This course provides Students with the opportunity to develop a solid understanding of programming that is essential before progressing to more advanced concepts. It is structured to demonstrate and provide the students with practice in the fundamentals of structured programming. It is designed for Postgraduate students who do not have a programming background.
Students will learn to use an industrial-strength object-oriented programming language and form basic mental models of how computer programs execute and interact with their environment. The course focuses on key aspects of solving programming problems: reasoning about a problem description to design appropriate data representations and function/method descriptions, to find examples, to write, test, debug, and otherwise evaluate the relevant code, and to present and defend their approach.
Students will learn to effectively use a large standard library and key standard data structures, including lists, trees, hash tables, and graphs. The course also introduces the basics of reasoning about the time and space complexity of algorithms, in particular as related to the above data structures.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Internalize computation thinking
- Apply fundamental programming concepts, using an object-oriented programming language, to solve practical programming problems
- Implement, debug, and evaluate algorithms for solving substantial problems; implement an abstract data type
- Apply basic algorithmic analysis to simple algorithms; use appropriate algorithmic approaches to solve problems
- Design, implement, and test data structures and code
- Understand their ethical responsibilities as a programmer with respect to Academic integrity, the use of Artificial Intelligence and authorship of code
- Present, explain, evaluate, and defend choices in design and implementations of programs and algorithms
Examination Material or equipment
- Mid-term tests are closed-book, and no materials are allowed.
- For the final exam, students will be allowed to bring a cheat sheet.
- Further instructions will be released on the online forum prior to the examination.
Recommended Resources
Whether you are on campus or studying online, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.
ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.
A laptop computer that can be brought to the lectures (BYOD) with an up-to-date version of Java (23+), a git client, a basic text editor, and IntelliJ Idea (Community Edition).
Staff Feedback
Students will be given feedback in the following forms in this course:
- written comments
- verbal comments
- feedback to whole class, groups, individuals, focus group etc
Student Feedback
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.
Other Information
The use of Generative AI Tools (e.g., ChatGPT) is permitted in this course, given that proper citation and prompts are provided, along with a description of how the tool contributed to the assignment. Guidelines regarding appropriate citation and use can be found on the ANU library website https://libguides.anu.edu.au/generative-ai
Marks will reflect the contribution of the student rather than the contribution of the tools. Further guidance on appropriate use should be directed to the convener for this course.
Class Schedule
| Week/Session | Summary of Activities | Assessment |
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| 1 | Course Introduction and Programming FoundationsIntroduction to the course structure, expectations, and assessment. Overview of fundamental programming concepts using Java. Introduction to the Java development environment, IDE usage, and interactive programming with JShell. |
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| 2 | Version Control and Java FundamentalsIntroduction to version control concepts and collaborative development using Git. Introduction to Java fundamentals, including variables, control flow, and basic syntax. |
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| 3 | Object-Oriented Programming Core Concepts Introduction to Object-Oriented Programming principles, focusing on objects, classes, methods, constructors, and encapsulation. |
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| 4 | Object-Oriented Programming (continued)Advanced OOP concepts including inheritance, polymorphism, abstract classes, enumeration types, and sealed classes. Discussion of design considerations and appropriate use of inheritance hierarchies. |
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| 5 | Object-Oriented Programming (Continued) and Review Continuation and consolidation of Object-Oriented Programming concepts. Structured review of key topics followed by the first mid-term test. |
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| 6 | Functional Features, Testing, and Documentation Introduction to lambda expressions and functional-style programming in Java. Basics of software testing concepts and practices. Introduction to Markdown for technical documentation and reporting. |
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| 7 | Exceptions and Generic Programming Handling runtime errors through exception handling mechanisms. Introduction to generic programming, including type parameters and their role in writing reusable, type-safe code. |
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| 8 | Recursion Concepts and applications of recursion Analysis of recursive problem-solving techniques, base cases, and recursive reasoning, with practical programming examples. |
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| 9 | Recursion Concepts and applications of recursion (continued) and ReviewConsolidation of key concepts followed by the second mid-term test. |
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| 10 | Abstract Data Types and Collections Introduction to Abstract Data Types (ADTs) and their role in software design. Exploration of Java Collections, including lists, sets, maps, and their common use cases. |
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| 11 | Algorithms, Complexity, and StreamsIntroduction to algorithmic thinking and computational complexity. Analysis of algorithm efficiency and performance. Introduction to Java Streams for data processing and functional-style operations on collections. |
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| 12 | Code Ethics, Professional Practice, and Review Discussion of ethical considerations and professional conduct in programming, including code quality, responsibility, and academic integrity. Final review and consolidation of course concepts. |
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Tutorial Registration
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.Assessment Summary
| Assessment task | Value | Due Date | Learning Outcomes |
|---|---|---|---|
| List of Exercises 1 | 1 % | 26/03/2026 | 1 |
| List of Exercises 2 | 1 % | 26/03/2026 | 1,2 |
| List of Exercises 3 | 1 % | 26/03/2026 | 1,2,3 |
| List of Exercises 4 | 1 % | 07/05/2026 | 1,2,3 |
| List of Exercises 5 | 1 % | 07/05/2026 | 1,2,3,4 |
| List of Exercises 6 | 1 % | 07/05/2026 | 1,2,3,4,5 |
| List of Exercises 7 | 1 % | 07/05/2026 | 1,2,3,4,5 |
| List of Exercises 8 | 1 % | 28/05/2026 | 1,2,3,4,5 |
| List of Exercises 9 | 1 % | 28/05/2026 | 1,2,3,4,5,7 |
| List of Exercises 10 | 1 % | 28/05/2026 | 1,2,3,4,5,7 |
| Computer Lab Assignment 1 | 1 % | 04/03/2026 | 1 |
| Computer Lab Assignment 2 | 1 % | 17/03/2026 | 1,2 |
| Computer Lab Assignment 3 | 1 % | 24/03/2026 | 1,2,3 |
| Computer Lab Assignment 4 | 1 % | 21/04/2026 | 1,2,3,4 |
| Computer Lab Assignment 5 | 1 % | 28/04/2026 | 1,2,3,4,5 |
| Computer Lab Assignment 6 | 1 % | 05/05/2026 | 1,2,3,4,5,7 |
| Computer Lab Assignment 7 | 1 % | 19/05/2026 | 1,2,3,4,5,7 |
| Computer Lab Assignment 8 | 1 % | 26/05/2026 | 1,2,3,4,5,7 |
| Codewalk | 2 % | 20/05/2026 | 1,7 |
| Mid-term test 1 | 15 % | 26/03/2026 | 1,2,3 |
| Mid-term test 2 | 15 % | 07/05/2026 | 4,5,7 |
| The 'Book' Assignment | 10 % | 29/05/2026 | 1,5,6,7 |
| Final Exam | 40 % | * | 1,2,3,4,5,6,7 |
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
Policies
ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:
- Academic Integrity Policy and Procedure
- Student Assessment (Coursework) Policy and Procedure
- Extenuating Circumstances Application
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
- Code of practice for teaching and learning
Assessment Requirements
The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Canvas’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Examination(s)
Two mid-term tests are held in Weeks 5 (15 marks) and 9 (15 marks). Both mid-term tests are paper-based, and the lower of the two marks is redeemable against the final exam.
Final exam (40 marks): Conducted in the computer labs. No materials are permitted except for a cheat sheet and approved dictionaries, which must be approved prior to the start of the exam.
Assessment Task 1
Learning Outcomes: 1
List of Exercises 1
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 2
Learning Outcomes: 1,2
List of Exercises 2
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 3
Learning Outcomes: 1,2,3
List of Exercises 3
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 4
Learning Outcomes: 1,2,3
List of Exercises 4
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 5
Learning Outcomes: 1,2,3,4
List of Exercises 5
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 6
Learning Outcomes: 1,2,3,4,5
List of Exercises 6
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 7
Learning Outcomes: 1,2,3,4,5
List of Exercises 7
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 8
Learning Outcomes: 1,2,3,4,5
List of Exercises 8
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 9
Learning Outcomes: 1,2,3,4,5,7
List of Exercises 9
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 10
Learning Outcomes: 1,2,3,4,5,7
List of Exercises 10
A list of exercises consisting of approximately 5-10 programming questions aligned with the weekly topics. The exercises form a formative assessment designed to support practice and consolidation of key concepts. Students may complete the exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 11
Learning Outcomes: 1
Computer Lab Assignment 1
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 12
Learning Outcomes: 1,2
Computer Lab Assignment 2
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 13
Learning Outcomes: 1,2,3
Computer Lab Assignment 3
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 14
Learning Outcomes: 1,2,3,4
Computer Lab Assignment 4
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 15
Learning Outcomes: 1,2,3,4,5
Computer Lab Assignment 5
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 16
Learning Outcomes: 1,2,3,4,5,7
Computer Lab Assignment 6
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 17
Learning Outcomes: 1,2,3,4,5,7
Computer Lab Assignment 7
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 18
Learning Outcomes: 1,2,3,4,5,7
Computer Lab Assignment 8
Computer Lab Assignments are practical programming tasks designed to reinforce the concepts introduced in lectures. These assignments are completed during scheduled laboratory sessions, with guidance and support provided by tutors. Some assignments may be assessed in-lab (e.g., Task 11), while others require submission by a specified deadline (e.g., Task 12 to Task 18). Students may complete the lab exercises individually or collaboratively. Assessment is attempt-based (individual submission counts as completion), rather than based on the correctness of the final solutions. The use of GenAI tools is permitted, provided that all prompts used are submitted.
Assessment Task 19
Learning Outcomes: 1,7
Codewalk
Each student is required to participate in one code walk during the course. Code walks are conducted during the scheduled lab sessions, where a tutor will randomly select a small number of students to discuss their code. During the code walk, students must explain their implementation and demonstrate their understanding of the programming concepts used. The code walk is a formative assessment, and marking is attempt-based rather than based on correctness. It provides students with an opportunity to receive direct feedback from tutors and to practice articulating their understanding of programming concepts.
Assessment Task 20
Learning Outcomes: 1,2,3
Mid-term test 1
The mid-term tests are summative assessments in which students must individually demonstrate their understanding of the course material. Each mid-term test is conducted during the scheduled lecture time and is paper-based. No materials are permitted during the mid-term tests. Students are assessed on their ability to apply concepts independently under exam conditions. There are two mid-term tests, and the lower of the two mid-term test marks will be redeemed against the final exam.
Assessment Task 21
Learning Outcomes: 4,5,7
Mid-term test 2
The mid-term tests are summative assessments in which students must individually demonstrate their understanding of the course material. Each mid-term test is conducted during the scheduled lecture time and is paper-based. No materials are permitted during the mid-term tests. Students are assessed on their ability to apply concepts independently under exam conditions. There are two mid-term tests, and the lower of the two mid-term test marks will be redeemed against the final exam.
Assessment Task 22
Learning Outcomes: 1,5,6,7
The 'Book' Assignment
The book assignment is a group-based assessment in which students collaborate to write on a predefined list of topics related to the course. Each group is responsible for producing a book chapter focused on one assigned topic.
The chapter must include (max 2000 words excluding exercises, solutions and JavaDoc):
- An explanation of the core conceptual topic
- A relevant use case illustrating practical application
- A set of three exercises related to the topic
- A list of potential solutions for the exercises
- JavaDoc is mandatory for all code
Note that submissions that exceed the prescribed length will incur a penalty of 1 mark per 10% (or part thereof) beyond the specified limit, up to a maximum of the total mark.
All content must be original and created by the authors (students). This is a summative assessment and marked based on correctness of the concepts, use case, exercises, solutions and JavaDoc.
Assessment Task 23
Learning Outcomes: 1,2,3,4,5,6,7
Final Exam
The final exam is a summative, individual, and computer-based assessment in which students must demonstrate their understanding and application of the concepts covered throughout the course. The exam will be conducted in the computer labs, and students will use IntelliJ IDEA, Java and Git. The final exam may include a combination of coding tasks, multiple-choice questions, and open-ended questions. The duration of the final exam is 180 minutes. The use of GenAI tools is not permitted during the exam. Students may bring one cheat sheet. Further specifications will be provided prior to the exam via the course communication forum.
Academic Integrity
Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.
The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.
The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.
The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.
Online Submission
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
Hardcopy Submission
For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.
Late Submission
- Late submission permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.
Referencing Requirements
The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.
Returning Assignments
Students will receive feedback directly in code walks by tutors, and will also be provided with the results of automated tests used to evaluate their submitted code. Further information to be provided on course site.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
No assignment resubmissions.
Privacy Notice
The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Accessibility for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents all ANU students
Convener
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Dr Bernardo Pereira Nunes
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