Modelling and Optimisation under Uncertainty – Hire Academic Expert

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Faculty of Engineering, Environment and Computing
7135CEM – Modelling and Optimisation under
Uncertainty
Assignment Brief 2021/2022

Module Title
Modelling and
Optimisation under
Uncertainty
Individual or
Group Project
(2 people)
Cohort
JAN & MAY 22
Module Code
7135CEM
Coursework Title (e.g. CWK1)
CW
Hand out date:
22/07/2022
Lecturer: Dr Alireza Daneshkhah Due date and time:
Date:
19/08/2021
Online: 18:00
Estimated Time (hrs): 30hrs
Word Limit*: 12 pages A4, up to 6000
Coursework type:
Written Assignment
% of Module Mark
100%: each task of this
CW worth 50%.
Submission arrangement online via AULA/CUMoodle:
Submit before 1800, late work will receive a mark of zero.
File types and method of recording: Submit Two Word or pdf documents (or similar). One for
each of the Two Tasks (see below).
Mark and Feedback date: 04/09/2022
Mark and Feedback method: given on each script.

 

Module Learning Outcomes Assessed:
On completion of this module the student should be able to:
1. Apply supervised and unsupervised learning applications using Gaussian process
emulators.
2. Apply Dirichlet processes for unsupervised learning applications
3. Develop the knowledge and skills necessary to design, implement and apply the
Graphical models to solve real world applications.
4. Evaluate the applications of fuzzy systems and their usage in hybrid intelligent
systems, in combination with evolutionary computing and other machine learning
methods.
5. Apply evolutionary computing methods to develop solutions for the real world
optimisation problems and appraise their advantages and limitations.
Task and Mark distribution:
This coursework consists of two tasks and you should attempt both and submit one Word or pdf
file (or similar) for each task. Each task is worth 50 marks and the marks breakdown for each task
is provided with each task. This coursework contributes 100% to your overall module mark.

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assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

Task 1: The Machine learning algorithms for solving real-world problems in Regression,
Classification, modelling data, and text mining
Individual/Group (of size 2) Research Paper: 50% of the module mark
Context
During this module, you learned about different advanced machine learning techniques,
associated concepts and applications. We explored the Gaussian process model, which is
computationally efficient method for Regression, Classification, optimization, etc. We have also
covered the Bayesian networks as promising tools for modelling the data with complex
dependency structure. Finally, you have learned how to use Dirichlet Latent processes for
unsupervised learning applications, particularly text mining.
In this assignment, you will have to select an application related to a regression, classification,
modelling un-structured data, or text mining problem, and explore how best to apply the machine
learning algorithms to solve it. The selected application for each of the methods mentioned above
should have the following features:
1.
Gaussian Process regression and Classification: The application selected for any of
these two methods must consist of
at least four input variables and a single output
variable
. You must also implement Gaussian process classification by appropriately define
a threshold on the output variable to create a binary or multiple classes first, and then apply
the Gaussian process classification on the categorized output.
2.
Bayesian network: If you are choosing an application for this method, this application must
consist of at least
eight random variables. The random variables could be all discrete or
continuous or hybrid.
3.
There is no restriction on selecting the application to apply the Latent Dirichlet allocation
model for topic modelling.
There are
some potential projects listed below, which could be studied to get some ideas.
However, I strongly recommend you to come up with
your own idea(s) by reviewing these project
and some other relevant and recent articles.
1.
This dataset from the UCI repository is quite interesting. The task is to predict the depth
in the body (effectively, the depth along the spine) given the properties of a two
dimensional “slice” of the body. The hard part about this problem is that it is actually the
output causing the input rather than the other way around. I have not had luck designing
a good regression method for this data. Can you do this?
2. Find a Bayesian interpretation of
elastic net regularization, and compare this method for
regression against “standard” Bayesian regression (with a Gaussian prior) on a dataset
of your choosing.
3.
Probabilistic PCA using Gaussian Process is a Bayesian interpretation of the classical
PCA algorithm for
dimensionality reduction. Implement Gaussian Process based PPCA
in Python, R or Matlab, and compare its performance with other methods (such as
“standard” PCA) on a dataset of your choosing.
4.
Bayesian optimization is very important issue with a wide range of applications.
However, this was not fully studied during lectures, but it can be easily implemented
using
Gaussian Process. The Python codes and some examples can be found here!
5. The squared exponential covariance is widely used for Gaussian process regression. It
is probably used in 90+% of all GP publications. That said, it is widely believed to be
“too smooth” for many real-world regression tasks. Compare the squared exponential

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assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

covariance versus the Matéern covariance on several datasets via Bayesian model
selection. How often is the squared exponential the right choice?
6. Latent Dirichlet allocation (LDA) is a Bayesian method for creating “topic models” of text
documents. There are plenty of interesting text datasets available (e.g., DBpedia could
be a good resource!). One idea would be to compare the behavior of LDA with other
techniques, such as latent semantic analysis.
You may be able to get
relevant dataset and ideas by visiting the following sites:
This compentition site consists of some relevant data, and the relevant ideas could be
developed by analyzing this data. Check also dataset in Kaggle competitions.
This website has a fantastic compilation of 100 interesting, relevant datasets from all sorts
of application areas.
The creators of libSVM have also compiled a great list of datasets, all in a standardized
format. The libSVM codebase also includes
libsvmread for reading these in MATLAB.
The UCI Machine Learning Repository is a mainstay in machine-learning research. There
is a wide range of datasets there from many different application areas and with many
different properties (large, small, high-dimensional, low-dimensional, classification,
regression, etc.).
DBpedia is an amazing resource that automatically extracts structured data from Wikipedia.
They have all sorts of data available for download in convenient formats. This tool can be
used to extract labeled graphs from DBpedia, but there is so much more you could do.
The purpose of the first TASK of this coursework is to
Examine the fundamental concepts of machine learning, their implementation and
application.
Perform appropriate preparation of a dataset and evaluate the performance of different
learning algorithms on this dataset.
Gain practical experience in selecting machine learning algorithms for solving a real-life
Regression, classification, modelling data with complex dependency structure, or text
mining problems.
Demonstrate effectiveness in project teamwork and leadership.
You will be required to:
Ideally work in groups of 2 or 3, developing a paper/report by providing the details of
contributions of each group member in developing/writing the project report. If you are
working in a group of two, you need to consider developing
at least one common
methodology and one individual techniques for your analysis
. For individual groups,
at least two different analysis methods must be applied and compared.
Actively participate in all activities;
Consult with your tutor about your project work if needed during the support sessions.
You will write a proposal (maximum of 1 A4 page), giving the title of the project, the
names of all group members, the description of the problem and the plan of the work. You
will need to
submit this proposal to your tutor by Tuesday 2nd of August via 7135CEM
Aula submission link. In case of any required changes, you will receive feedback from
your tutor.

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assessed work for this module and should not be passed to third parties or posted on any
website. Any infringements of this rule should be reported to
[email protected].

Any questions about your work can be raised in the first two follow-up sessions on 27th
July and 3rd of August (2-5pm)
to be held online (via MSTeams) or possibly Face-to
Face on Campus.
Your final submission on TASK 1 will include a scientific paper (in 6 pages A4, up to
4000 words), written individually or as group based on the experience and the
results gained during the group work.
You will have to acknowledge the
contributions of all group members in your paper by clearly stating the details of
your individual contributions in a separate section
.
You are encouraged to target a certain conference or journal and submit the proposed paper to it.
You can either use the template of
Machine Learning Journal, or any other single column formats
from other relevant journal or conference sites.
The paper should broadly include the following sections:
Abstract
Introduction (where you introduce the problem along a short literature review of related
work; if the literature review is longer, it is recommended to be a section on its own)
Problem and Data set(s) description (where you describe in detail the problem you want to
solve and its significance)
Methods (where you shortly describe the machine learning methods and/or other methods
employed to solve the problem)
Experimental setup (including data pre-processing, feature selection and extraction)
Results
Social, ethical, legal and professional considerations
Discussion and Conclusions
References
These are generic section titles, which you may adapt appropriately to the application/problem that
is being investigated. You may include sections describing modifications of algorithms or
developments that are novel and specific to your work. You may include figures, tables, pseudo
code, and appendices with the actual code that has been developed.
The project general guidelines and milestones:
Please note, the following guidelines are good practice and should lead to better result, but you
have the freedom to pick whatever is suitable for your style:
Working in groups of maximum 2 or 3, you have to select a challenging real world problem and
one (or more) appropriate data set(s) as suggested above. You could also use the following
links, which have numerous problems and data sets:
1. Learning Repository:
http://archive.ics.uci.edu/ml/;
2. Kaggle competitions: http://www.kaggle.com/competitions;
3. PhysioBank Databases
https://archive.physionet.org/physiobank/database/
4. kdnuggets https://www.kdnuggets.com/datasets/index.html
5. DATA CATALOG https://catalog.data.gov/dataset?tags=diabetes

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assessed work for this module and should not be passed to third parties or posted on any
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[email protected].

6. Vision & Eye Health Data Portal
https://chronicdata.cdc.gov/browse?category=Vision+%26+Eye+Health

Marking Scheme for Task 1 Mark
1) Proposal
The description of the problem and the initial plan of the
work;
The initial and correct application of the proposed methods
for the selected dataset;
Notes:
1. You will not get the full marks of this section if you
submit your proposal late.
2. If the final submission of your CW is the different to what
you propose in your proposal, you will not get any
marks for parts 2 & 3.
5
2) Technical quality
1. Rigour and extent of the experiments.
2. Correct application of the selected algorithms and suitability of
the methods.
3. Data preparation – technical quality.
4. Extent of evidence of running the experiments provided in
appendices.
5 5 5 5
3) Evaluation
1. Evaluation and discussion of the results. Why the results are
important? How would the results be useful to other researchers
or practitioners?
2. Is this a “real” problem or a small “toy” problem? How does the
paper advance the state of the art?
Notes about Valid codes:
All your programming code should be included in the Appendix
of your report or provided via a valid GitHub link. Please display
them in a structured way (put headings for each Task
implemented in your CW), with appropriate
comments/annotations.
You need to attach the original R code (or Python/Matlab), NOT
the screenshots of the code
.
The code will be marked as part of the above marking scheme
(for all the Tasks in this coursework, you will need to provide the
corresponding code; when you describe/discuss the Tasks in
the main text of the report, please reference the corresponding
code section in the Appendix or link).
7 2

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assessed work for this module and should not be passed to third parties or posted on any
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[email protected].

4) Social, ethical, legal and professional considerations related to
the problem in question.
2
5) Clarity of the writing:
1. Is there sufficient information for the reader to reproduce the
results? Is the language used in the paper good?
2. References and general presentation; Are results clearly
presented, with appropriate visualisations?
5 3
6) Originality:
1. Is there some original approach to the problem, original use of
techniques?
2. Is there any (and how much) difference from previous
contributions?
3 3
Task 2: Evolutionary and Fuzzy Systems

Fuzzy Logic Optimized Controller (FLC) for an Intelligent Assistive Care Environment

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Design and Implement an FLC for controlling the environmental parameters of an intelligent flat
for disabled residence, where the system needs to automate the regulation of environmental
conditions and user preferences or the operation of assistive equipment, ramps, auto adjusting
furniture, kitchen worktops, HAVC, lighting or water temperature control. The environment could
be based on a room of choice in a small flat. A more ambitious project might consider the aspects
of the whole flat but this is left to your choice.
The environmental parameters to be controlled could be ambient temperature, thermal conform
and lighting using actuators such as cooling fans, heaters/boilers, blinds and dimmer switches.
You might also consider other parameters such as TV or music volume control, and power down
options for electronic devices and heating. Environmental parameters could be controlled based
on monitoring sensors such as temperature, humidity, weather conditions, light levels, time of
day, level of activity / motion of the user as well as mood and qualitative indicators such as user
preferences.
https://bstassen.wordpress.com/tag/ambient-assisted-living/
FLC Design
The FLC should be based on determining the inputs and outputs of the system, depending on
what control behaviour(s) you decide the FLC should implement.
Note that, depending on the
control behaviours you wish to implement, you can select to use a subset of the input sensors
for example, so first think about the behaviour(s) the FLC should control
.

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assessed work for this module and should not be passed to third parties or posted on any
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Design choices should be made to consider the type and number of fuzzy sets for the inputs
and/or outputs of the FLC.
A set of suitable control rules should be defined, which can be experimented with to achieve a
good control performance of the chosen behaviour(s).
The FLC should therefore implement the followings:
Consideration of which Fuzzy Inference model to use: Mamdani or Sugeno (TSK) fuzzy
models.
Mapping the crisp input and output data into the designed fuzzy sets.
Map input fuzzy sets into output fuzzy sets (for Mamdani model) based on a set of designed
rules that capture the desired control behaviour of the system.
Employ appropriate inference operation (rule implication) that handles the way in which rules
are activated and combined together (
composition and aggregation).
The outputs of the fuzzy inference engine will define a modified output fuzzy set (for Mamdani
model) that specifies a possibility distribution of the control actions in relation to activated
rules.
Use an appropriate defuzzifier to convert the modified fuzzy outputs into nonfuzzy (crisp)
control values that can then be used to set the actuation outputs..
Part 1 – Design and Implementation of the FLC
(30 Marks)
Design and implement a demonstrable FLC, which can be a simulated system programmed in
Matlab, FuzzyLite or Juzzy, see links below:
Matlab Fuzzy Logic Toolbox (
http://uk.mathworks.com/videos/getting-started-with-fuzzy-logic
toolbox-part-1-68764.html
,
http://www-rohan.sdsu.edu/doc/matlab/toolbox/fuzzy/fuzzyt10.html)
Fuzzylite (
http://www.fuzzylite.com)
Juzzy (
http://juzzy.wagnerweb.net)
Provide suitable evidence of your implementation in the form of diagrams and screenshots of the
different components.
(16 marks)
Discuss and justify your design decisions for the choice of fuzzy sets – membership functions,
fuzzy rules, FLC inference mechanism selected, and defuzzification method that was chosen.
Back up your explanations with evidence in the form of appropriate diagrams and screenshots.
(7 marks)
Perform analysis of the output behaviour of the controller showing the rules activation, controller
output and control surface plots, demonstrating how the controller achieves the specified
behaviours in relation to an operational scenario.
(7 marks)

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assessed work for this module and should not be passed to third parties or posted on any
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[email protected].

Part 2 – Optimize the FLC developed for Part 1
(10 Marks)
Consider the Fuzzy Logic Controller (FLC) for controlling the smart home you have designed for
the above Part 1. The purpose of this part is to optimize the fuzzy controller you have previously
developed. A data set of n input-output examples, (x
i,yi), i = 1,2 …, n, is available to evaluate
the performance of your controller.
Keeping the same structure of the FLC as you have used in Part 1, design a genetic algorithm
to adjust the membership functions of the input and output variables of the FLC in order to
optimize the performance of your FLC. Give details of the genetic algorithm you have used, i.e.,
problem encoding, genetic operators, fitness function. Some of you may have designed the FLC
as a Mamdani model, while others may have used Sugeno models. Clarify what is the length of
the chromosomes used in your solution. In case you have used a Mamdani model to implement
your FLC, describe how the genetic algorithm solution would change if you were to use a Sugeno
model for your FLC. Conversely, if you have used a Sugeno model in Part 1, describe how the
genetic algorithm solution would change if you were to use a Mamdani model for your FLC.
Part 3 – Compare different optimization techniques on CEC’2005 functions
(10 Marks)
Choose two functions from the CEC’2005 suite of benchmark functions available here:
http://www.cmap.polytechnique.fr/~nikolaus.hansen/Tech-Report-May-30-05.pdf
More details about the special session at CEC’2005 can be found here:
https://www.al-roomi.org/benchmarks/cec-database/cec-2005
Some of the links in these pages are broken, but you will be able to download the Matlab
code for the functions if you click “Resources Database (Different Formats) [Download]”
on the last web page indicated above.
This part is to compare the performance of at least 2 different optimization techniques on the two
functions you have chosen, for both D=2 and D=10, where D is the number of dimensions. If you
want to challenge yourself, you may try higher dimensional spaces, for example D=100, but this
is optional. As optimization techniques to compare in this part, you may choose Genetic
Algorithms, Particle Swarm Optimization, Simulated Annealing or other optimization methods
available in the Global Optimization Toolbox or the Optimization Toolboox in Matlab, or
developed as standalone programs by yourself.
To make the comparison meaningful you would have to run each optimization algorithm 15 times
and report the average performance (including the standard deviation of the obtained results),
as well as the best and the worst performance among the 15 runs. You may try to compare your
results with results reported in the literature on the same functions.
In your report, you should include the description of the functions you have selected, the Matlab
code for those functions, the results obtained and the parameters of the optimization algorithms

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used to obtain the reported results, any other Matlab scripts or code used in your simulations,
convergence graphs, etc.
Parts and Mark distribution for Task 2:

Part 1
Part 2
Part 3
30
10
10

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Marking Rubric (To be edited by staff per each assessment)
PG

Mark
band
Outcome Guidelines
90-100%
Distinction
Meets learning
outcomes
Distinction – Exceptional work with very high degree of rigour, creativity and critical/analytic skills. Mastery of
knowledge and subject-specific theories with originality and autonomy. Demonstrates exceptional ability to analyse and
apply concepts within the complexities and uncertainties of the subject/discipline.
Innovative research with exceptional ability in the utilisation of research methodologies. Demonstrates, creativity,
originality and outstanding problem-solving skills. Work completed with very high degree of accuracy, proficiency and
autonomy. Exceptional communication and expression demonstrated throughout. Student evidences the full range of
technical and/or artistic skills. Work pushes the boundaries of the discipline and may be strongly considered for external
publication/dissemination/presentation.
80-89%
Distinction
Distinction – Outstanding work with high degree of rigour, creativity and critical/analytic skills. Near mastery of
knowledge and subject-specific theories with originality and autonomy. Demonstrates outstanding ability to analyse and
apply concepts within the complexities and uncertainties of the subject/discipline.
Innovative research with outstanding ability in the utilisation of research methodologies. Work consistently
demonstrates creativity, originality and outstanding problem-solving skills. Work completed with high degree of
accuracy, proficiency and autonomy. Outstanding communication and expression demonstrated throughout. Student
demonstrates a very wide range of technical and/or artistic skills. With some amendments, the work may be considered
for external publication/dissemination/presentation
70-79%
Distinction
Distinction – Excellent work undertaken with rigour, creativity and critical/analytic skills. Excellent degree of knowledge
and subject-specific theories with originality and autonomy demonstrated. The work exhibits excellent ability to analyse
and apply concepts within the complexities and uncertainties of the subject/discipline.
Innovative research with excellent ability in the utilisation of research methodologies. Work demonstrates creativity,
originality and excellent problem-solving skills. Work completed with very consistent levels of accuracy, proficiency and
autonomy. Excellent communication and expression demonstrated throughout. Student demonstrates a very wide
range of technical and/or artistic skills.
60-69%
Merit
Merit – Very good work often undertaken with rigour, creativity and critical/analytic skills. Very good degree of
knowledge and subject-specific theories with some originality and autonomy demonstrated. The work often exhibits the
ability to fully analyse and apply concepts within the complexities and uncertainties of the subject/discipline.

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parties or posted on any website. Any infringements of this rule should be reported to
[email protected].

Very good research evidence and shows very good ability in the utilisation of research methodologies. Work
demonstrates creativity, originality and problem-solving skills. Work completed with very consistent levels of accuracy,
proficiency and autonomy. Very good communication and expression demonstrated throughout. Student demonstrates
a wide range of technical and/or artistic skills.
50-59%
Pass
Pass – Good work undertaken with some creativity and critical/analytic skills. Demonstrates knowledge and subject
specific theories with some originality and autonomy demonstrated. The work exhibits the ability to analyse and apply
concepts within the complexities and uncertainties of the subject/discipline.
Good research and shows some ability in the utilisation of research methodologies. Work demonstrates problem-solving
skills and is completed with some level of accuracy, proficiency and autonomy. Satisfactory communication and
expression demonstrated throughout. Student demonstrates some of the technical and/or artistic skills.
40-49%
Pass
Pass – Assessment demonstrates some advanced knowledge and understanding of the subject informed by current
practice, scholarship and research. Work may be incomplete with some irrelevant material present. Sometimes
demonstrates the ability to analyse and apply concepts within the complexities and uncertainties of the
subject/discipline.
Acceptable research with evidence of basic ability in the utilisation of research methodologies. Demonstrates some
originality, creativity and problem-solving skills but often with inconsistencies. Expression and presentation sufficient for
accuracy and proficiency. Sufficient communication and expression with professional skill set. Student demonstrates
some technical and/or artistic skills.
30-39%
Fail
Fails to achieve
learning
outcomes
Fail – Very limited understanding of relevant theories, concepts and issues with deficiencies in rigour and analysis. Some
relevant material may be present but be informed from very limited sources. Fundamental errors and some
misunderstanding likely to be present. Demonstrates limited ability to analyse and apply concepts within the
complexities and uncertainties of the subject/discipline.
Limited research scope and ability in the utilisation of research methodologies. Limited originality, creativity, and
struggles with problem-solving skills. Expression and presentation insufficient for accuracy and proficiency. Insufficient
communication and expression and with deficiencies in professional skill set. Student demonstrates deficiencies in the
range of technical and/or artistic skills.
20-29%
Fail –
Fail – Clear failure demonstrating little understanding of relevant theories, concepts, issues and only a vague knowledge
of the area. Little relevant material may be present and informed from very limited sources. Serious and fundamental
errors and virtually no evidence of relevant research. Fundamental errors and misunderstandings likely to be present.

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parties or posted on any website. Any infringements of this rule should be reported to
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Little or no research with no evidence of utilisation of research methodologies. No originality, creativity, and struggles
with problem-solving skills. Expression and presentation insufficient for accuracy and proficiency. Insufficient
communication and expression and with serious deficiencies in professional skill set. Student has clear deficiencies in
range of technical and/or artistic skills.
0-19%
Fail
Fail – Clear failure demonstrating no understanding of relevant theories, concepts, issues and no understanding of area.
Little or no relevant material may be present and informed from minimal sources. No evidence of ability in the
utilisation of research methodologies. No evidence of originality, creativity, and problem-solving skills. Expression and
presentation deficient for accuracy and proficiency. Insufficient communication and expression and with deficiencies in
professional skill set. Student has clear deficiencies in range of technical and/or artistic skills.

UG

Mark band Outcome Guidelines
90-100%
1st
Meets learning
outcomes
1st – Exceptional work with very high degree of understanding, creativity and critical/analytic skills. Evidence of exceptional
research well beyond minimum recommended using a range of methodologies. . Exceptional understanding of knowledge and
subject-specific theories. Demonstrates creative flair, a high degree of originality and autonomy.
Exceptional ability to apply learning resources. Demonstrates well-developed problem-solving skills. Work completed with very
high degree of accuracy and proficiency and autonomy. Exceptional communication and expression, significant evidence of
professional skill set. Student evidences deployment of a full range of exceptional technical and/or artistic skills.
80-89%
1st
1st – Outstanding work with high degree of understanding, creativity and critical/analytical skills. Outstanding understanding of
knowledge and subject-specific theories. Evidence of outstanding research well beyond minimum recommended using a range of
methodologies. Demonstrates creative flair, originality and autonomy.
Outstanding ability to apply learning resources. Demonstrates clear problem-solving skills. Assessment completed with high
degree of accuracy and proficiency and high-level of autonomy. Outstanding communication and expression, evidence of
professional skill set. Student evidences deployment of a full range of technical and/or artistic skills.
70-79%
1st
1st – Excellent work with clear evidence of understanding, creativity and critical/analytical skills. Thorough research well beyond
the minimum recommended using methodologies beyond the usual range. Excellent understanding of knowledge and subject
specific theories with evidence of considerable originality and autonomy.
Excellent ability to apply learning resources. Demonstrates consistent, coherent substantiated argument and interpretation.
Demonstrates considerable creativity and clear problem-solving skills. Assessment completed with accuracy, proficiency, and
considerable autonomy. Excellent communication and expression, some evidence of professional skill set. Student evidences
deployment of a highly developed range of technical and/or artistic skills.

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60-69%
2:1
2:1 – Very good work demonstrating strong understanding of theories, concepts and issues with clear critical analysis. Thorough
research, using established methodologies accurately, beyond the recommended minimum with little, if any, irrelevant material
present. Very good understanding, evidencing breadth and depth, of knowledge and subject-specific theories with some
originality and autonomy.
Very good ability to apply learning resources. Demonstrates coherent substantiated argument and interpretation. Demonstrates
some originality, creativity and problem-solving skills. Work completed with accuracy, proficiency, and autonomy. Very good
communication and expression with evidence of professional skill set. Student has a thorough command of a good range of
technical and/or artistic skills.
50-59%
2:2
2:2 – Good understanding of relevant theories, concepts and issues with some critical analysis. Research undertaken accurately
using established methodologies, enquiry beyond that recommended may be present. Some errors may be present and some
inclusion of irrelevant material. Good understanding, with evidence of breadth and depth, of knowledge and subject-specific
theories with indications of originality and autonomy.
Good ability to apply learning resources. Demonstrates logical argument and interpretation with supporting evidence.
Demonstrates some originality, creativity and problem-solving skills but with inconsistencies. Expression and presentation mostly
accurate, proficient, and conducted with some autonomy. Good communication and expression with appropriate professional
skill set. Student consistently demonstrates a well-developed range of technical and/or artistic skills.
40-49%
3
rd Class
3rd – Meet the learning outcomes with a basic understanding of relevant theories, concepts and issues.. Demonstrates an
understanding of knowledge and subject-specific theories sufficient to deal with concepts. Assessment may be incomplete and
with some errors. Research scope sufficient to evidence use of some established methodologies. Some irrelevant material likely
to be present.
Basic ability to apply learning resources. Demonstrates ability to devise and sustain an argument. Demonstrates some originality,
creativity and problem-solving skills but with inconsistencies. Expression and presentation sufficient for accuracy and proficiency.
Sufficient communication and expression with basic professional skill set. Student demonstrates technical and/or artistic skills.
30-39%
Fail
Fails to achieve
learning outcomes
Fail – Very limited understanding of relevant theories, concepts and. Little evidence of research and use of established
methodologies. Some relevant material will be present. Deficiencies evident in analysis. Fundamental errors and some
misunderstanding likely to be present.
Limited ability to apply learning resources. Student’s arguments are weak and poorly constructed. Very limited originality,
creativity, and struggles with problem-solving skills. Expression and presentation insufficient for accuracy and proficiency.
Insufficient communication and expression and with deficiencies in professional skill set. Student demonstrates some
deficiencies in technical and/or artistic skills.
20-29%
Fail
Fail – Clear failure demonstrating little understanding of relevant theories, concepts and issues. Minimal evidence of research and
use of established methodologies and incomplete knowledge of the area. Serious and fundamental errors and aspects missing

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Little evidence of ability to apply learning resources. Students arguments are very weak and with no evidence of alternative
views. Little evidence of originality, creativity, and problem-solving skills. Expression and presentation deficient for accuracy and
proficiency. Insufficient communication and expression and with deficiencies in professional skill set. Student demonstrates a
lack of technical and/or artistic skills.
0-19%
Fail
Fail – Inadequate understanding of relevant theories, concepts and issues. Complete failure, virtually no understanding of
requirements of the assignment. Material may be entirely irrelevant. Assessment may be fundamentally wrong, or with major
elements missing. Not a serious attempt. No evidence of research.
Inadequate evidence of ability to apply learning resources. Very weak or no evidence of originality, creativity, and problem
solving skills. Students presents no evidence of logical argument and no evidence of alternative views. Expression and
presentation extremely weak for accuracy and proficiency. Communication and expression very weak and with significant
deficiencies in professional skill set. Student evidences few or no technical and/or artistic skills