Part B: Course Detail
Teaching Period: Term2 2025
Course Code: GEOM5192C
Course Title: Digitally enhance and process image data
School: 530T Built Environment and Sustainability
Campus: City Campus
Program: C4417 - Certificate IV in Surveying and Spatial Information Services
Course Contact: Matthew Sweeney
Course Contact Phone: +61 3 9925 4105
Course Contact Email: matthew.sweeney@rmit.edu.au
Name and Contact Details of All Other Relevant Staff
Dr Indira Wittamperuma
Course Coordinator
Building, Surveying & Land Management
Built Environment & Sustainability
RMIT University
College of Vocational Education
Melbourne, Victoria, Australia
Email: indira.wittamperuma@rmit.edu.au
Phone: +61399254176
Nominal Hours: 40
Regardless of the mode of delivery, represent a guide to the relative teaching time and student effort required to successfully achieve a particular competency/module. This may include not only scheduled classes or workplace visits but also the amount of effort required to undertake, evaluate and complete all assessment requirements, including any non-classroom activities.
Pre-requisites and Co-requisites
None
Course Description
In this course you will develop the skills and knowledge required to extract information from original digital data before processing using remote sensing/geographic information system (GIS) software to manipulate, enhance, classify and process data. The image data can be multispectral or from aircraft, unmanned aerial vehicles or satellites.
This course is suitable for entry-level technicians who use a broad range of cognitive, technical and communication skills to select and apply a range of methods, tools, materials and information to complete routine and non-routine activities and provide and transmit solutions to a variety of predictable and sometimes unpredictable problems.
National Codes, Titles, Elements and Performance Criteria
National Element Code & Title: |
CPPSSI4026 Digitally enhance and process image data |
Element: |
1. Identify image data |
Performance Criteria: |
1.1 Analyse project specifications to determine study area and image data requirements for task in consultation with appropriate persons. 1.2 Identify potential sources of image data to meet task requirements. 1.3 Identify constraints of different types and formats of image data in relation to task requirements. |
Element: |
2. Process and analyse image data |
Performance Criteria: |
2.1 Access image data and open in software. 2.2 Interpret graphical and technical information, including landscape features within digital images. 2.3 Enhance visual display to analyse image features. 2.4 Classify image data and interpret results to achieve task requirements. 2.5 Check results for accuracy and resolve identified problems. |
Element: |
3. Present and document results |
Performance Criteria: |
3.1 Present results of analysis in map, table or graph form. 3.2 Document results of analysis to meet task and organisational requirements. 3.3 Comply with health and safety requirements when using screen-based equipment and completing records and documentation. 3.4 Comply with legislative and organisational requirements relating to data privacy, copyright and licensing. |
Learning Outcomes
On successful completion of this course you will have developed and applied the skills and knowledge required to demonstrate competency in the above elements.
Details of Learning Activities
Learning Activities:
- Identify image data sources that meet task needs.
- Understand constraints of different image types and formats for the task.
- Access and open image data in software.
- Interpret information from images, including landscape features.
- Enhance visuals to analyse image features.
- Classify image data using both unsupervised and supervised methods and interpret the results.
- Check for accuracy and solve any issues.
- Present analysis results in maps, tables, or graphs.
- Document findings according to task and organisation requirements.
The total number of scheduled hours for teaching, learning, and assessment in this course encompasses all planned activities, including face-to-face classes, lectures, workshops, seminars, workplace visits, online learning, and other structured learning formats. It also includes the time required to undertake, assess, and complete all assessment tasks, observe work performance, and participate in discussions.
Teaching Schedule
Session/Date |
Theme |
Assessments |
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Session One 14-20 July |
Lesson Title: Introduction to the subject and Assessment requirements Description: During this session, you will be provided with an overview of the assessment. The teacher will guide you through the assessment documentation, explaining the requirements and expectations. In addition to the assessment materials, you will also be introduced to various supplementary training resources. These resources may include platforms like LinkedIn Training, where they can access additional training modules and materials to enhance their knowledge and skills beyond the scope of the assessment. |
Assessment 1 Released |
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Session Two 21-27 July |
Lesson Title: Introduction to basic properties and characteristics of imagery Description: During this session, you will be introduced to the fundamental properties and characteristics of imagery. You will gain knowledge about various types and formats of imagery, as well as potential sources for acquiring such imagery. Additionally, you will learn about the constraints related to different imagery types and formats, including spatial reference aspects such as projections, datum’s, and coordinate systems. By understanding these concepts, you will develop a solid foundation in working with and interpreting different types of imagery effectively. |
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Session Three 28 July - 3 August |
Lesson Title: Aerial Calculations Description: In this session, you will receive an introduction to aerial calculations, focusing on determining the sizes of objects and the scale using relevant formulas. You will learn about the necessary calculations involved in determining object sizes from aerial imagery and how to apply appropriate formulas to obtain accurate measurements. Additionally, you will understand the concept of scale and its significance in aerial calculations. By successfully engaging with these concepts and applying the provided formulas, you will develop the skills to accurately determine object sizes and scale from aerial imagery. This understanding will enable you to effectively analyze aerial data and make informed decisions based on accurate measurements. |
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Session Four 4-10 August |
Lesson Title: Introduction to spatial references Description: In this session, you will receive an introduction to the key features of spatial references, which include projections, datums, and coordinate systems. You will learn about the importance of spatial references in accurately representing and locating geographic data. The session will cover different types of map projections and their characteristics, datums and their role in defining the Earth's shape and orientation, and coordinate systems used to specify positions on the Earth's surface. By understanding these key features of spatial references, you will be equipped with the knowledge to work with geospatial data effectively. This understanding will enable you to properly interpret and analyze geographic information, ensuring accurate spatial analysis and decision-making. |
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Session Five 11-17 August |
Lesson Title: Introduction to Remote Sensing Description: In this session, the student will be introduced to the sources of satellite images, the spectral properties of images, and the key features of spectral signatures. The student will explore various sources from which satellite images can be obtained and understand the significance of these sources in acquiring relevant and high-quality imagery. Additionally, the student will learn about the spectral properties of images, which involve the different wavelengths and bands captured by remote sensing instruments. This knowledge will enable the student to interpret and analyze imagery based on the unique characteristics of its spectral signature. By the end of this session, the student will have a solid understanding of the sources of satellite images, the spectral properties that influence image composition, and the key features found in spectral signatures. This knowledge will serve as a foundation for effectively working with satellite imagery and extracting valuable information from it |
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Session Six 18-24 August |
Lesson Title: Unsupervised classification techniques Description: In this session, the student will be introduced to image classification techniques used in remote sensing, with a focus on unsupervised classification algorithms. The student will explore the concept of image classification and its importance in analyzing remote sensing data. Specifically, the student will learn about unsupervised classification algorithms, which allow for the automatic grouping of pixels in an image based on their spectral properties. These algorithms help identify patterns and clusters within the data without the need for predefined training samples. Furthermore, the student will also delve into common band combinations used in remote sensing. Band combinations involve selecting specific spectral bands from an image to enhance certain features or highlight specific land cover types. Understanding these band combinations will enable the student to optimize the visual interpretation and analysis of remote sensing data. By the end of this session, you will have a solid understanding of image classification techniques, particularly unsupervised algorithms, and the application of band combinations in remote sensing. This knowledge will equip you with valuable skills for effectively analyzing and interpreting remote sensing imagery. |
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Session Seven 25-31 August |
Lesson Title: Conduct unsupervised classification Description: In this session, the student will apply image classification techniques to categories remote-sensed imagery using unsupervised classification methods. By the conclusion of this session, participants will have gained hands-on experience, ensuring a solid understanding of the application of these techniques and the ability to implement them proficiently in practical situations. |
Commencing of Assessment 1
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Mid-semester break 1-7 September |
The Mid-semester break is a scheduled break in the semester. No teaching or assessment will occur during this time. Also, your Trainer/Assessor won't be available during this time. If you need to contact them, please email them via your student email account, and they will respond once they return from the break. Semester 2 census date 01 Sep 2025 |
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Session Eight 8-14 September |
Lesson Title:
Assessment 1: Meeting and clarifying requirements Description: During this session, the student will be assigned an assessment task that involves participating in a meeting with the client. The purpose of the meeting is to engage in extensive discussions and document all project requirements. Furthermore, the student will be required to respond to a series of knowledge questions directly tied to the assessment task. The main objective of these discussions and documentation is to establish a clear and precise understanding of the project scope. This meticulous process will facilitate effective planning and execution of the assessment task, ultimately leading to a successful outcome. By successfully completing these tasks, you will demonstrate your proficiency in understanding and managing project requirements, as well as their ability to communicate effectively with clients. |
Continuation of Assessment 1 |
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Session Nine 15-21 September |
Lesson Title: Assessment 1 - Perform Unsupervised Classification and submission. Description: In this session, students will undertake an assessment task where they'll utilise GIS software to perform unsupervised classification. The task involves interpreting both graphical and technical information to classify landscape features within digital images and analyse the obtained results. Successfully completing this assessment not only demonstrates proficiency in utilizing GIS software for unsupervised classification and interpreting complex information but also showcases the ability to articulate knowledge through thoughtful responses to the associated set of knowledge questions. It serves as a comprehensive evaluation of both practical skills and theoretical understanding in the realm of image classification and GIS applications. |
Assessment 1 - Finalisation Assessment 1 Due: 21 Sep 2025
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| Session Ten 22-28 September |
Lesson Title: Introduction to the subject and Assessment 2 requirements. Description: During this session, the student will be provided with an overview of the assessment. The teacher will guide the students through the assessment documentation, explaining the requirements and expectations. In addition to the assessment materials, students will also be introduced to various supplementary training resources. These resources may include platforms like LinkedIn Training, where they can access additional training modules and materials to enhance their knowledge and skills beyond the scope of the assessment. AFL Grand Final public holiday (Friday) 26 Sep 2025 |
Assessment 2 Released |
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Session Eleven 29 September - 5 October |
Lesson Title: Introduction to Remote Sensing (Recap) Description: In this session, the student will receive additional training building upon the previous session where we covered essential topics. This included an introduction to the sources of satellite images, understanding the spectral properties of images, and identifying key features within spectral signatures. We explored various sources for obtaining satellite images in this session, highlighting their significance in acquiring relevant and high-quality imagery. Additionally, in this session, students gained insights into the spectral properties of images, involving different wavelengths and bands captured by remote sensing instruments. This knowledge empowers students to interpret and analyze imagery based on the unique characteristics present in its spectral signature. Moving forward within this session and beyond, we will continue to build on this foundation, applying our understanding to practical scenarios and enhancing students' proficiency in remote sensing applications. |
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Session: Twelve 6-12 October |
Lesson Title: Supervised classification techniques Description: In this session, you will be introduced to image classification techniques used in remote sensing, with a focus on supervised classification algorithms. You will explore the concept of image classification and its significance in analyzing remote sensing data. Specifically, you will learn about supervised classification algorithms, which involve training the algorithm using labeled samples to classify pixels in an image. These algorithms rely on the knowledge and expertise of the analyst to define training areas and assign class labels. By leveraging spectral properties and other relevant information, supervised classification algorithms enable the identification and mapping of different land cover classes. Furthermore, you will delve into common band combinations used in remote sensing. Band combinations involve selecting specific spectral bands from an image to enhance certain features or highlight particular land cover types. Understanding these band combinations will enable you to optimize the visual interpretation and analysis of remote sensing data. By the end of this session, you will have a solid understanding of image classification techniques, particularly supervised algorithms, and the application of band combinations in remote sensing. This knowledge will equip you with valuable skills for effectively analyzing and interpreting remote sensing imagery. |
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Session Thirteen 13-19 October |
Lesson Title: Perform supervised classification Description: In this session, the student will apply image classification techniques to categorise remote-sensed imagery using supervised classification methods. By the conclusion of this session, participants will have gained hands-on experience, ensuring a solid understanding of the application of these techniques and the ability to implement them proficiently in practical situations. |
Commencing of Assessment 2
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Session Fourteen 20-26 October |
Lesson Title: Perform supervised classification (Continued) Description: In this session, the student will continue to apply image classification techniques to categorise remote-sensed imagery using supervised classification methods. By the conclusion of this session, the student will have honed their skills in applying image classification techniques for categorizing remote-sensed imagery through the use of supervised classification methods. Through practical application and hands-on experience, the session aims to ensure that participants not only grasp the theoretical underpinnings but also develop a proficient ability to employ supervised classification algorithms effectively. |
Continuation of Assessment 2
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Session Fifteen 27 October- 2 November |
Lesson Title: Assessment 2: Meeting and clarifying requirements Description: During this session, you will be assigned an assessment task that involves participating in a meeting with the client. The purpose of the meeting is to engage in extensive discussions and document all project requirements, while also addressing a series of questions. The main objective of these discussions and documentation is to establish a clear and precise understanding of the project scope. This meticulous process will facilitate effective planning and execution of the assessment task, ultimately leading to a successful outcome. By successfully completing these tasks, you will demonstrate your proficiency in understanding and managing project requirements, as well as their ability to communicate effectively with clients. |
Assessment 2 - Finalisation
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Session Sixteen 3-9 November |
Lesson Title: Assessment 2 - Perform Supervised Classification and submission. Description: In this session, students will undertake an assessment task where they'll utilise GIS software to perform supervised classification. This will involve downloading remote sensed imagery, capturing training samples for each class, and implementing pre-processing techniques to enhance overall image quality. Subsequently, students will utilize a classification algorithm to extract essential spectral, textural, and spatial features. Following the classification phase, post-processing steps will be employed to refine the results and enhance accuracy. The comprehensive nature of the task involves interpreting both graphical and technical information to effectively classify landscape features within digital images. Successfully completing this assessment not only demonstrates the proficiency of students in utilising GIS software for supervised classification but also showcases their ability to navigate the intricacies of image analysis. By downloading remote sensed imagery and meticulously capturing training samples for each class, participants lay the foundation for accurate classification. The implementation of pre-processing techniques further refines the image quality, setting the stage for the application of a classification algorithm. As students adeptly extract spectral, textural, and spatial features, the subsequent post-processing steps contribute to refining results and elevating overall accuracy. The holistic approach of interpreting both graphical and technical information underscores the students' capacity to not only execute the classification process but also comprehend and communicate the nuances of landscape features within digital images Melbourne Cup Day public holiday (Tuesday) 04 Nov 2025 |
Assessment 2 Due: 09 Nov 2025 |
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Session Seventeen 10-16 November |
Revision and feedback on work completed |
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Session Eighteen 17-23 November |
Assessment marking and finalising results |
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Official Results Release 01 Dec |
Important: It is your responsibility to check your results on this date. Your official results for this course will be released on this date. Your teacher will not inform you of your final result. It will only be available via My Student Record on RMIT's website. It is not your Trainer/Assessors responsibility to let you know your final result. Your teacher will not be available to comment on your assessment or final results from 23 Nov - 07 Dec 2025. After this date, you can contact them to talk about your final result if you need it. |
Learning Resources
Prescribed Texts
References
Other Resources
Students will be able to access course information and learning materials through the Learning Hub and may be provided with additional materials in class. Lists of relevant reference books, resources in the library and accessible Internet sites will be provided where possible. You will also use equipment and software packages in the laboratory for the project work. During the course, you will be directed to websites to enhance your knowledge and understanding of difficult concepts
Check the Library Subject Guides: http://rmit.libguides.com/geospatial
Accessed Via the RMIT Library Link:
Overview of Assessment
Assessment for this course is ongoing throughout the semester. Your knowledge and understanding of course content is assessed through participation in class exercises, oral presentations and through the application of learned skills and insights to your written tasks. Full assessment briefs will be provided and can be found on CANVAS.
Assessment Tasks
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Assessment Name |
Description |
Release Date |
Due Date |
| Assessment 1: Built Environment Focus | This assessment requires you to capture and classify built environment feature using remote sensing imagery classification techniques and classification algorithms for the topographic mapping program | 14 Jul 2025 | 21 Sep 2025 |
| Assessment 2: Natural environment focus | This assessment requires you to capture and classify natural environment feature using remote sensing imagery classification techniques and classification algorithms for the topographic mapping program | 22 Sep 2025 | 09 Nov 2025 |
Assessment Matrix
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Elements and Performance Criteria | |||
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Elements describe the essential outcomes. |
Performance criteria describe what needs to be done to demonstrate achievement of the element. |
AT1: Built Environment Focus |
AT2: Natural environment focus |
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1C, 2A |
1C, 2A |
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2A |
2A |
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2A |
2A |
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3B |
3B |
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3B |
3B |
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3B |
3B |
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3B |
3B |
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3B |
3B |
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3B, 5B |
3B, 4B |
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3B, 5C |
3B, 4C |
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5B |
4A |
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5A |
4A |
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Foundation Skills – This section is only completed when foundation are explicitly stated in the unit of competency. In most Training Packages the foundation skills are integrated into the unit of competency and this is clearly stated. |
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Foundation skills essential to performance are explicit in the performance criteria of this unit of competency. |
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Performance Evidence | ||
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To demonstrate competency a candidate must meet the elements and performance criteria of this unit by using remote sensing or geographic information system (GIS) applications to digitally enhance, process and classify image data for two projects: |
AT1: Built Environment Focus |
AT2: Natural Environment Focus |
and
Images may include: | ||
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3B |
3B |
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3B |
3B |
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3B |
3B |
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3B |
3B |
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Knowledge Evidence | ||
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To be competent in this unit a candidate must demonstrate knowledge of: |
AT1: Built Environment Focus |
AT2: Natural Environment Focus |
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2A, 4A |
2A, 3A |
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3A, 4B, 4C |
3A |
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3B, 4D |
3B, 4C |
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3A. 4G |
3A, 3B |
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3A, 3B |
3A, 3B |
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1F, 4J, 4K, 5A |
4A, 4D |
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Assessment conditions |
Describe how assessments meet the assessment conditions |
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Assessors must meet the requirements for assessors contained in the Standards for Registered Training Organisations. |
RMIT employment requires all trainers and assessors to comply with the Standards for RTOs in respect to holding the TAE40116, or higher VE qualification including any necessary updated units. All employees must show currency within their vocational specialty along with their professional employment. |
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Competency is to be assessed in the workplace or a simulated environment that accurately reflects performance in a real workplace setting where these skills and knowledge would be performed. |
Assessments reflect the simulated workspace environment in line with current industry practices. |
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Candidates must have access to: | |
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All labs have the appropriate computers and software installed. Students will be provided home use licenses for ESRI ArcGIS Pro and Safe Software Feature Manipulation Engine (FME) |
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Students will be required to download the appropriate documents relating to work health and safety (WHS), data and information privacy and/or licensing as part of the training and assessment. |
Other Information
Attendance Requirement - 85% for all students.
Resubmission Policy:
To pass this course, students must achieve a Satisfactory (S) result for all their Assessments (and parts of those assessments).
Students who do not achieve a Satisfactory (S) result for their entire original submission for an Assessment will be provided with 1 (one) more submission opportunity (e.g. the resubmission) with a specific due date by their assessor. Students must achieve a Satisfactory (S) result on their resubmission for that Assessment, by the due date given to them by their Assessor.
Students who fail to submit their assessment by the due date will be issued a DNS (Did Not Submit) result which will not be overturned by teaching or managing staff. A DNS result will be overturned based on an approved Extension of Time or approved Special Consideration.
To be deemed Competency Achieved, students must achieve a satisfactory result in all assessment tasks. Students who fail to receive a Satisfactory result on all assessment tasks and have exhausted their resubmissions, extension of time applications, special consideration applications or Equitable Learning Plan provisions will be deemed as Not Yet Competent.
Credit Transfer and/or Recognition of Prior Learning (RPL):
You may be eligible for credit towards courses in your program if you have already met the learning/competency outcomes through previous learning and/or industry experience. To be eligible for credit towards a course, you must demonstrate that you have already completed learning and/or gained industry experience that is:
• Relevant
• Current
• Satisfies the learning/competency outcomes of the course
Please refer to the following link for more information about Credit Transfer and/or Recognition of Prior Learning (RPL):
http://www.rmit.edu.au/students/enrolment/credit
Study and learning Support:
Study and Learning Centre (SLC) provides free learning and academic development advice to you.
Services offered by SLC to support your numeracy and literacy skills are:
• Assignment writing, thesis writing and study skills advice
• Maths and Science developmental support and advice
• English language development
Please refer to the following link for more information about study and learning support:
http://www.rmit.edu.au/studyandlearningcentre
Equitable Learning Services (ELS):
If you are suffering from long-term medical condition or disability, you should contact Equitable Learning Services (ELS) to seek advice and support to complete your studies.
Please refer to the following link for more information about equitable learning services:
https://www.rmit.edu.au/students/support-and-facilities/student-support/equitable-learning-services
Late submission:
If you require an Extension of Submittable Work (assignments, reports or project work etc.) for 7 calendar days or less (from the original due date) and have valid reasons, you must complete and lodge an Application for Extension of Submittable Work (7 Calendar Days or less) form and lodge it with the Senior Educator/ Program Manager.
The application must be lodged no later than one working day before the official due date. You will be notified within no more than 2 working days of the date of lodgement as to whether the extension has been granted.
If you seek an Extension of Submittable Work for more than 7 calendar days (from the original due date) must lodge an Application for Special Consideration form under the provisions of the Special Consideration Policy, preferably prior to, but no later than 2 working days after the official due date.
Submittable Work (assignments, reports or project work etc.) submitted late without approval of an extension will not be accepted or marked.
Special consideration:
Please refer to the following link for more information about special considerations:
http://www.rmit.edu.au/students/specialconsideration
Plagiarism:
Plagiarism is a form of cheating, and it is very serious academic offence that may lead to expulsion from the University.
Please refer to the following link for more information about plagiarism:
www.rmit.edu.au/academicintegrity
All email communications will be sent to your RMIT email address, and you must regularly check your RMIT emails.
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