Outline

Overview

Mobile computing is becoming a main stream for many IT applications, due to the availability of more and more powerful and affordable mobile devices with rich sensors such as cameras and GPS, which have already significantly changed many aspects in business, education, social network, health care, and entertainment in our daily life. Therefore it has been critical for students to be equipped with sufficient knowledge of such new computing platform and necessary skills. The unit aims to provide an in-depth overview of existing and emerging mobile computing techniques and applications, the eco-system of the mobile computing platforms, and its key building components. The unit will also train students with hand-on experiences in developing mobile applications in a broad range of areas.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(DATA3888 or COMP3888 or COMP3988 or CSEC3888 or ISYS3888 or SOFT3888 or ENGG3112 or SCPU3001) and INFO1113
Corequisites ?	None
Prohibitions ?	COMP5216
Assumed knowledge ?	A major in a computer science area
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Anusha Withana, anusha.withana@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final Exam	50%	Formal exam period	1.5 hours	AI prohibited
Outcomes assessed:
Practical skill	Mini Projects There will be 5 mini-projects (MP), each worth 2 points, to evaluate your skills. Only the highest-scoring 2 will be counted towards the final marks (4%). Meaning you can safely miss 3 out of 5. Once you achieve the 4%, the other MPs will not be graded.	4%	Multiple weeks	Weekly - In-class and tutorial activity.	AI allowed
Outcomes assessed:
In-class quiz	In-Class Quiz There will be 5 quizzes, each worth 2 points, to evaluate your knowledge. Only the highest-scoring 2 will be counted towards the final marks (4%). Meaning you can safely miss 3 out of 5. Once you achieve the 4%, the rest of the quizzes will not be graded.	4%	Multiple weeks	Weekly - In-class and tutorial activity.	AI allowed
Outcomes assessed:
Written work	Project Proposal Project proposal for the development of a mobile application.	5%	Week 04 Due date: 28 Aug 2025 at 23:59	2 weeks	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Project Progress - Demo Demonstration of the progress of the mobile application development project.	5%	Week 08	6 weeks	AI limited - refer to Canvas
Outcomes assessed:
Portfolio or journal	Project Phase 2 - Mid-Progress Project progress report and code repo. Hurdle: All the members must participate and contribute; if no contribution at all, the student will fail the UoS.	10%	Week 08 Due date: 25 Sep 2025 at 23:59	6 weeks	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Project Final Demo Demonstration of the mobile application development project	7%	Week 12	10 weeks	AI limited - refer to Canvas
Outcomes assessed:
Portfolio or journal	Project Phase 3 - Final submission Project final report and code repo. Hurdle: All the members must participate and contribute; if no contribution at all, the student will fail the UoS.	15%	Week 12 Due date: 30 Oct 2025 at 23:59	8 weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Project Proposal: The project proposal assignment asks students to produce a proposal/report for an innovative mobile application development.

Project Final: Students are expected to demonstrate the successfull development of the mobile application through reports, videos, in-class presentation and demonstration.

Lab Skills 1,2: Lab skill component assesses the progress of practical programming tasks for mobile application development.

Final Exam: Final exam is written exam that covers all aspects of the course. Obtaining at least 40% of the available marks from the written exam is a requirement to pass the course.

Detailed information for each assessment will be made available via Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Deduction of 5% of the maximum mark for each calendar day after the due time on the due date. After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and Overview	Lecture (2 hr)
Week 01	Understanding the Development Ecosystem	Computer laboratory (1 hr)
Week 02	Mobile Application Design	Lecture (2 hr)
Week 02	Designing a Mobile Application	Computer laboratory (1 hr)
Week 03	Mobile Application Programming - 1	Lecture (2 hr)
Week 03	Designing Interactive Mobile Applications	Computer laboratory (1 hr)
Week 04	Mobile Application Programming - 2	Lecture (2 hr)
Week 04	Project Activities - Phase 1	Computer laboratory (1 hr)
Week 05	Mobile Concepts - Multimedia	Lecture (2 hr)
Week 05	Designing Multimedia Mobile Applications	Computer laboratory (1 hr)
Week 06	Mobile Concepts - Components	Lecture (2 hr)
Week 06	Integrating Mobile Components	Computer laboratory (1 hr)
Week 07	Mobile Concepts - Connectivity	Lecture (2 hr)
Week 07	Integrating Connectivity	Computer laboratory (1 hr)
Week 08	Mobile Concepts - Security	Lecture (2 hr)
Week 08	Project Activities - Phase 2 - Demo	Computer laboratory (1 hr)
Week 09	Advanced Concepts 1 - Networking	Lecture (2 hr)
Week 09	Integrating Advance Network Concepts	Computer laboratory (1 hr)
Week 10	Advanced Concepts 2 - Services	Lecture (2 hr)
Week 10	Integrating Services	Computer laboratory (1 hr)
Week 11	Deployment and Management	Lecture (2 hr)
Week 11	Project Acitivities - Phase 3	Computer laboratory (1 hr)
Week 12	Project Activities - Demo	Lecture (2 hr)
Week 12	Project Activities - Demo	Computer laboratory (1 hr)
Week 13	Guest Lecture and Revisions	Lecture (2 hr)
Week 13	Revisions	Computer laboratory (1 hr)

Attendance and class requirements

We expect you to attend the classes and tutorials. The classes will include in-class activities. If you do not participate, you can lose the respective points.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Articulate multimedia processing techniques and present applications
LO2. Demonstrate project management and team coordination skills
LO3. Employ learned knowledge and skills to solve problems with mobile technology
LO4. Understand the mobile computing ecosystem
LO5. Demonstrate competent skills on mobile application development
LO6. Understand emerging application domains and techniques of mobile computing
LO7. Understand project management in mobile application development
LO8. Demonstrate skills in exploring broad interests relevant to mobile computing
LO9. Demonstrate practical skills in system modelling & design and computing techniques.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Added more focus on the mobile app development. Integrate cross-platform development. Assessment feedback is included for the project work. More practical content in class.

Additional information

Only the highest-scoring 2 quizzes and mini-projects will be counted towards the final marks (4%) respectively. Meaning you can safely miss 3 out of 5 of each. Once you achieve the 4% in each category, the other Quizzes/MPs will not be graded. However, you can seek feedback in person at the tutorial.
Hurdle in projects: All the group members must participate and contribute - contribution of individual members should be reflected in the code repo; if no contribution at all, the student will fail the UoS

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

COMP4216: Mobile Computing

Semester 2, 2025 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links