Assessment Brief
PROG2007 PROGRAMMING II
Summary
Title Assessment 2
Type Portfolio
Due Date Monday 8th April 11:59 pm AEST/AEDT (start of Week 6)
Length NA
Weighting 60%
Academic Integrity
(See below for limits
of use where GenAI
is permitted)
GenAI May be used for this assessment.
Please see the Academic Integrity section below for acceptable use of
GenAI in this assessment.
Submission Please see the Submission section below on how to submit your
assessment.
Unit Learning
Outcomes
This assessment task maps to the following ULOs:
ULO2: design, implement, test, and debug simple programs in an objectoriented programming language.
ULO3: demonstrate how to use class mechanisms to support
encapsulation and efficient code.
ULO4: analyse and determine appropriate data structures and iteration
methods to support a solution.
Rationale
This assessment, focused on creating a Python-based weather data extraction bot, aligns with current educational
objectives by emphasizing real-world applicability and comprehensive problem-solving skills. It integrates core
programming elements—object-oriented design, data handling, error management, and collections usage—
preparing students for professional challenges in fields reliant on accurate data, such as environmental science
and urban planning. Moreover, the task encourages students to develop robust, maintainable software, mirroring
industry demands where software reliability is crucial. The added requirement of a video presentation hones their
ability to communicate complex technical concepts, enhancing their employability by equipping them with a skill
set that is highly valued across various industries, thereby making this assessment a holistic tool for learning and
real-world application.
Task Description
In this assignment, you will write a bot in Python that extracts and processes data from a website
which contains weather information, much like the BOM site. The site has been created for this unit,
given that we don’t have permission or want to overrun the BOM site.
The assignment contains two programming parts as follows:
• In Part 1, you will design and implement an object-oriented program that reads weather
data from a csv file, stores it in an appropriate collection, and performs some simple data
analysis.
• In Part 2, you will modify your program from part one and add the ability to handle dates
and times, log messages to a log file, and implement web scraping and data analysis
capabilities using third party libraries.
This assignment also requires you to create a video explaining why you completed the assignment
the way you did.
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Assessment Brief
Task Instructions
Part 1
Visit the website at https://prog2007.it.scu.edu.au/weather and view the following pages:
• https://prog2007.it.scu.edu.au/weather/sydney.html
• https://prog2007.it.scu.edu.au/weather/melbourne.html
• https://prog2007.it.scu.edu.au/weather/brisbane.html
You will notice that all three pages have an identical format as follows:
• A location for the forecast.
• The date and time the forecast was last updated.
• A daily forecast covering 7 days from Friday the 8th of March until Thursday the 14th of
March.
Create a csv file based on the three pages (Sydney, Melbourne, and Brisbane) that contains all of the
data from the pages. You can use the following example as a template if needed or you can create
your own template. This example contains a headings row along with the data for the first record on
the Sydney forecast page:
Location,Updated Date,Forecast Date,Min Temp,Max Temp,Condition,Possible
Rainfall,Chance of any Rain,Forecast,Warning
Sydney,Forecast updated at 9:39 am EDT on Thursday 7 March 2024,Friday 8
March,Min: 21,Max: 28,Morning shower or two.,Possible rainfall: 0 to 1 mm,Chance
of any rain: 50%,Partly cloudy. Medium chance of showers in the morning and early
afternoon. Light winds becoming east to northeasterly 15 to 20 km/h in the late
afternoon then becoming light in the evening.,Sun protection recommended from 9:30
am to 4:30 pm. UV Index predicted to reach 9 [Very High]
Once you have created the csv file with all of the data from the Sydney, Melbourne, and Brisbane
pages, your job is to write an object-oriented program in Python with a minimum of five classes that:
• Reads the data in the csv file and uses the data to create appropriate objects.
• Stores all of the objects in appropriate collection(s).
• Uses a Set to perform some basic data analysis.
Things to think about:
• How will you handle and manipulate the strings in the csv file?
• How will you structure your classes and what relationships will the classes contain?
• What attributes should each class have and what data type should they be?
• How will you secure and validate the data in each class?
• How will you handle any potential errors in your program?
Two possible ways to approach the problem may be to:
• Have a forecast class with attributes for individual items in each forecast.
• Have an observation class and view the individual items in each forecast as an observation
(similar to the sample project used in the Live Coding sessions).
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Assessment Brief
Your mark will be determined based on:
• Your proficiency in handling and manipulating the strings in the CSV file into suitable data
types e.g. ints or doubles for values, units of measurements, start time and end time that
sun protection should be worn etc.
• How well you demonstrate ALL the class design concepts you have learned in Modules 1 and
2.
• How well you demonstrate the error handling concepts you have learned in Module 3.
• How well you demonstrate your ability to choose and implement appropriate collections
based on the concepts you have learned in Modules 3 and 4.
Part 2
Make a copy of your program from Part 1. Update your program so that it:
• Scrapes the data from the Sydney, Melbourne, and Brisbane pages using the request library
and Beautiful Soup instead of reading it from a csv file.
• Uses the datetime module for any date or time related data.
• Uses the logging module to output appropriate messages where applicable e.g. info,
warning, error messages.
• Performs some basic data analysis using pandas instead of a set.
You mark will be determined based on:
• The criteria listed in the previous section.
• How well you demonstrate the concepts you have learned in Module 5.
Video
You are required to create a video explaining why you completed the assignment the way that you
did. Your video should address each part of the assignment separately i.e. cover part one first, then
part two.
You are not required to explain your code line by line. Rather for each part of the assessment your
video should focus on the following:
• Your class design.
• Any techniques you've employed to secure and validate your data.
• Your error handling.
• Your use of collections.
• The data analysis you chose to do.
Resources
Everything that you need to know to complete this assessment was covered in:
• Programming 1
• Modules 1 through 5 in this unit.
Task Submission
You can use PyCharm or VSCode to complete the assignment and your assignment must run using
Python 3. This is covered in the Getting Started section of MySCU.
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Assessment Brief
The submission requirements are identical to assessment one as follows:
• Your submission must be named FirstName_LastName_A2. For example, Alex_Hendry_A2
• You are only required to submit your Python files and not your project files.
• You are required to use the WakaTime plugin and submit a screenshot of your WakaTime
dashboard when submitting your assignment clearly identifying how much time you spent
on each part of the assessment.
• Your video must be uploaded to your student OneDrive account and shared using a link.
All of the above points are covered in the submission instruction video for assessment one. The
video covers how to submit your Python files, your screenshot of the WakaTIme dashboard, and
your video. The only difference is the A2 in the submission name.
Please DO NOT start this assessment without watching the video.
Please note that all submission instructions in this assignment and the submission video must be
followed EXACTLY, including the folder names you are instructed to use. Failure to do so may result a
requirement to resubmit. The reason for this is as a programmer, you will often work as part of a
team and will be required to follow design documentation. If the design parameters are not followed
precisely, bugs will be introduced into the software when all of the individual components of the
program are assembled.
Academic Integrity
At Southern Cross University, academic integrity means behaving with the values of honesty,
fairness, trustworthiness, courage, responsibility and respect in relation to academic work.
The Southern Cross University Academic Integrity Framework aims to develop a holistic, systematic
and consistent approach to addressing academic integrity across the entire University. For more
information, see: SCU Academic Integrity Framework
NOTE: Academic Integrity breaches include unacceptable use of generative artificial intelligence
(GenAI) tools, the use of GenAI has not been appropriately acknowledged or is beyond the
acceptable limit as defined in the Assessment, poor referencing, not identifying direct quotations
correctly, close paraphrasing, plagiarism, recycling, misrepresentation, collusion, cheating, contract
cheating, fabricating information.
Use of GenAI
There are no limitations on using GenAI in this assignment providing you follow the following
guidelines:
• You understand the code you are submitting and can explain it.
• You are not using any concepts that are not taught in the unit.
• All code that was created using GenAI must be clearly identified using a comment. The
following are possible examples of comments you may use:
o This method was created using GenAI.
o This block of code was partly created using GenAI.
o This constructor was generated using ChatGPT.
• Your code runs without errors.
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Assessment Brief
If your submission is deemed to be suspicious by your marker you may be asked to attend an
interview in your tutorial class to explain your code. If you cannot explain your code, you may be
submitted for academic integrity. Possible reasons your submission may be deemed suspicious could
include:
• Using programming concepts not taught in the unit.
• Using programming concepts considered by your marker to be beyond your programming
abilities as demonstrated in the class.
• Submitting code suspected of being generated using GenAI software but is not clearly
identified.
To summarise:
• You can use GenAI as much as you like as long as you clearly identify the code you submit and
understand what it does.
• DO NOT SUBMIT ANY CODE THAT YOU DO NOT UNDERSTAND AND CANNOT EXPLAIN.
Collusion
Please note that your source code for this assignment will be run through a plagiarism detection
system designed for code that compares all assignments and highlights identical or very similar
submissions. If you are found to have colluded with other students, you will be submitted for
academic integrity.
Special Consideration
Please refer to the Special Consideration section of Policy.
https://policies.scu.edu.au/document/view-current.php?id=140
Late Submissions & Penalties
Please refer to the Late Submission & Penalties section of Policy.
https://policies.scu.edu.au/view.current.php?id=00255
Grades & Feedback
Assessments that have been submitted by the due date will receive an SCU grade. Grades and
feedback will be posted to the ‘Grades and Feedback’ section on the Blackboard unit site. Please
allow 7 days for marks to be posted.
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Assessment Brief
Assessment Rubric
Marking Criteria and %
allocation
High Distinction
(85–100%)
Distinction
(75–84%)
Credit
(65–74%)
Pass
(50–64%)
Fail
(0–49%)
Analyse and convert
data types
(ULO2 & 4)
20%
String data from CSV files is
analysed and converted
flawlessly, demonstrating an
advanced understanding of
data type conversion.
Potential issues are proactively
identified and resolved,
ensuring optimal program
efficiency and data integrity.
String data from CSV files is
accurately converted,
showcasing a welldeveloped understanding of
data type conversion.
Potential issues are
effectively addressed,
maintaining high program
efficiency and data integrity.
String data from CSV files is
consistently converted
accurately, reflecting a solid
grasp of data type
conversion principles.
Common issues are
identified and corrected,
supporting smooth program
operation.
String data from CSV files is
adequately converted with
minor errors, indicating a
basic understanding of data
type conversion.
Fundamental issues are
addressed to sustain
program functionality.
Struggles with accurate
conversion of string data
from CSV files, revealing
limited understanding and
compromising data integrity
and program performance.
Significant conversion issues
remain unresolved.
Design and implement
class concepts
(ULO3)
40%
Demonstrates exemplary skills
in designing and implementing
sophisticated class structures,
demonstrating deep insights
into object-oriented concepts,
and creating highly efficient,
well-encapsulated code.
Demonstrates strong
proficiency in class design
and implementation,
showing a thorough
understanding of objectoriented principles and
producing efficient, wellstructured code.
Demonstrates competent
ability in designing and
implementing classes,
effectively applying objectoriented principles to
produce functional and
organised code.
Adequately designs and
implements class structures,
demonstrating basic
understanding of objectoriented concepts with code
that meets essential
requirements.
Struggles with designing and
implementing class
structures, showing limited
understanding of objectoriented concepts, resulting
in ineffective and
disorganised code.
Apply error handling
and validation
strategies
(ULO2)
20%
Applies comprehensive error
handling and validation
strategies, ensuring robust
program resilience and
flawless handling of all
potential exceptions.
Applies effective error
handling and validation
strategies, significantly
enhancing program stability
and effectively managing
most exceptions.
Adequately applies error
handling and validation
strategies, maintaining
program stability and
addressing common
exceptions appropriately.
Applies basic error handling
and validation strategies,
covering essential
exceptions and maintaining
minimal program continuity.
Fails to apply effective error
handling and validation
strategies, resulting in
program instability and
inadequate management of
exceptions.
Evaluate and utilise
collections
(ULO4)
20%
Evaluates and utilises
advanced collection types,
demonstrating strategic
choices that optimise data
management and program
efficiency.
Evaluates and uses
appropriate collection types,
significantly enhancing data
handling and program
functionality.
Evaluates and employs
suitable collection types,
showing a good
understanding of their
impact on data
management and program
performance.
Adequately selects and
utilises basic collection
types, meeting fundamental
requirements for data
management and program
operation.
Struggles to choose or use
collections effectively,
resulting in suboptimal data
management and program
performance.
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Assessment Brief
Description of SCU Grades
High Distinction:
The student’s performance, in addition to satisfying all of the basic learning requirements, demonstrates distinctive insight and ability in researching, analysing and
applying relevant skills and concepts, and shows exceptional ability to synthesise, integrate and evaluate knowledge. The student’s performance could be described as
outstanding in relation to the learning requirements specified.
Distinction:
The student’s performance, in addition to satisfying all of the basic learning requirements, demonstrates distinctive insight and ability in researching, analysing and
applying relevant skills and concepts, and shows a well-developed ability to synthesise, integrate and evaluate knowledge. The student’s performance could be described
as distinguished in relation to the learning requirements specified.
Credit:
The student’s performance, in addition to satisfying all of the basic learning requirements specified, demonstrates insight and ability in researching, analysing and applying
relevant skills and concepts. The student’s performance could be described as competent in relation to the learning requirements specified.
Pass:
The student’s performance satisfies all of the basic learning requirements specified and provides a sound basis for proceeding to higher-level studies in the subject area.
The student’s performance could be described as satisfactory in relation to the learning requirements specified.
Fail:
The student’s performance fails to satisfy the learning requirements specified.

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