ASSESSMENT BRIEF 3: Calculations
Summary
| Title | Calculations |
| Due Date | Monday the 3rd of October at 11:59 (NSW) AEST – End of Week 5 |
| Length | 1000-word (does not include equations, charts, graphs or tables) |
| Weighting | 35% |
| Submission | WORD document submitted to Turnitin |
| Unit Learning Outcomes | ULO 3: apply basic statistical analysis to engineering applications. |
Task Description
This is an individual assessment where you will complete a series of statistical calculations, analyses and interpretations of the data and its meaning based on an assigned case study of a Rube Goldberg Machine. You will be assigned a specific scenario based on their last names. Folders for students with an alpha range of last names will be available at the beginning of week 5. The task will resemble the scenario that we will be using in class to calculate statistical operations, perform data analyse and interpret the data. The rubric will also be integrated into the evaluation of written examples to provide an understanding of how your work will be marked. You will be able to use this as an exemplar for completing your assessment 3.
What will you be doing that covers the Unit Learning Outcome: apply basic statistical analysis to engineering applications.
Week 2:
- The engineering application used for this ULO is the Rube Goldberg machine and we will be evaluating the effectiveness of the machine and the risk that each stage of the machine contributes to the overall success of the machine.
- You will clean the data (data calibration), look at the data presented and determine the chance of success and failure for each stage Week 3:
- You will use the sigma process to evaluate the risk level for the machine – this is used by engineers in the industry • You will analyse the risk and look at how you can make modifications to the machine to improve the success rate Week 4:
- You will use graphs and charts to display the information so it is more easily understood
- You will look at the frequencies of the events and explain what the various charts and graphs tell you about the data
Week 5:
- You will analyse the times for the stages and determine the likelihood of meeting the time requirements for each stage and the overall time requirements for the whole machine
- You will analyse the data and use descriptive statistics to discuss the time values for the stages and the machine as a whole
Rationale
Through the use of a Rube Goldberg Scenario, you will gain the ability to recognise specific types of statistical operations and when and why they were used. You will be able to evaluate the data and recognise statistical trends and be able to evaluate where there is the highest chance of risk (failure) occurring. These skills and knowledge align with ULO 3: apply basic statistical analysis to engineering applications, as you will be evaluating a working machine that has gone through testing with many successes and failures along the way. In any construction work environment being able to predict the risk of various parts of the project is an important part of site safety and the integrity of the construction project. In each of the modules, there are important concepts of statistics that are presented that we will then practice on the provided case study during each of the weekly tutorials (Weeks 2-5). It is important that you review the online content and the case scenario section for that week prior to attending the tutorials so that you are able to get the most out of the calculations each week.
Task Instructions
- For this task, you will first be provided with guided practices on similar statistical calculations, data analysis and interpretation based on a case study that we will work through in Weeks 2-5 (see list in Task Description).
- The work in class will directly reflect what you will need to complete on your Calculation Assessment 3.
- We will also review the marking rubric each week to evaluate the level of examples provided so you are clear on what level of work you need to do to reach a Pass or higher.
- I will also show you how to complete any relevant calculations in Excel when required and how to use Word for showing equations and calculation workings
- For all equations that are being used for the first time, you must show all workings with an explanation of what you are doing – pretend you are teaching someone else what to do- you can then just show the numbers in the equation and the answer.
- Make sure to check answers for the correct number of significant figures as indicated in the instructions for the case study.
- For any areas where you need to research an industry standard, you will need to have at least two reference sources that you have used.
Resources
You will use the content within the online modules weeks 2-5 and the case study exemplar to complete this assessment
Referencing style
Please include a reference list and use APA7 as a guide for your in-text citations and reference list. You will need to find a few journal articles and/or government frameworks to support some of the decisions you will make about what an appropriate risk level is for the Rube Goldberg Scenario.
Submission
You will submit your completed Calculations in a word document to Turnitin. A submission link is provided in the Assessment tasks and Submission area in Blackboard in the 3. Calculations folder. You will need to submit your assessment by 11:59 pm (NSW) AEST Monday the 3rd of October
Grades & Feedback
Your grades for this assessment will be released on Monday 10th of October at 11:59 pm. You will be able to access your grades through Grades and Feedback in Blackboard.
Assessment Criteria & Rubrics
A marking Rubric for your assessment has been included with descriptors for each of the levels of achievement. We will use this rubric to evaluate the case study workings each week so you are able to judge the quality of your own work for this task.
Assessment Rubric
Note: The number of criteria used will vary depending on the complexity of the assessment task. Typically, 4-5 criteria are used.
| Marking Criteria and % allocation | High Distinction (No Errors) 100% | High Distinction 99 -85% | Distinction 84-75% | Credit 74-65% | Pass 64-50% | High Fail 49-40% | Low Fail 39-20% | Absent Fail 0% |
| Criterion 1 (25%) Calculations | All calculations completed with explanations, correct decimal places and | Almost all calculations are completed with explanations, correct decimal | Most calculations are completed with explanations, correct decimal places and | A majority of calculations are completed with explanations, most answers have | Some calculations were completed with explanations, some with | Calculation s have been completed but are not | An attempt has been made but the | No work submitted |
| rounding, no errors in workings, final answer is correct | places and rounding, a couple minor errors in workings, final answer is correct | rounding, a few minor errors in workings, final answer is correct | correct decimal places and rounding, some errors in workings, with one critical error, final answer is correct | correct decimal places and rounding, some errors in workings, with two critical errors, final answer is not correct | correct or relevant | calculatio ns are difficult to interpret | ||
| Criterion 2 (25%) Data Analysis | All data analysis is complete, creating useful charts and graphs with all labelling correct, arrangement of data is meaningful and useful information can be drawn from the analysis to help in decision making | Almost all data analysis is complete, most of the charts and graphs are useful and have all labelling correct, arrangement of data is mostly meaningful and useful information can be drawn from the analysis to help in decision making | Most data analysis is complete, many of the charts and graphs are useful and have most of the labelling correct, arrangement of data is mostly meaningful and useful information can be drawn from the analysis to help in decision making | A majority of the data analysis is complete, some of the charts and graphs are useful and have most of the labelling correct, arrangement of data is somewhat meaningful and some useful information can be drawn from the analysis to help in decision making | Some data analysis is complete, a few charts and graphs are useful and have some of the labelling correct, arrangement of data provides limited useful information that can be used in making any helpful decisions | Data analyses are not correct or relevant | An attempt has been made but the analysis is difficult to decipher | No work submitted |
| Criterion 3 (25%) Data Interpretation | The interpretation of the data analysis is accurate, meaningful and provides useful conclusions/ solutions about the | The interpretation of the data analysis is mostly accurate, meaningful and some useful conclusions/ solutions about the | The interpretation of the data analysis is somewhat accurate and meaningful with some useful conclusions/ | The interpretation of the data analysis is somewhat accurate and meaningful with some useful conclusions about | The interpretation of the data analysis has little accurate and meaningful information with a few conclusions | Data interpretati ons are not correct or relevant | An attempt has been made but the interpreta tion is | No work submitted |
| risk within the project and possible mitigations are suggested | risk within the project and possible mitigations are suggested | solutions about the risk within the project and possible mitigations are suggested | the risk within the project with a few changes suggested but not linked to risk improvement | about the risk within the project | difficult to follow | |||
| Criterion 4 (25%) Written Explanations | The explanations and justification are backed up by several journals/ industry frameworks and are linked clearly to the statistical analysis and interpretation of the data demonstrating a clear understanding of why various statistical tools are used when assessing project risk | The explanations and justification are backed up by some journals/ industry frameworks and are mostly linked clearly to the statistical analysis and interpretation of the data demonstrating a clear understanding of why various statistical tools are used when assessing project risk | The explanations and justification are backed up by a few journals/ industry frameworks and are mostly linked to the statistical analysis and interpretation of the data demonstrating an understanding of why various statistical tools are used when assessing project risk | The explanations and justification are somewhat linked to the statistical analysis and interpretation of the data, but do not demonstrate a clear understanding of why various statistical tools are used when assessing project risk | The explanations and justification are not clearly linked to the statistical analysis and interpretation of the data, but do not demonstrate a clear understanding of why various statistical tools are used when assessing project risk | Written explanation is not correct or relevant | An attempt has been made but the explanati on is difficult to follow | No work submitted |
Description of Grades
High Distinction+:
Achieves all the criteria for a high distinction to an exemplary standard, without any errors.
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.
Absent Fail:
Not attempted.
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