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Unit outline_

COMP5216: Mobile Computing

Semester 2, 2022 [Normal evening] - Camperdown/Darlington, Sydney

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
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

COMP5214 OR COMP9103 OR COMP9003. Software Development in JAVA, or similar introductory software development units

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Kanchana Thilakarathna, kanchana.thilakarathna@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Exam
Final Exam
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO3 LO4 LO5 LO6 LO8 LO9
Creative assessment / demonstration Lab Skills 1
Demonstration of mobile app development skills.
5% Week 04 2 weeks
Outcomes assessed: LO5 LO9
Assignment group assignment Project Proposal
Project proposal for the development of a mobile application.
10% Week 06 4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Creative assessment / demonstration Lab Skills 2
Demonstration of mobile app development skills.
5% Week 08 3 weeks
Outcomes assessed: LO5 LO8 LO9
Assignment group assignment Project Final
Demonstration of the mobile application development project.
30% Week 12 10 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
group assignment = group assignment ?
Type D final exam = Type D final exam ?

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 guide to grades.

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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction Lecture (2 hr) LO4
Android Basics Computer laboratory (1 hr) LO5 LO9
Week 02 Mobile Programming 1 Lecture (2 hr) LO4 LO7
Handling Interactions Computer laboratory (1 hr) LO5 LO9
Week 03 Mobile Programming 2 Lecture (2 hr) LO4 LO6
Local Data Storage Computer laboratory (1 hr) LO5 LO9
Week 04 Mobile Programming 3 Lecture (2 hr) LO4 LO6
Cloud Service Computer laboratory (1 hr) LO1 LO5 LO9
Week 05 Advanced Concepts 1 - Networking Lecture (2 hr) LO3 LO4 LO6
Media Access Computer laboratory (1 hr) LO1 LO5 LO9
Week 06 Advanced Concepts 2 - Security & Privacy Lecture (2 hr) LO3 LO4 LO6
Augmented/Virtual Reality on Smartphones Computer laboratory (1 hr) LO6 LO8 LO9
Week 07 Advanced Concepts 3 - Cloud Computing & Energy Lecture (2 hr) LO3 LO4 LO6
Location Access Computer laboratory (1 hr) LO5 LO9
Week 09 Mobile Innovation - Beyond Smartphones Lecture (2 hr) LO1 LO3 LO4 LO6
Access to Sensors Computer laboratory (1 hr) LO6 LO9
Week 10 Industry Guest Lecture Lecture (2 hr) LO3 LO4 LO6 LO7
User and Account Management Computer laboratory (1 hr) LO7 LO9
Week 11 Mobile Innovation - Cross-platform App Development Lecture (2 hr) LO4 LO6 LO7
App Analytics and Publishing Computer laboratory (1 hr) LO7 LO9
Week 12 Project Demo Day Lecture (2 hr) LO2 LO5 LO8 LO9
Project Demo Day Computer laboratory (1 hr) LO2 LO5 LO8 LO9
Week 13 Review and Reflection Lecture (2 hr) LO3 LO4 LO6 LO7
Exam Preparation Computer laboratory (1 hr) LO3 LO4 LO6 LO7

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Assessments are adjusted for online delivery.

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.