COMP SCI 7202BNA - Foundations of Computer Science B

Ngee Ann Academy - Trimester 2 - 2014

Introduces the fundamental concepts of object oriented programming. Topics include: Fundamental constructs - data types, arrays, strings and string processing, files, variables, expressions, conditionals, iteration, simple I/O. Object oriented fundamentals - methods, classes, interfaces, inheritance Algorithms and problem solving - problem solving process and strategies, simple searching and Sorting algorithms (linear and binary search, selection and insertion sort) Software development tools and techniques - testing: black box, requirements, unit Machine level representation - bits, bytes, words, number bases, representation of data, memory management.

  • General Course Information
    Course Details
    Course Code COMP SCI 7202BNA
    Course Foundations of Computer Science B
    Coordinating Unit Computer Science
    Term Trimester 2
    Level Postgraduate Coursework
    Location/s Ngee Ann Academy
    Units 3
    Contact Up to 12 hours per week
    Incompatible COMP SCI 7080
    Restrictions For approved Master of Computing and Innovation, Graduate Diploma in Computer Science and Graduate Certificate in Computer Science students only.
    Assessment Written exam and/or assignments
    Course Staff

    Course Coordinator: Dr Alfred Fred Brown

    Teaching PeriodCourse Coordinator
    Trimester 2 Dr Cheryl Pope
    Trimester 3
    Dr Fred Brown

    Lecturer: Mr Kwang Hua Lim
    Course Timetable

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

    The full timetable of all activities for this course can be accessed from
  • Learning Outcomes
    Course Learning Outcomes
    The key learning objectives for this course are:
    1. Design, implement and test algorithms using fundamental programming constructs and data structures.
    2. Translate between machine level representations and demonstrate how data is represented in computers.
    3. Identify, evaluate and use information sources to support the practice of programming, including APIs, tutorials and documentation.
    4. Determine and compare the runtime complexity of common searching and sorting techniques and their implementations – both iterative and recursive.
    5. Identify and apply searching and sorting techniques (linear and binary search, selection, insertion, merge, quick, bucket sorts).
    6. Identify and apply basic data structures: linked list, stack, queue, qraph, tree (ordered, binary, balanced).
    7. Design, implement and test solutions to problems selecting appropriate data structures and basic algorithmic techiques (brute force, divide and conquer, transform and conquer, greedy).
    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. 1,2,3,4,5,6,7
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 3
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1,6,7
    Skills of a high order in interpersonal understanding, teamwork and communication. 1,3
    A proficiency in the appropriate use of contemporary technologies. 1,2,3,4,5,6,7
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 3
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 3
  • Learning Resources
    Required Resources
    There is no required text. The course will make use of various freely available resources.
    Recommended Resources
    If you prefer to have a textbook for reference, we recommend Walter Savitch, "Problem Solving with C++ 8th ed", Addison-Wesley, 2012, ISBN-10:0132162733, ISBN-13:9780132162739
    Online Learning
    The School of Computer Science uses a variety of e-learning tools to support traditional face-to-face lectures, tutorials and workshops.  These tools provide access to various features including announcements, course materials, discussion boards and assessments for each course of study.  Online learning resources can be accessed by selecting your course from
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course is offered in blended learning mode with the face-to-face component offered as intensives.

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

    As a guide, a 3 unit course comprises a total of 144 hours work (this includes face-to-face contact, any online components, and self directed study).
    Learning Activities Summary
    1. Class hierarchies, inheritance, multiple inheritance, polymorphism, namespaces.
    2. Recursion, evaluating algorithms memory and runtime, algorithmic approaches to searching (linear and binary), sorting.
    3. Programming data structures: stacks, queues, linked lists.
    4. Trees, algorithmic strategies, review.
    Specific Course Requirements
    Students are expected to complete work and practice programming outside of intensive times. The lab at NAAEC has all the required resources for the course.
  • 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
    An overview of the course assessment appears in the following Table. Details appear in the following section:

    During practical lab sessions, you are encouraged to collaborate with your classmates. This extends to discussing ideas, alternatives, possible solutions and questions about material discussed in class. It does not permit copying of code from classmates. All code you submit must be your own. Practical exams are individual and you must not consult classmates in developing your solutions. 

    Assessment No. Form of Assessment/ Collaborative Task Indiv or Collab   Weighting  Learning objective covered (see 2.1 for detail)

    1 Practicals Collaborative 20% 1,2,3,4,5 

    2 Prac Exams Individual   20%  1,2,3,4 

    3 Final Theory Exam Individual   30%  1,2,3 

    4 Final Practical Exam Individual   30%  4 

    Total   100%
    Assessment Related Requirements
    Students must achieve a minimum of 40% of available marks in the four assessment components given above and must achieve a total mark of at least 50% to be eligible to pass the course.

    Students must complete all course assessment requirements and must attend lectures to be eligible to pass the course.

    Course results are subject to moderation by the School of Computer Science Assessment coordinator.
    Assessment Detail
    Assessment 1: Practicals
    Weighting: 20%
    Due Dates: please note schedule on course website. Some practicals will be undertaken during class time and some must be completed in between intensive sessions.
    Submission Details: Online through course website

    Task: Design, test and implement solutions to the practical problems.

    Scope: Each practical will assess your understanding of the course material covered during the prior session. Practical work is both summative (assessing your understanding) and formative (used to give you feedback and help you prepare for practical exams) - see schedule above. Students are encouraged to work collaboratively on practicals. Final submission must be the student’s own work, unless specified otherwise by the lecturer. ie students are encouraged to share ideas, but should not share actual code.

    Criteria by which your assignment will be marked: Practicals will be assessed on design, functionality, testing and program style.

    Learning objectives with this assessment (refer to section 2.1): 1,2,3,4,5

    Assessment 2: Practical Exams
    Weighting: 20%
    Due Dates: please note schedule on course website. Practical exams will be held during intensives. Submission Details: Online through course website

    Task: Design, test and implement solutions to the practical problems.

    Scope: Practical exams are summative assessment and cover all topics up to the time of the practical exam - see schedule.

    Criteria by which your assignment will be marked: Practical exams will be assessed on design, functionality, testing and program style.

    Learning objectives with this assessment (refer to section 2.1): 1,2,3,

    Assessment 3: Final Theory Exam
    Weighting: 30%
    Due Dates: The final theory exam will be scheduled during the examination period
    Submission Details: Written Exam

    Task: The theory exam assesses students depth of knowledge of programming constructs in general, tradeoffs of memory and cpu use in algorithms and data representation.

    Scope: 60 minute written exam. Covers material from all of course.

    Criteria by which your assignment will be marked: Correctness of answers.

    Learning objectives with this assessment (refer to section 2.1): 1,2,3 Page 8 of 11

    Assessment 4: Final Practical Exam
    Weighting: 30%
    Due Dates: The final practical exam will be held during the final intensive
    Submission Details: Lab Exam

    Task: Design, test and implement solutions to the practical problems.

    Scope: 60 minute lab exam. Covers material from all of course.

    Criteria by which your assignment will be marked: Practicals will be assessed on design, functionality, testing and program style.

    Learning objectives with this assessment (refer to section 2.1): 4
    All practical based assignments must be submitted via the web submission system. Please see the course website for links.

    There are a few points to note about the submission of assignments:

    · Assignment Submission: Assignments should not be emailed to the instructor but should be lodged via the web submission system. Note that assignments may be processed via online plagiarism prevention tools.

    · Backup Copy of Assignments: All practical work is to be stored in your SVN repository. Failure to use the repository and any subsequent loss of work will not be grounds for extensions.

    · Extensions of Time: Any request for an extension of time for the submission of an assignment should be made well before the due date of the assignment to the Course Coordinator. Normally, extensions will only be granted for a maximum of two weeks from the original assignment submission date. Extensions will only be granted in cases of genuine extenuating circumstances and proof, such as a doctor’s certificate, is required. Requests for extensions must be sent to the Course Coordinator using the form Note that supporting documentation MUST be submitted with the form.

    · Failure to submit: Failure to submit an assignment on time or by the agreed extension deadline will result in penalties and may incur a fail grade. Note that a late penalty of 25% of the total available marks for that assessment item will be incurred each day an assignment is handed in late. Assignments handed in after 4 days from the due submission date will receive a mark of 0% even if a 100% mark is granted for the work. 
    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.

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