COMP SCI 1104 - Grand Challenges in Computer Science

North Terrace Campus - Semester 2 - 2017

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.

  • General Course Information
    Course Details
    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
    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.
    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 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 Course Planner.

    The course timetable takes place over Semester 2, 2015,

    See the class planner 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
    On successful completion of this course students will be able to:

    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 visualisation 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.

    The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
    The course is designed to develop the following Elements of Competency: 1.1   1.2   1.3   1.4   1.5   1.6   2.1   2.2   2.3   2.4   3.1   3.2   3.3   3.4   3.5   3.6   

    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1, 4, 5
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    1, 4, 5
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1, 4, 5
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    1, 5
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, diffuse conflict and engage positively in purposeful debate
    2, 5
  • 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 reason, 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.

    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?
    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
    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.
  • Assessment

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

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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*                                                             
     Due Week 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 of the ICT core body of knowlege. Numbers assigned correspond to the Bloom taxonomy (see page 26 of the same document).
    All programming assignments will be submitted via the school's Web Submission gateway, available from the school web page ( Other materials may be submitted to the school's Moodle forums (
    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 Access Adelaide.

  • 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 ( 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
  • 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’s disciplinary procedures.

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