Commit a01f0ed1 authored by Jamie Forth's avatar Jamie Forth

april coursework changes

parent c9111f23
......@@ -2818,7 +2818,7 @@ In this module we are primarily concerned with research questions.
*** Survey project research statements (formative)
This formative assessment activity provides an opportunity to explore
potential ideas for coursework element 1 – survey project.
potential ideas for coursework element 1.1 – survey project.
Complete the following steps:
1. read the full specification of the assignment to ensure you
......@@ -2834,8 +2834,8 @@ Complete the following steps:
- your research questions should be specific and realistic
- pay particular attention to the scope implied by different
research questions you develop
- aim for one or two research objectives per topic, and three or
four research questions per topic
- aim for one or two general research objectives per topic, and
three or four very specific research questions per topic
4. compare your ideas to the examples provided
- are your research statements wider or narrower in scope?
- does the topic of the survey require specific participants, or is
......
......@@ -4438,11 +4438,10 @@ You should keep you surveys as short as possible. Aim for
approximately ten survey questions (in addition to basic demographic
questions). The exact number of questions is not the primary issue –
the important thing is that your survey should collect the data
necessary to answer your two or three specific research questions, and
no more.
necessary to answer your specific research questions, and no more.
The word limit of 1500–2000 words for the final report is the hard
limit on scope.
The word limit of 3000 words for the final report is the hard limit on
scope.
Your questionnaire should include:
- a clear statement about the topic of the questionnaire
......@@ -4507,7 +4506,7 @@ Student peer-grading rubric.
- good number of questions to capture sufficient data for
analysis without excess
Please provide commends where appropriate to justify your assessment
Please provide comments where appropriate to justify your assessment
of the survey.
**** Make your survey live
......
......@@ -2286,57 +2286,69 @@ TBC
:END:
*** C1.1 Survey report (summative)
- Submit your report as a *PDF* file (approx. 1500–2000 words).
- You can write your report either as a Jupyter Notebook using
inline markdown, or using a separate document editor.
- In either case export the document as a PDF as a hard-copy (using
‘print to pdf’ in the browser is fine, you do not have to install
XeLaTeX to export from within Jupyter)
- If you use LibreOffice/Word/LaTeX etc., *do not use screenshot
images*, save your figures as high quality images (e.g. 300 DPI
PNG files).
- Submit all code and data (scripts and/or notebook files) in a
*ZIP* file.
- Include a copy of your survey, either as an appendix or a separate
PDF file.
- You must write your report as a Jupyter notebook using inline
markdown.
- You must submit all work in a single *ZIP* file.
- Your *ZIP* file must include:
- your notebook (=ipynb=)
- all survey data (=csv=, =xlsx=, =ods= files etc., data can be
anonymised)
- any supplementary scripts
- a copy of your survey as a PDF file.
- The maximum word limit is 3000 words (suggested range 2000–3000
words).
- Include any supplementary information not essential to the main body
of the report as appendices. Appendices do /not/ count towards the
word limit.
- No marks will be directly awarded for material submitted in
appendices.
- No marks will be awarded for analysis discussion submitted as
comments in code cells.
- See provided template notebook for how to count the number of words
in your notebook.
**** Report guidelines
Reports should include discussion of the following points.
***** Research topic and background [15%]
***** Research topic and background [0%]
- introduction
- overview of topic
- relevant news or research articles
- research objectives and motivation
- overview of key findings
- research question(s)
- population
- population and sampling method
- explicitly stated research question(s)
- scope (should be appropriate for the assignment)
- domain concepts
- clearly define important terms and concepts in the study
***** Survey design [5%]
***** Survey design [0%]
- describe structure
- operational definitions
- justification of how the survey questions will produce good data
that are relevant to the research question(s)
- justification of how the survey questions will produce good data
that are relevant to the research question(s)
***** Analysis [60%]
***** Data overview and pre-processing [10%]
- overview of data
- data types and pre-processing
- brief description of variables and data types
- describe and justify data cleaning and pre-processing (i.e. tidy
data)
- handing of missing or erroneous data
- data summary statistics
- number of responses
- brief description of key variables
- descriptive statistics
- summarise demographics
- summary of demographics and key variables
- use of tables or easily understandable quantities in prose
- appropriate graphs and/or tables summarising key variables
- structure analysis with respect to research question(s)
***** Analysis [50%]
- appropriate graphs and/or tables presenting analysis and findings in
relation to research questions
- visualise individual variables
- visualise relationships between variables
- aim for high quality *explanatory* visualisation that describe or
......@@ -2349,13 +2361,15 @@ Reports should include discussion of the following points.
***** Code [10%]
- all python code files should be submitted: Notebooks =.ipynb= or
scripts =.py= files
- all python code should be submitted in your notebook (=.ipynb= file)
- supplementary scripts (=.py= files) that are called from within your
notebook can be used and must also be submitted
- code should be legible, with brief comments
- re-using and adapting code you find in documentation or elsewhere
online is OK, but sources must be attributed correctly (web link and
date accessed)
- re-using and adapting code covered during the module is encouraged
- make sure all code runs correctly prior to submission
**** Rubric
***** Research topic and background [15 marks]
......@@ -2363,16 +2377,16 @@ Reports should include discussion of the following points.
- [0] No introduction
- [1] Key points briefly discussed
- [2] Clear and concise discussion
****** Motivation and objectives
- [0] No motivation or objectives
- [1] Key motivations briefly discussed
- [2] Clear and concise rational motivating the study and potential
impact
****** Background and context
- [0] No background
- [1] Brief discussion of related wider issues
- [2] Clear and concise discussion of related research or news
stories
****** Motivation and objectives
- [0] No motivation or objectives
- [1] Key motivations briefly discussed
- [2] Clear and concise rational motivating the study and potential
impact
****** Sample and population
- [0] Not discussed
- [1] Sample and population stated
......@@ -2410,27 +2424,28 @@ Reports should include discussion of the following points.
(e.g. multi-question operationalisation) and/or use of sophisticated
measurement scales from published research
***** Analysis [60 marks]
****** Overview of data [5 marks]
***** Data overview and pre-processing [10 marks]
****** Data types and pre-processing
- [0] Not discussed
- [1] Number of responses stated
- [1] Description of key variables and data types
- [2] Discretionary mark
- [3] Descriptive statistics presented for demographics and key variables
- [3] Discussion of data cleaning and tidy data
- [4] Discretionary mark
- [5] Reflective discussion of response data and appropriate handling
of missing, 'other', or erroneous data
****** Tables and summary stats [5 marks]
****** Data summary statistics
- [0] No tables or summary stats used
- [1] Basic tables or inline descriptive statistics for key variables,
e.g. using pandas describe() included in prose
- [2] Discretionary mark
- [1] Number of responses stated
- [2] Basic tables or inline descriptive statistics for key variables
describing statistical quantities in simple language
- [3] Appropriate use of cross-tabulation and sorting
- [4] Discretionary mark
- [5] Highly effective communicative tables showing more advanced data
processing such as grouping, aggregating, filtering or normalisation
****** Appropriate plots for each variable data type
***** Analysis [50 marks]
****** Appropriate plots for variable data types
- [0] Many highly inappropriate visualisations, e.g. using line graphs
for non-sequential data, pie charts with many categories, or
pointless use of 3D
......@@ -2455,10 +2470,13 @@ Reports should include discussion of the following points.
****** Visual communication
- [0] Many meaningless or pointless plots
- [5] Some effective simple plots, but also some confusing or misleading visualisations
- [5] Some effective simple plots, but also some confusing or
misleading visualisations
- [10] Discretionary
- [15] Consistent high quality univariate plots, with some effective multivariate plots
- [20] Good range of univariate and multivariate plots, each able to effectively communicate a strong message
- [15] Consistent high quality univariate plots, with some effective
multivariate plots
- [20] Good range of univariate and multivariate plots, each able to
effectively communicate a strong message
- [25] Discretionary
- [30] Consistent highly efficient visual communication requiring
little or no explanation
......@@ -2488,13 +2506,13 @@ Reports should include discussion of the following points.
- [0] No code submitted
- [2] Python code for each visualisation
- [4] Discretionary
- [6] Scripts and notebooks are well commented and make idiomatic use
of Python data science libraries (i.e. using the APIs correctly
results in fewer lines of code, which is generally better)
- [6] Code is well commented and make idiomatic use of Python data
science libraries (i.e. using the APIs correctly results in fewer
lines of code, which is generally better)
- [8] Discretionary
- [10] Scripts and notebooks are well commented and logically
structured with minimal copy-and-paste code, ensuring that the
process of analysis is transparent and reproducible
- [10] Code is well commented and logically structured with minimal
copy-and-paste code, ensuring that the process of analysis is
transparent and reproducible
** Topic summary
......
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