.

·         Team work activities consisting of a presentation (week 11), broadcast (week 11) and problem-solving (weeks 5,7 & 10)

 

Formative assessment and feedback

The first problem-solving exercise is formative.  It is marked and returned with feedback. All problem-solving exercises are supported by a 2-hour computer-based class during which students obtain feedback on progress.  For all problems, students see their marked reports with comments.  Students receive written feedback on their essay in the form of three peer-review forms. Students receive written feedback on their group presentations via assessment forms. In the laboratory, students receive oral feedback weekly from academic and/or postgraduate demonstrators. The data handling activity includes academic and/or postgraduate demonstrator feedback and advice for the computer-based sessions.

 

Module aims

  • teach the basic elements of probability distributions and to be able to undertake simple statistical and error analysis.  To be able to use a computer spreadsheet to do such analysis, plot graphs and perform curve fitting.
  • establish a foundation of practical skills when conducting experiments to verify theory and to improve understanding
  • develop skills in analysing data. 
  • explore issues of ethics in science and academic misconduct
  • provide an introduction to finding suitable information from different sources available through the library, and referencing the sources appropriately. 
  • to develop writing skills and referencing of scientific work through writing a short essay
  • develop skills in critique and be able to participate in peer review
  • to develop confidence and skills in oral presentation
  • to undertake a problem-solving activity as part of a team to produce collaborative reports.
  • to develop skills in scientific modelling using a spreadsheet
  • to present scientific information appropriately including use of diagrams, figures and graphs and presentation of equations and numbers
  • to present scientific information in a form suitable for the general public

Learning outcomes

Attributes Developed
001 Analyse and present reduced experimental and probabilistic results of the multiple measurements of physical observables.  C
002 Quote averages and errors of such variables. K
003 Fit theoretical predictions to graphs where one independent observable is changing using the method of least squares, and find the errors in the fitting parameter(s).  C
004 Use simple error theory to find the errors of quantities dependent on (combinations of) the observables. C
005 Use simple probability distributions to predict the outcome of experiments.  C
006 Apply basic practical skills for laboratory work, including use of different measuring equipment CPT
007 Demonstrate teamwork skills with a lab partner; T
008 Perform measurements and keep clear and accurate records of the results. C
009 Be aware of and understand how to access library resources available in the University Library and online. T
010 Understand different types of citations, including those for books and journals. P
011 Use the web for authoritative information T
012 Be able to find information from different sources available in the University Library. PT
013 Be able to write bibliographies in a variety of formats, and reference appropriate sources. PT
014 Understand the structure used in different types of scientific writings. T
015 Understand the principle of peer review, and be able to critically review work. P
016 Work collaboratively as a team member to solve problems and formulate joint reports PT
017 Present science in a manner suitable for consumption by the general public KPT
018 Undertake individual research on a science topic and present in essay format KCPT
019 Produce a simple numerical simulation using EXCEL PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Workshop Hours: 14

Independent Study Hours: 92

Lecture Hours: 18

Laboratory Hours: 26

Methods of Teaching / Learning

The learning and teaching strategy is designed to:


equip students with practical and professional skills
provide students with subject knowledge and the ability to apply it to practical situations


The learning and teaching methods include:


Four hours per week for the first 6 weeks in the laboratory
Probability, Statistics, Data Analysis, Spreadsheets and Computer Algebra: A one hour lecture followed by a one hour tutorial session in a computing laboratory weekly, for six weeks.
6 hours of material delivered by University Library staff in a mixture of workshop and lecture format. 
19 hours of team working spread over the semester with sessions on essay work, scientific ethics, problem-solving support and presentation skills.


 

 

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

Reading list for SCIENTIFIC INVESTIGATION SKILLS : http://aspire.surrey.ac.uk/modules/phy1035

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Physics with Nuclear Astrophysics MPhys 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics with Nuclear Astrophysics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics with Astronomy BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics MPhys 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics with Astronomy MPhys 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics with Quantum Technologies MPhys 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Physics with Quantum Technologies BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.