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Unit outline_

ISYS2110: Analysis and Design of Web Info Systems

Semester 1, 2022 [Normal day] - Remote

This course discusses the processes, methods, techniques and tools that organisations use to determine how they should conduct their business, with a particular focus on how web-based technologies can most effectively contribute to the way business is organized. The course covers a systematic methodology for analysing a business problem or opportunity, determining what role, if any, web-based technologies can play in addressing the business need, articulating business requirements for the technology solution, specifying alternative approaches to acquiring the technology capabilities needed to address the business requirements, and specifying the requirements for the information systems solution in particular, in-house development, development from third-party providers, or purchased commercial-off-the-shelf (COTS) packages.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
INFO1113 OR INFO1103 OR INFO1105 OR INFO1905
Corequisites
? 
None
Prohibitions
? 
INFO2110
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Josiah Poon, josiah.poon@sydney.edu.au
Lecturer(s) Josiah Poon, josiah.poon@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final Exam
AI proctoring via ProctorU
50% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
In-semester test (Record+) Type B in-semester exam Quiz
MC plus case study. Carried out in the 2nd session of w7 lecture
15% Week 07
Due date: 08 Apr 2022 at 16:00

Closing date: 08 Apr 2022
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO9
Assignment group assignment Group Assignment
Report, prototype and video deliverables expected.
30% Week 10
Due date: 07 May 2022 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO9 LO10
Assignment group assignment Quality as Reviewer
This is to assess how well your group is when you review and mark other gro
5% Week 12
Due date: 21 May 2022 at 23:59
Less than one week.
Outcomes assessed: LO1 LO2 LO3 LO4 LO9
Group assignment with individually assessed component = group assignment with individually assessed component ?
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?

Assessment summary

Quiz: It is a mid-semester assessment to your understanding of the knowledge content. This will be in MC and/or short answer.

Group Project: Elicit and document system requirements; assemble descriptions of the system using appropriate notations; design a simple web-based prototype; deliver/explain these in a video presentation. 

Final Exam: held in formal exam week with questions covering all topics included in the lecture, tute, report and project assignment.

Detailed information for each assessment can be found on Canvas.

1. The 40% exam barrier policy

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 final 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.

2. Usage of similarity detection software

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.”

And then depending on whether we check for software similarity of text similarity in documents:

"Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.”

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

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

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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:

Late assignment submissions immediately attract a 5% per day penalty (or part thereof). Submissions with more than 10 days late will receive a mark of 0.

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 to analysis and design and web information system Lecture and tutorial (2 hr) LO1
Week 02 Managing System Projects Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 03 Requirement Modelling Lecture and tutorial (4 hr) LO1 LO3
Week 04 Business Functions and Process Modelling (Use Case Analysis) Lecture and tutorial (4 hr) LO3 LO4
Week 05 User Interface Lecture and tutorial (4 hr) LO9
Week 06 HTML & CSS Lecture and tutorial (4 hr) LO9 LO10
Week 07 Structural and Behaviourial Design Lecture and tutorial (4 hr) LO4
Week 09 Data Management Layer Design Lecture and tutorial (4 hr) LO5
Week 10 Systems architecture design Lecture and tutorial (4 hr) LO6
Week 11 Construction Lecture and tutorial (4 hr) LO8
Week 12 Installation and Post-Installation Lecture and tutorial (4 hr) LO7
Week 13 Course review Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

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.

Required readings

TEXTBOOK(S)
Dennis, A., Wixom, B.H., and Tegarden, D., System Analysis & Design  with UML Version 2.0. John Wiley & Sons, 2012.


RECOMMENDED REFERENCES
Scott Tilley & Harry J. Rosenblatt, Systems Analysis and Design 11th Ed, Cengage Learning US.


Satzinger, Jackson & Burd, Systems Analysis and Design in a Changing World (7th Ed), Cengage Learning US.

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. Discuss the stages in the process of developing an information system, and the relationship to the organisational context (especially the role of systems analysts interacting with other stakeholders), and explain the way the process uses documents such as requirements descriptions and analysis models.
  • LO2. Describe the issues of risk, and methods of dealing with them, including cost-benefit analyses, project planning and management, and be able to work with project planning documents including Gantt charts and detailed work breakdown structures
  • LO3. Identify requirements based on a substantial realistic context, e.g. through joint requirements planning, carrying out or watching interviews, questionnaires, and be able to work with requirements documents, to identify aspects of requirements including functional, performance and usability conditions.
  • LO4. Develop understanding of technical methods to document requirements, as well as structural and behavioural design.
  • LO5. Consideration of data management layer of a system in the design stage, with the attention to look at how objects can be stored and the important characteristics.
  • LO6. Introduce different types of physical architecture, their characteristics and issues.
  • LO7. Introduce the system installation process. Understand different types of conversion strategies and also change management. Also, get familiar with post-installation process.
  • LO8. Be familiar with system construction process, and different types of test and when to use. Produce clear well-constructed documentation.
  • LO9. Understand the fundamental user interface design principle and, to be specific, in a web-based information system.
  • LO10. Able to develop web-based prototype

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.

No changes have been made since this unit was last offered except the distribution of the assessments.

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.