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python代写-SCC403-Assignment 1
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SCC403 – Data Mining
Coursework Assignment 1
1 Introduction
The objective of the assignment is to conduct pre-processing using two sets of different real life
data. The first data set concerns the climate and the second data is a video stream. The assignment
includes selection and justification of the specific methods for data pre-processing (normalisation,
standardisation, feature selection and/or extraction, anomaly detection, missing data (if any)),
their implementation and analysis of the results as well as a well annotated code. You are expected
to critically analyse the results of applying these techniques, and demonstrate a clear understanding
of the purpose and processes of data analysis. In addition to your report, please submit your source
code, including comments. To achieve top marks a well justified variety of specific pre-processing
techniques is expected. Analysis and understanding of the methods, algorithms and the overall
process are the most important elements in addition to the implementation skills such as the code
and the presentation.
We expect the use of Python - the most widely used language for machine learning which we
also use in the labs, but if you prefer to use a different language we may need to contact you for
clarification, if we we believe that your code is not running correctly.
2 Data Set 1
You are expected to use the set of climate data provided in the file ′ClimateDataBasel.csv′. This
data is a subset of publicly available (from https://www.meteoblue.com/) data about climate in
Basel, Switzerland which contains 1763 18-dimensional records of data from the summer and the
winter seasons of the period from 2010 to 2019. The meaning of each column of data is listed
below:
• Temperature (Min) oC.
• Temperature (Max) oC.
• Temperature (Mean) oC.
• Relative Humidity (Min) %.
• Relative Humidity (Max) %.
• Relative Humidity (Mean) %.
• Sea Level Pressure (Min) hPa.
• Sea Level Pressure (Max) hPa.
• Sea Level Pressure (Mean) hPa.
• Precipitation Total mm.
1
• Snowfall Amount cm.
• Sunshine Duration min.
• Wind Gust (Min) Km/h.
• Wind Gust (Max) Km/h.
• Wind Gust (Mean) Km/h.
• Wind Speed (Min) Km/h.
• Wind Speed (Max) Km/h.
• Wind Speed (Mean) Km/h
3 Data Stream 2
The second data concern a real multi-dimensional video stream showing two moving objects (a
car and a motorbike) represented by the file ′OriginalV ideoStream.m4v′. A snap shot of this
video stream (a single image frame) is given in Figure 1) which shows a police car in pursuit of
a motorcycle. The video contains several multi-channel data sources like RGB (Red-Green-Blue)
encoding for each pixel of each frame as well as sound.
Figure 1: An image frame from the original video.
The original video file can be processed using the so-called background subtraction method for
image processing which results in a binary video (the file ′BinaryV ideo.avi′) where the pixels of
the background are black and the pixels of the foreground (moving objects that differ from the
background) are white. A snapshot of this video (a binary image frame) is shown in Figure 2.
Figure 2: An image frame with binary info: black - background; white - foreground.
Within this binary video, of special interest are the foreground pixels and the object that they
represent when considered together.
2
Remember, that feature extraction is the process of transformation of the original features (such
as pixel colour, e.g. R, G, B or temperatures, pressures, age, etc.) into a set of new, derivative
features (e.g. size, shape, area, etc. or principle components). One possible approach for feature
extraction applicable to Data Stream2 is to form rectangular enclosures (bounding boxes) that
surround the suspected objects represented by groups of foreground pixels, see Figure 3.
Figure 3: Selecting corner pixels.
For example, these can be determined using the top left and bottom right corners of the
enclosures (bounding boxes), see Figure 3. Then, based on the coordinates of these corners it is
easy to determine the width (W), the length (L), and the area (A) of the rectangles that enclose
these objects. For the example provided in Figure 3 it can be derived that:
• Wmotorcycle = 287 - 279 = 8 pixels
• Wcar = 401 - 351 = 50 pixels
• Lmotorcycle = 142 - 109 = 33 pixels
• Lcar = 297 – 246 = 51 pixels
• A=W*L
• Amotorcycle = 264 pixels2
• Acar = 2550 pixels2
Each one of the image frames of the binary video file (′BinaryV ideo.avi′) where processed as
described above and, as a result, the set of W (width), L (length) and A (area) were determined
and saved in the file called ′WLA.csv′. The file has 188 lines and 3 columns. The columns represent
the dimensions of the rectangular blob of foreground pixels (W, L and A).
Finally, using human expertise (manual annotation) the true labels are provided in the file
′Labels.csv′ where 1 denotes ”car” and 2 - ”motorbike”. You may notice that the first 16 lines
represent image frames in which only the motorbike is visible, while the remaining 172 lines (which
represent the next 86 image frames) have both, the police car and the motorbike. So, in total there
are 102 image frames in the videoclips.
You should select features based on which the further data processing such as clustering or
classification can be performed.
Hint: due to high correlation you may decide to use less than 3 features. Please, justify your
choice.
Furthermore, you should apply other prepossessing techniques and justify your choice.
3
4 Deadlines and general requirements
The lectures and tutorials will provide you with the necessary tools to conduct your analysis. You
may also include additional analysis methods that you have researched separately,that may help
derive your conclusion (this is not compulsory).
You are expected to critically analyse the results of applying these techniques, and demonstrate
a clear understanding of the purpose and processes of data analysis.
The deadline for submission is: 4pm, 12 November 2021, Friday. The cut-off deadline
is 4pm, 15 November 2021, Monday (with late submission penalty incurred which is 1 letter grade or
10%). Submissions after this deadline cannot be accepted according to the University regulations.
In case your code is unclear to us you may be contacted for interview. If you fail to reply or
attend the interview your code could be marked as “not working”.
5 Marking Scheme
The marks are allocated as follows:
• Structure and presentation (10%)
• Language and style (5%)
• Use of literature and references (5%)
Plus the same for each of the Data Set 1 and Data Stream 2:
• Level of understanding (8%)
• Depth of analysis (8%)
• Working, well annotated code and results (8%)
• Justification of selected methods (8%)
• Independent research and use of methods not given in the lectures (8%)
At the end of this document there is an Appendix, which explains what a mark means in
Lancaster University and includes suggestions for a well-written report.
The length of the report should not exceed 4 pages. You can use double column format, e.g.
the so-called IEEE style as described in the Appendix. You may include an Appendix (2 pages
maximum) after the main report.
6 Tasks description
Pre-processing includes data standardisation and/or normalisation, detecting and removing anoma-
lies, missing values (if any), feature selection and/or extraction. Pre-processing provides an insight
into the data correlations and patterns.
If you choose to use the Principle Component Analysis (PCA) method, you can extract new,
orthogonal (independent) features, which are a linear combination of the original ones (which carry
a clear physical meaning, such as temperature or pressure). If you choose to use PCA, please,
comment on the amount of variance, interpretability and the link with the original features. You
should also plot the results using, for example, the one or two of the principle components which
contain most of the variance.
4
7 Additional Comments
You must report in an “acknowledgements” section the use of any libraries, readily available online
code, and code from online tutorials. Additionally, you are free to discuss your work with colleagues,
but you must also report in the “acknowledgments” section if anyone has helped you significantly.
Remember that using others’ work without giving the due credit is an act of plagiarism, and it is
not a good academic practice.
5
APPENDIX 1
Example of the style of the
report
Title of the Report
Subtitle as needed
Author’s names, Student number
line 1: dept. name of organization
line 2: name of the programme and
module
Abstract— Briefly describe the outline of your report.
I. Introduction
Here you have to provide the background review. of the existing
approaches stressing the ones that have been actually used.
Critically analyse and compare alternative techniques and
methods. Try to go beyond what was given in the lectures using
external sources and references.
II. Pre-processing
Here you have to provide a description and description and the
results of pre-processing techniques that are relevant and stress
those that you actually used in your work. Provide the software
code that you used to obtain the results in an Appendix. Do not
forget to justify your choice.
III. Conclusion
Describe briefly what has been done, with a summary of the
main results. Discuss here possible future developments (what
you would have done more). What is distinctive about the
results you have obtained?
VI. References
The template will number citations consecutively within
brackets [1]. The sentence punctuation follows the bracket [2].
Refer simply to the reference number, as in [3]—do not use
“Ref. [3]” or “reference [3]” except at the beginning of a
sentence: “Reference [3] was the first ...”
Number footnotes separately in superscripts. Place the
actual footnote at the bottom of the column in which it was
cited. Do not put footnotes in the reference list. Use letters for
table footnotes.
[1] J. Han, M. Kamber, Data Mining: Concepts and Techniques,
Morgan Kaufmann, 2001
[2] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Heidelberg, Germany:
Springer Verlag, 2001
[3] Angelov, P.: Autonomous Learning Systems: From Data Streams to
Knowledge in Real Time. John Wiley and Sons (2012).
[4] Angelov, P.: Outside The Box:An Alternative Data Analytics FrameWork.
Journal of Automation, Mobile Robotics & Intelligent Systems.
Vol. 8, 29–35.
Appendix
Please, provide here a well annotated working code.
Presenting someone else’s work as your own in an
assignment without proper citation of the source is an act of
plagiarism. More information about Lancaster University
Plagiarism Framework can be found
at https://www.lancaster.ac.uk/academic-standards-
andquality/information-and-resources/policies-
andguidelines/plagiarism-framework/
Please include here additional experimental results
or additional details.
6
What a Mark Means in Lancaster University
70 + (Distinction)
Critical Understanding of Topic
Excellent understanding and exposition of relevant
issues; insightful and well informed, clear evidence of
independent thought; good awareness of nuances and
complexities; appropriate use of theory.
Structure of Research
Substantial evidence of well implemented independent
research and / or Substantial evidence of well selected
evidence to support argument.
Use of Literature
Excellent use of literature to support argument /points.
Conclusion
Excellent; clear implications for theory and/or practice.
Language
Excellent; a delight to read.
Structure and Presentation
Arguments clearly structured and logically developed;
sensible weighting of parts; meaningful diagrams;
properly formatted references.
65 – 69% (Very Good Pass)
Critical Understanding of Topic
Clear awareness and exposition of relevant issues; some
awareness of nuances and complexities but tendency to
simplify matters; based on appropriate choice and use of
theory.
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Good use of literature to support arguments.
Conclusion
Very good; draws together main points; some
implications for theory and/or practice
Language
Carefully written; negligible errors.
Structure and Presentation
Arguments clearly structured and logically developed;
good weighting of parts; meaningful diagrams; properly
formatted references.
60 – 65% (Good Pass)
Critical Understanding of Topic
Shows awareness of issues and theories; attempts at
analysis but tendency to lapse into description
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Use of standard literature to support arguments.
Conclusion
Reasonable conclusion that summarises essay; a few
implications for theory and/or practice.
Language
A few errors; generally satisfactory.
Structure and Presentation
Arguments reasonably clear but undeveloped; some
meaningless diagrams or poor structure.
50 – 59% (Pass)
Critical Understanding of Topic
Work shows understanding of topic but at superficial
level; no more than expected from attendance at lectures;
some irrelevant material; too descriptive.
Structure of Research
Insufficient evidence of independent research and / or
very limited evidence used to support argument.
Use of Literature
Use of secondary literature to support arguments.
Conclusion
Conclusion does not do justice to body of essay; too
short; no implications.
Language
Some errors; grammar and syntax need attention.
Structure and Presentation
Arguments not very clear; poor organisation of material;
poor use of diagrams; poor referencing.
45 – 49% (Marginal Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
Relies on a superficial repeat of class notes.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.
0 – 44% (Clear Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
No significant reference to literature.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.
学霸联盟
Coursework Assignment 1
1 Introduction
The objective of the assignment is to conduct pre-processing using two sets of different real life
data. The first data set concerns the climate and the second data is a video stream. The assignment
includes selection and justification of the specific methods for data pre-processing (normalisation,
standardisation, feature selection and/or extraction, anomaly detection, missing data (if any)),
their implementation and analysis of the results as well as a well annotated code. You are expected
to critically analyse the results of applying these techniques, and demonstrate a clear understanding
of the purpose and processes of data analysis. In addition to your report, please submit your source
code, including comments. To achieve top marks a well justified variety of specific pre-processing
techniques is expected. Analysis and understanding of the methods, algorithms and the overall
process are the most important elements in addition to the implementation skills such as the code
and the presentation.
We expect the use of Python - the most widely used language for machine learning which we
also use in the labs, but if you prefer to use a different language we may need to contact you for
clarification, if we we believe that your code is not running correctly.
2 Data Set 1
You are expected to use the set of climate data provided in the file ′ClimateDataBasel.csv′. This
data is a subset of publicly available (from https://www.meteoblue.com/) data about climate in
Basel, Switzerland which contains 1763 18-dimensional records of data from the summer and the
winter seasons of the period from 2010 to 2019. The meaning of each column of data is listed
below:
• Temperature (Min) oC.
• Temperature (Max) oC.
• Temperature (Mean) oC.
• Relative Humidity (Min) %.
• Relative Humidity (Max) %.
• Relative Humidity (Mean) %.
• Sea Level Pressure (Min) hPa.
• Sea Level Pressure (Max) hPa.
• Sea Level Pressure (Mean) hPa.
• Precipitation Total mm.
1
• Snowfall Amount cm.
• Sunshine Duration min.
• Wind Gust (Min) Km/h.
• Wind Gust (Max) Km/h.
• Wind Gust (Mean) Km/h.
• Wind Speed (Min) Km/h.
• Wind Speed (Max) Km/h.
• Wind Speed (Mean) Km/h
3 Data Stream 2
The second data concern a real multi-dimensional video stream showing two moving objects (a
car and a motorbike) represented by the file ′OriginalV ideoStream.m4v′. A snap shot of this
video stream (a single image frame) is given in Figure 1) which shows a police car in pursuit of
a motorcycle. The video contains several multi-channel data sources like RGB (Red-Green-Blue)
encoding for each pixel of each frame as well as sound.
Figure 1: An image frame from the original video.
The original video file can be processed using the so-called background subtraction method for
image processing which results in a binary video (the file ′BinaryV ideo.avi′) where the pixels of
the background are black and the pixels of the foreground (moving objects that differ from the
background) are white. A snapshot of this video (a binary image frame) is shown in Figure 2.
Figure 2: An image frame with binary info: black - background; white - foreground.
Within this binary video, of special interest are the foreground pixels and the object that they
represent when considered together.
2
Remember, that feature extraction is the process of transformation of the original features (such
as pixel colour, e.g. R, G, B or temperatures, pressures, age, etc.) into a set of new, derivative
features (e.g. size, shape, area, etc. or principle components). One possible approach for feature
extraction applicable to Data Stream2 is to form rectangular enclosures (bounding boxes) that
surround the suspected objects represented by groups of foreground pixels, see Figure 3.
Figure 3: Selecting corner pixels.
For example, these can be determined using the top left and bottom right corners of the
enclosures (bounding boxes), see Figure 3. Then, based on the coordinates of these corners it is
easy to determine the width (W), the length (L), and the area (A) of the rectangles that enclose
these objects. For the example provided in Figure 3 it can be derived that:
• Wmotorcycle = 287 - 279 = 8 pixels
• Wcar = 401 - 351 = 50 pixels
• Lmotorcycle = 142 - 109 = 33 pixels
• Lcar = 297 – 246 = 51 pixels
• A=W*L
• Amotorcycle = 264 pixels2
• Acar = 2550 pixels2
Each one of the image frames of the binary video file (′BinaryV ideo.avi′) where processed as
described above and, as a result, the set of W (width), L (length) and A (area) were determined
and saved in the file called ′WLA.csv′. The file has 188 lines and 3 columns. The columns represent
the dimensions of the rectangular blob of foreground pixels (W, L and A).
Finally, using human expertise (manual annotation) the true labels are provided in the file
′Labels.csv′ where 1 denotes ”car” and 2 - ”motorbike”. You may notice that the first 16 lines
represent image frames in which only the motorbike is visible, while the remaining 172 lines (which
represent the next 86 image frames) have both, the police car and the motorbike. So, in total there
are 102 image frames in the videoclips.
You should select features based on which the further data processing such as clustering or
classification can be performed.
Hint: due to high correlation you may decide to use less than 3 features. Please, justify your
choice.
Furthermore, you should apply other prepossessing techniques and justify your choice.
3
4 Deadlines and general requirements
The lectures and tutorials will provide you with the necessary tools to conduct your analysis. You
may also include additional analysis methods that you have researched separately,that may help
derive your conclusion (this is not compulsory).
You are expected to critically analyse the results of applying these techniques, and demonstrate
a clear understanding of the purpose and processes of data analysis.
The deadline for submission is: 4pm, 12 November 2021, Friday. The cut-off deadline
is 4pm, 15 November 2021, Monday (with late submission penalty incurred which is 1 letter grade or
10%). Submissions after this deadline cannot be accepted according to the University regulations.
In case your code is unclear to us you may be contacted for interview. If you fail to reply or
attend the interview your code could be marked as “not working”.
5 Marking Scheme
The marks are allocated as follows:
• Structure and presentation (10%)
• Language and style (5%)
• Use of literature and references (5%)
Plus the same for each of the Data Set 1 and Data Stream 2:
• Level of understanding (8%)
• Depth of analysis (8%)
• Working, well annotated code and results (8%)
• Justification of selected methods (8%)
• Independent research and use of methods not given in the lectures (8%)
At the end of this document there is an Appendix, which explains what a mark means in
Lancaster University and includes suggestions for a well-written report.
The length of the report should not exceed 4 pages. You can use double column format, e.g.
the so-called IEEE style as described in the Appendix. You may include an Appendix (2 pages
maximum) after the main report.
6 Tasks description
Pre-processing includes data standardisation and/or normalisation, detecting and removing anoma-
lies, missing values (if any), feature selection and/or extraction. Pre-processing provides an insight
into the data correlations and patterns.
If you choose to use the Principle Component Analysis (PCA) method, you can extract new,
orthogonal (independent) features, which are a linear combination of the original ones (which carry
a clear physical meaning, such as temperature or pressure). If you choose to use PCA, please,
comment on the amount of variance, interpretability and the link with the original features. You
should also plot the results using, for example, the one or two of the principle components which
contain most of the variance.
4
7 Additional Comments
You must report in an “acknowledgements” section the use of any libraries, readily available online
code, and code from online tutorials. Additionally, you are free to discuss your work with colleagues,
but you must also report in the “acknowledgments” section if anyone has helped you significantly.
Remember that using others’ work without giving the due credit is an act of plagiarism, and it is
not a good academic practice.
5
APPENDIX 1
Example of the style of the
report
Title of the Report
Subtitle as needed
Author’s names, Student number
line 1: dept. name of organization
line 2: name of the programme and
module
Abstract— Briefly describe the outline of your report.
I. Introduction
Here you have to provide the background review. of the existing
approaches stressing the ones that have been actually used.
Critically analyse and compare alternative techniques and
methods. Try to go beyond what was given in the lectures using
external sources and references.
II. Pre-processing
Here you have to provide a description and description and the
results of pre-processing techniques that are relevant and stress
those that you actually used in your work. Provide the software
code that you used to obtain the results in an Appendix. Do not
forget to justify your choice.
III. Conclusion
Describe briefly what has been done, with a summary of the
main results. Discuss here possible future developments (what
you would have done more). What is distinctive about the
results you have obtained?
VI. References
The template will number citations consecutively within
brackets [1]. The sentence punctuation follows the bracket [2].
Refer simply to the reference number, as in [3]—do not use
“Ref. [3]” or “reference [3]” except at the beginning of a
sentence: “Reference [3] was the first ...”
Number footnotes separately in superscripts. Place the
actual footnote at the bottom of the column in which it was
cited. Do not put footnotes in the reference list. Use letters for
table footnotes.
[1] J. Han, M. Kamber, Data Mining: Concepts and Techniques,
Morgan Kaufmann, 2001
[2] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Heidelberg, Germany:
Springer Verlag, 2001
[3] Angelov, P.: Autonomous Learning Systems: From Data Streams to
Knowledge in Real Time. John Wiley and Sons (2012).
[4] Angelov, P.: Outside The Box:An Alternative Data Analytics FrameWork.
Journal of Automation, Mobile Robotics & Intelligent Systems.
Vol. 8, 29–35.
Appendix
Please, provide here a well annotated working code.
Presenting someone else’s work as your own in an
assignment without proper citation of the source is an act of
plagiarism. More information about Lancaster University
Plagiarism Framework can be found
at https://www.lancaster.ac.uk/academic-standards-
andquality/information-and-resources/policies-
andguidelines/plagiarism-framework/
Please include here additional experimental results
or additional details.
6
What a Mark Means in Lancaster University
70 + (Distinction)
Critical Understanding of Topic
Excellent understanding and exposition of relevant
issues; insightful and well informed, clear evidence of
independent thought; good awareness of nuances and
complexities; appropriate use of theory.
Structure of Research
Substantial evidence of well implemented independent
research and / or Substantial evidence of well selected
evidence to support argument.
Use of Literature
Excellent use of literature to support argument /points.
Conclusion
Excellent; clear implications for theory and/or practice.
Language
Excellent; a delight to read.
Structure and Presentation
Arguments clearly structured and logically developed;
sensible weighting of parts; meaningful diagrams;
properly formatted references.
65 – 69% (Very Good Pass)
Critical Understanding of Topic
Clear awareness and exposition of relevant issues; some
awareness of nuances and complexities but tendency to
simplify matters; based on appropriate choice and use of
theory.
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Good use of literature to support arguments.
Conclusion
Very good; draws together main points; some
implications for theory and/or practice
Language
Carefully written; negligible errors.
Structure and Presentation
Arguments clearly structured and logically developed;
good weighting of parts; meaningful diagrams; properly
formatted references.
60 – 65% (Good Pass)
Critical Understanding of Topic
Shows awareness of issues and theories; attempts at
analysis but tendency to lapse into description
Structure of Research
Some evidence of independent research reasonably well
implemented and / or some evidence of identification of
suitable evidence to support argument.
Use of Literature
Use of standard literature to support arguments.
Conclusion
Reasonable conclusion that summarises essay; a few
implications for theory and/or practice.
Language
A few errors; generally satisfactory.
Structure and Presentation
Arguments reasonably clear but undeveloped; some
meaningless diagrams or poor structure.
50 – 59% (Pass)
Critical Understanding of Topic
Work shows understanding of topic but at superficial
level; no more than expected from attendance at lectures;
some irrelevant material; too descriptive.
Structure of Research
Insufficient evidence of independent research and / or
very limited evidence used to support argument.
Use of Literature
Use of secondary literature to support arguments.
Conclusion
Conclusion does not do justice to body of essay; too
short; no implications.
Language
Some errors; grammar and syntax need attention.
Structure and Presentation
Arguments not very clear; poor organisation of material;
poor use of diagrams; poor referencing.
45 – 49% (Marginal Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
Relies on a superficial repeat of class notes.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.
0 – 44% (Clear Fail)
Critical Understanding of Topic
Establishes a few relevant points but superficial and
confused; much irrelevant material; very little or no
understanding of the issues raised by the topic or topic
misunderstood; content largely irrelevant; no choice or
use of theory; essay almost wholly descriptive; no grasp
of analysis with many errors and/or omissions.
Structure of Research
No evidence of independent research and / or No attempt
to identify suitable evidence to support argument.
Use of Literature
No significant reference to literature.
Conclusion
No recognisable conclusion.
Language
Frequent errors; needs urgent attention.
Structure and Presentation
Arguments often confused and undeveloped; no logical
structure; very poor organisation of material; many
meaningless diagrams; negligible referencing.
学霸联盟
