COMP SCI 1104 - Grand Challenges in Computer Science
North Terrace Campus - Semester 2 - 2014
General Course Information
Course Code COMP SCI 1104 Course Grand Challenges in Computer Science Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 5 hours per week 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. Course Description 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.
Course Coordinator: Dr Bradley AlexanderDr 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.
The full timetable of all activities for this course can be accessed from Course Planner.
The course timetable takes place over Semester 2, 2014, with one 2-hour lecture on every Tuesday, one 1-hour Tutorial on each Friday and a 2-hour project/practical session on each Friday. The location of activities may vary depending on the week and the activity. Students are expected to read the course forum (Section 3.3) to confirm any changes to location.
Course Learning OutcomesIn 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:
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.
To develop and apply systematic and creative thinking techniques for analysis and problem solving
To gain experience in the application of critical thinking skills in the development of complex activities and in the provision of constructive criticism.
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.
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
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4,5 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,3,4 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2,3,4 Skills of a high order in interpersonal understanding, teamwork and communication. 3,5 A proficiency in the appropriate use of contemporary technologies. 1,4 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 1,2,3,5 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 1,3,5
Required ResourcesRequired readings will be provided on the course website. There is no required textbook for this course.
Recommended ResourcesThere are no textbooks for this course. There are a number of reference books and additional notes will be given during class including:
- Data Analysis with Open Source Tools P. Janert,
- Information is Beautiful D. McCandless, Collins
Online LearningThe Grand Challenges course uses a Moodle forum to provide online resources to students: http://forums.cs.adelaide.edu.au/
Learning & Teaching Activities
Learning & Teaching ModesThe course aims to introduce students to a wide range of concepts and techniques. The course will be taught using the following class activities:
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 ￼reason, marks will be forfeited as identified within the late penalty structure and assignment-specific rubric.
- Lectures, tutorials and practical/project activities Students are expected to attend all classes.
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.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.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 SummaryLecture 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?
Topics are selected according to project. Topics may include:Defining a grand challenge; Parallelisable Problems; Simulation and Modelling; Agent-based Modelling; 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 exerciseProject 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.
- Project 2: Presentations: to School of CS.
Specific Course RequirementsNone
Small Group Discovery ExperienceGrand 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 SummaryThe assessment for this course consists of the following weightings:
Exam – 20%
Project 1 – 20%
Project 2 – 40%
Written report on an existing challenge – 10%
Course Improvement Reports (1 and 2): 10% (5% each)
Assessment Related RequirementsIn order to pass, students must achieve an overall passing grade and not ￼
score less than 40% in all of the Exam, Project 1 and Project 2. The Course Improvement Reports are not part of the minimum performance requirement.
Assessment DetailThe projects are weighted as above, with the following breakdown of marks within the projects (based on the weights as listed in 5.1).
- Project 1 Pitch: Formative, 10%, learning objective 1,2,3,4,5
- Project 1 First cut demo: Formative, 20%, learning objectives 3,4,5
- Project 1 Feedback Report: Formative, 20%, learning objective 1,3,5.
- Project 1 Final Submission: Summative, 25%, learning objective 1,2,3,4,5
- Project 1 Final Report: Summative, 25%, learning objective 1,3,5
- Project 2 Pitch: Formative, 10%, learning objective 1,2,3,4,5
- Project 2 Progress Report: Formative, 0%, learning objective 1,3,5
- Project 2 First cut demo: Formative, 25%, learning objectives 3,4,5
- Project 2 Feedback Report: Summative, 10%, learning objective 1,3,5
- Project 2 Final Submission: Summative, 30%, learning objective 1,2,3,4,5
- Project 2 Final Report: Summative, 25%, learning objective 1,3,5
- Course Improvement Report 1: Summative, learning objective 1,2,3,4,5
- Course Improvement Report 2: Summative, learning objective 1,2,3,4,5
Due Dates: The assignment due dates will be made available on the course website.
SubmissionAll 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, and (4) extensions are never granted because you have been busy, have managed your time poorly or are over-committed.
Extensions are only available for issues that are beyond your control or where you have carefully planned your time where you have a known conflict and, well enough in advance, discussed the matter and arranged an extension.
You will receive ongoing feedback on other matters throughout the course. Any written assignments will be marked and returned, where possible, within a two week period. Where your work is of a standard that would put you at risk of hitting the minimum performance criterion, we will attempt to identify you as an at-risk student early on and, if you have been putting in sufficient effort elsewhere, you may be given a chance to resubmit some work that will have the possibility of removing the MP block, although it will not improve your mark. Students who demonstrate a late hand-in pattern, or have intermittent hand- in completions, will be contacted and encouraged to change their planning patterns to achieve better results.
Obviously, students may choose to continue with their current patterns, with no other penalty than continuing the behaviour that has led to them achieving low marks. Students who achieve a final mark in the range of 45-49 will be automatically granted academic supplementary examinations if they have completed all required coursework. Students who achieve a final mark of 40-44 may be offered academic supplementary examinations, academic supplementary coursework or a combination of these but the offer is at the discretion of the academic staff. Students who have done very well in the exam but have completed little to no coursework will not be given an opportunity for redemption.
Grades for your performance in this course will be awarded in accordance with the following scheme:
GS8 (Coursework Grade Scheme) Grade Description CN Continuing FNS Fail No Submission NFE No Formal Examination F Fail NGP Non Graded Pass P Pass C Credit D Distinction HD High Distinction 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 Access Adelaide.
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.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
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- Coursework Academic Programs Policy
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- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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