COMP SCI 1104 - Grand Challenges in Computer Science

North Terrace Campus - Semester 2 - 2016

The course information on this page is being finalised for 2016. Please check again before classes commence.

Introduction to key research areas in Computer Science and the "Grand Challenges". Topics include AI, Algorithms, Distributed Systems, Networking, Data Mining and Hardware; scholarship and writing in the discipline, critical analysis and thinking skills.

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Course Details

Course Code	COMP SCI 1104
Course	Grand Challenges in Computer Science
Coordinating Unit	Computer Science
Term	Semester 2
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 5 hours per week
Available for Study Abroad and Exchange	N
Assumed Knowledge	COMP SCI 1101
Restrictions	Available to B.Comp Sc (Advanced) students only, or by permission of the Head of School. Non-B.Comp Sc (Advanced) students must achieve a GPA of at least 6 in Computer Science courses before being considered for entry.
Assessment	May include reports, practical assignments, and presentations. Details will be provided at the start of the course.

Course Staff

Course Coordinator: Associate Professor Nickolas Falkner

Dr Brad Alexander will be the primary lecturer and coordinator for the course. In addition there will some consulting support available for project work as required during the semester.

Course Timetable

The full timetable of all activities for this course can be accessed from .

The course timetable takes place over Semester 2, 2015,

See the for this course for details.

Note, if the class is large some project sessions may be held over two rooms. Details will be announced in lectures.

Learning Outcomes
Course Learning Outcomes
In this course, you will learn about the grand challenges in the field of computing and what the six, currently defined, grand challenges are.

The learning objectives for Grand Challenges are:
1. To be able to identify, justify and discuss the grand challenge problems, giving clear examples of why these are significant to the discipline and to the population at large.
2. To develop and apply systematic and creative thinking techniques for analysis and problem solving
3. To gain experience in the application of critical thinking skills in the development of complex activities and in the provision of constructive criticism.
4. To gain experience in the application of fundamental Computer Science methods and algorithms in the analysis, summarization and presentation of large and significant data sets.
5. To develop or further refine the ability to communicate, in written, visual and verbal form, in order to convey complex information to others in a way that supports decision-making.
University Graduate Attributes

No information currently available.
Learning Resources
Required Resources

Required readings will be provided on the course website. There is no required textbook for this course.

Recommended Resources
There are no textbooks for this course. There are a number of reference books and additional notes will be given during class including:
1. Data Analysis with Open Source Tools P. Janert,
2. Information is Beautiful D. McCandless, Collins
Online Learning

The Grand Challenges course uses a Moodle forum to provide online resources to students:
Learning & Teaching Activities
Learning & Teaching Modes
The course aims to introduce students to a wide range of concepts and techniques. The course will be taught using the following class activities:
- Lectures, tutorials and practical/project activities Students are expected to attend all classes.
Marks will not be awarded for attendance, but a number of activities constitute designated presentation times and, if the activity is missed with no prior arrangement or sound 锟紃eason, marks will be forfeited as identified within the late penalty structure and assignment-specific rubric.

In addition, students are expected to spend significant time working on their assignments both within and outside of the laboratory. During the course, students will undertake a series of assignments designed to complement the material discussed in lectures and tutorials. These assignments involve the design and development of project work and reflective essays, and will enable students to test their knowledge of the concepts and theory discussed in class. You will be expected to record the production process of all of your assignments and your experiences across the course. This will provide you with the opportunity for reflection and review.
Workload

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

This information is provided as a guide to assist students in engaging appropriately with the course requirements. Grand Challenges is a 3 unit course. The expectation is that students will devote at least 156 hours to a 3 unit course, including contact hours. It is important to note that, given that the exam weighting is significantly smaller than is usual for Computer Science, it is expected that additional work time will be allocated to the assignments.

Learning Activities Summary
Lecture Topics
- Grand Challenges: Intro and the 6 Challenges
- Advanced Computational Methods and Algorithms, Data and Visualisation
- High Performance Computing; Software Infrastructure
- Education, training and workforce; Grand Challenge Communities
- Current Research: School research group leaders - grand challenge focus
- Supporting research with evidence: statistics.
- Thinking about thinking: fallacies, philosophical foundations and a world of effects.
- Dynamic visualisation in industry and research
- Blue sky thinking: research leaders discuss their wish list.
- How are we doing?
- Defined grand challenges versus achievement: 1945- 2012
- Ethical issues in Community Science: where are we going?
Tutorials
Topics are selected according to project. Topics may include:
Defining a grand challenge; Parallelisable Problems; Simulation and Modelling; Analysis of Stream Data; Introduction to analysis; Efficient methods for data analysis; Introduction to Bayesian probability; Statistical fallacies and paradoxes; Identifying fallacies and effects.; Self-assessment of Project 2 pitch; Rubric generation for assessing project 2;Outreach: how can I explain this to other people? Computer Science Identity: What are we? Producing an intro to R practical exercise

Project and Practical Activities
- Project 1: Preparation
- Project 1: Pitch of Candidates. Group feedback.
- Project 1: First cut
- Project 1: Feedback
- Project 1: Revised version, rebuttal.
- Project 2: Second Project iteration
- Project 2: Pitch and feedback
- Project 2: Progress report.
- Project 2: First demonstration and feedback.
- Project 2: Final demonstration.
Specific Course Requirements

None

Small Group Discovery Experience

Grand Challenges will examine relevant research literature and contains a project component but is not formally part of the small group discovery experience.

The University's policy on Assessment for Coursework Programs is based on the following four principles:

Assessment must encourage and reinforce learning.
Assessment must enable robust and fair judgements about student performance.
Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
Assessment must maintain academic standards.

Assessment Summary

The assessment for this course consists of the following weightings:
Exam – 25%
Project 1 – 25%
Project 2 – 45%
Course Improvement Report: 5%

Assessment Related Requirements

In order to pass, students must achieve an overall passing grade and not score less than 40% in the Exam.

Assessment Detail

The projects are weighted as above, with the following breakdown of marks within the projects and mapping to course objectives and CBOK Skills Sets.

Assessment	Type	Proportion of that assessment		learning objective	CBOK Mappping*
Assessment	Type	Proportion of that assessment	Due Week	learning objective	Abstraction	Design	Ethics	Communication	Societal issues	Data	Programming	HCI	Systems Development
Proj 1 Pitch	Formative	10%	week 3	1,2,3,4,5		3		3
Proj 1 First cut demo	Formative	20%	week 4	3,4,5	3	3		3	3	3	3	3	3
Proj 1 Feedback Report	Formative	20%	week 5	1,3,5		3		3
Proj 1 Final Submission	Summative	25%	week 6	1,2,3,4,5	3	3		3	3	3	3	3	3
Proj 1 Final Report	Summative	25%	week 7	1,3,5		3		3	3	3
Proj 2 Pitch	Formative	10%	week 8	1,2,3,4,5		3		3
Proj 2 First cut Demo	Formative	25%	week 9	1,3,5	3	3		3	3	3	3	3	3
Proj 2 Feedback Report	Summative	10%	week 10	1,3,5		3		3
Proj 2 Final Submission	Summative	30%	week 11	1,2,3,4,5	3	3		3	5	5	5	5	3
Proj 2 Final Report	Summative	25%	week 12	1,3,5		3		3	3	3
Course Improvement Report	Summative	100%	week 12	1,3,5				3
Exam	Summative	100%	exam period	1,2,3,5			4	3	4	3

Due Dates: The assignment due dates will be made available on the course website.
*CBOK categories are explained in section 4 . Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).

Submission

All programming assignments will be submitted via the school's Web Submission gateway, available from the school web page (http://www.cs.adelaide.edu.au). Other materials may be submitted to the school's Moodle forums (http://forums.cs.adelaide.edu.au).
Both electronic systems provide cover sheets for submitted work. No physical submissions of work will be accepted unless specifically requested by the lecturer - all other submissions will be electronic. Students are strongly advised to keep copies of any electronic work that they submit, if they are entering text into fields without a receipted copy.

The School of Computer Science observes a strict lateness policy. Your mark is capped by an additional 25% for each day late. 1 day late and your maximum mark can now only be 75%. 2 days late, 50%, 3 days late, 75%. Any submission beyond this point attracts no marks. Days are calculated from the time of hand-in, hence, if a hand-in is due at midnight, 12:01am is 1 day late.
Extensions may be requested in advance for medical or compassionate reasons but (1) all requests must be accompanied by documentation, (2) extensions awarded will be proportional to any days missed due to illness (sick for 1 day WITH a medical certificate will only get you a 1 day extension), (3) no extensions will be granted on the final day unless the issue is both severe and unforeseen

Course Grading

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
Grade	Mark	Description
FNS		Fail No Submission
F	1-49	Fail
P	50-64	Pass
C	65-74	Credit
D	75-84	Distinction
HD	85-100	High Distinction
CN		Continuing
NFE		No Formal Examination
RP		Result Pending

Further details of the grades/results can be obtained from Examinations.

Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

Final results for this course will be made available through .

Student Feedback

The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
Fraud Awareness

Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.

The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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