This document provides you with general information about the requirements for this assessment task. A generic Criterion Reference Assessment (CRA) rubric that markers will use to help grade the assessment task is included.

Task Overview

Assessment name:Assignment 1: Data Visualisation
Task description:Here you will develop a complete computer program that draws a visualization of given data sets. The data sets are generated by a template program which you must extend to complete your solution. To do so you will need to design appropriate symbols for the visualisation and develop reliable program code capable of processing any given data set and accurately drawing a complete corresponding image.
Learning outcomes measured:ULO 1: Explain the commonly used features in computer languages needed to build IT systems. ULO 2: Use selected computer languages effectively to de- develop software to address user needs. ULO 3: Apply appropriate processes and creative thinking to develop solutions to small IT challenges. ULO 5: Interpret client requirements to design and build user-centred IT systems.
Due Date:Part A, static images: Friday, end of Week 4, at 23:59 Part B, complete solution: Friday, end of Week 7, at 23:59
Estimated time to com- plete task:Approximately 30 hours
Weighting:Part A: 7% of final grade Part B: 23% of grade
Individual or Group:Individual
Authentic Assessment:  Yes
Formative/Summative:  Summative
How will I be assessed:  Grading scale using a supplied rubric

Task details

What you need to do:  Part A: Read the Criterion Reference Assessment Rubric.Study the “client’s requirements” for the assignment (on Blackboard).Download the relevant program template (from Black- board) and familiarise yourself with the drawing canvas it creates.Design the symbols to be used for your visualisation and create program code to draw them.Submit Part A (to Blackboard) by the due date above.   Part B: Read the Criterion Reference Assessment Rubric.Complete the program code as per the client’s require- ments to draw appropriate visualisations of any possible data set that can be generated by the client’s data func- tion.Upload drafts of your solution (to Blackboard) as you make progress.Be prepared to respond to last-minute changes in the re- quirements made by the “client”.Submit Part B (to Blackboard) by the due date above.
Presentation requirements:See the IFB104 Code Presentation Guide (on Black- board) for tips on code layout.See the assignment’s requirements (on Blackboard) for an example of the standard expected of the program’s output.Your program must run in a standard Python 3 environ- ment with no extensions. You may not use any Python modules that need to be downloaded and installed sep- arately, such as “Beautiful Soup” or “Pillow”. Only mod- ules that are part of a standard Python 3 installation may be used.
Resources needed to complete task:  The following items will be made available on Blackboard: Program template codeThe “client’s” requirements for the program

Submission Information

What you need to submit:Upload your completed version of the program template, as a sin- gle program file, after checking the following points: You have added your name and student number to the state- ment of authorship at the top.Your program code runs without “crashing” (generating un- handled exceptions), even if your solution is incomplete. You will receive partial marks for incomplete solutions.Your program code runs in a standard Python 3 environment and does not rely on any separately-installed extension mod- ules.
How to submit:A link will be made available on the IFB104 Blackboard site under Assessment Task 1 for uploading your solution file. Click on the Submit Assessment Task 1A or Task 1B link.Drag your Assessment file into the Attach Files box or click on the Browse My Computer button to locate your file. Add any comments you wish to make to the teaching staff in the Comments textbox.Click the Submit button.   Notes: You can submit as many drafts of your solution as you like. You are strongly encouraged to submit several draft solutions before the final submission date as insurance against com- puter or network problems near the deadline.If you are unsure whether or not you have successfully up- loaded your file, upload it again!If you encounter problems uploading your files to Blackboard contact HiQ’s Technology Services for advice (;; 3138 2000).Do not email assignments to the teaching staff. Teaching staff cannot submit assignments to Blackboard on a student’s be- half, and Python files are blocked by Microsoft Outlook and will not be received.
  What feedback will I receive?Under normal circumstances, you will receive marks for each cri- terion via a Blackboard rubric within 16 days of submission. Click on your mark in MyGrades to see the rubric. Usually the reason for each choice of mark is self-evident, but sometimes the
 marker will add a few words of explanation. You should use this feedback to strengthen your performance in the next assessment item.
Moderation:All staff who assess your work are instructed on the expected quality of solutions and the technical marking criteria before grading begins.
Academic Integrity
As a student of the QUT academic community, you are asked to work to uphold the principles of academic integrity during your course of study. QUT sets expectations and responsibilities of stu- dents, more specifically it states that students “adopt an ethical approach to academic work and assessment in accordance with this policy and the Student Code of Conduct. E/2.1 (MOPP C/5.3 Academic Integrity). At university, students are expected to demonstrate their own understanding and thinking using the ideas provided by ‘others’ to support and inform their work, always making due acknowledgement to the source. While we encourage peer learning, it is not appropriate to share assignments with other students unless your assessment piece has been stated as being a group assignment. If you do share your assignment with another student, and they copy part of or all of your assignment for their submission, this is considered collusion and you may also be reported for academic miscon- duct. If you are unsure and need further information you can find this at: “>
CriteriaPerfectHigh Distinc- tionDistinctionCreditPassMarginal FailFailLow FailNo Evidence
Static parts of visualisation drawn cor- rectly   Weight (of fi- nal grade): 7%All required images and text are well-de- signed, suffi- ciently de- tailed and distinct, and drawn cor- rectly. (7%)Excellent but a minor problem with one of the images, e.g., a mis- placed line, or a small part of the text, e.g., a spelling er- ror. (6.5%)Images are well-de- signed and sufficiently detailed, but a slight prob- lem with one of the draw- ings or a part of the text. (5.6%)Generally good but some small elements are wrong or missing, or the images are not en- tirely appro- priate and recognisa- ble. (4.9%)Good but noticeable weaknesses with the im- ages and/or text, e.g., unclear cap- tions, poor layout, or in- appropriate images. (4%)Significant parts of the required im- ages and text are missing, sub- standard, too similar or too trivial. (3.2%)Very little done, e.g., only one or two trivial images drawn and/or im- ages differ only in a triv- ial way (e.g., colour). (2.3%)A few shapes or lines drawn, or some text written, but the main re- quirements are not satis- fied signifi- cantly. (0.9%)No evidence of address- ing this crite- rion. (0%)
Data is pro- cessed cor- rectly   Weight: 10%It is made evident that all possible data sets are processed correctly and reliably. (10%)All data sets are pro- cessed cor- rectly, apart from an ex- treme case, e.g., the empty set. (9.3%)All data sets are pro- cessed cor- rectly, apart from a few special cases. (8%)The program works with almost all data sets but not some having a par- ticular char- acteristic. (7%)The program works cor- rectly with the majority of data sets but not a sig- nificant mi- nority. (5.8%)The program cannot cor- rectly pro- cess most data sets or works only with se- lected, fixed data sets. (4.5%)The program is hardwired to work with only a few fixed data sets. (3.3%)Program code cannot run to com- pletion or is hardwired to produce a fixed result only. (1.3%)No evidence of address- ing this crite- rion. (0%)
Dynamic parts of visualisa- tion drawn correctly   Weight: 8%The visual in- terpretation of all possi- ble data sets is accurate, well-de-Excellent apart from some minor defect in the drawing, e.g., one ele- ment slightly out of place.All data sets are visual- ised accu- rately and completely, but there is some minor flaw in a fewThe visuali- sation is drawn accu- rately in al- most every case, but in a few thereThe visuali- sation is drawn accu- rately in the majority of cases, but in some situa- tions thereSignificant problems with the vis- ualisation in many cases, e.g., images missing, in- correct orSome small parts of the necessary visualisation are drawn but the re- sult is largely incomplete,Program code cannot run to com- pletion or draws just a few fixed shapes or lines.No evidence of address- ing this crite- rion. (0%)



CriteriaPerfectHigh Distinc- tionDistinctionCreditPassMarginal FailFailLow FailNo Evidence
 signed, relia- ble and com- plete. (8%)(7.4%)cases, e.g., stray or missing lines (6.4%)are small er- rors. (5.6%)are noticea- ble errors. (4.6%)drawn in the wrong loca- tions. (3.6%)inaccurate and/or inco- herent. (2.6%)(1%) 
Code concise- ness, clarity and presenta- tion   Weight: 5%All code is concise, neatly laid out, uses meaningful identifiers, and is com- mented helpfully. (5%)Excellent but with a minor flaw such as a misleading comment, an obscure choice of identifier, etc. (4.6%)One or two substandard elements such as unin- formative identifiers (“i”, “j”, “n”, etc) or inad- equately commented code blocks. (4%)Generally good but a few unin- formative identifiers, inadequately commented blocks, or unneces- sarily dupli- cated code segments. (3.5%)Good but some signifi- cant seg- ments are hard to un- derstand due to poor commenting or identifier choices, or significant amounts of unnecessary duplication. (2.9%)Insufficient commenting in many parts, many unhelpful identifiers, and/or large amounts of unneces- sarily dupli- cated code. (2.3%)Little code to assess and/or barely any commenting and/or large amounts of confusing, unneces- sarily dupli- cated code. (1.6%)Very little code to as- sess or code is inade- quately pre- sented throughout. (0.6%)No evidence of address- ing this crite- rion. (0%)
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