COMP SCI 2203 - Problem Solving & Software Development
North Terrace Campus - Semester 2 - 2017
General Course Information
Course Code COMP SCI 2203 Course Problem Solving & Software Development Coordinating Unit School of Computer Science Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 1103, COMP SCI 1203 or COMP SCI 2103 Course Description This course presents students with open-ended and complex programming problems that focus on developing their software design and implementation skills. The course will also introduce software engineering principles, and particularly approaches to software quality.
Course Coordinator: Dr Cruz IzuLecturers
Cruz Izu (email@example.com)Brad Alexander (firstname.lastname@example.org)
Gavin Meredith, Chen Zhang
The full timetable of all activities for this course can be accessed from Course Planner.Contact hours for this course consist of a two hour lecture on Monday and a two hour laboratory session on Thursday.
Practical sessions will be devoted to programming practice and practical exams. Lectures will mix presentation of concepts with programming and problem-solving exercises.
Course Learning Outcomes
- An ability to recognise the broad algorithmic category to which a problem belongs, e.g. brute-force, recursive, dynamic programming, divide-and-conquer
- Skills in formulating a short solution sketch to a programming problem.
- Ability to quickly assess the efficiency of a proposed solution with respect to expected input data
- The ability to build your own process of design, testing, experimentation and programming.
- The ability to apply your own process to the timely production of solutions to a range of programming problems.
- Skills in completing practice examples with reasonable frequency in a timely manner
- Skills in relflecting in detail on your own programming performance and software development processes in a frequent, timely and useful manner
- Skills in designing and/or selecting new practice exercises in to address gaps in performance highlighted by your reflections.
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)
1-6 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
1-7 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
3-5 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
Recommended ResourcesIn addition to the resources above the following are likely to prove very useful:
The topcoder algorithms competition website: http://community.topcoder.com/tc including the algorithms tutorials: http://community.topcoder.com/tc?module=Static&d1=tutorials&d2=alg_index
We also recommend the following reference: "The Algorithm Design Manual", Steven S. Skeina, Second Edition, Springer. This book is a great (and very readable) reference summarising a broad range of common algorithms as well as decscribing common algorithmic categories and approaches to solving computational problems.
Additional links for program development and practice techniques will be added to course website before and during the semester.
Online LearningThe course forums (and other online resources) can be accessed via the course forums at:
Learning & Teaching Activities
Learning & Teaching ModesLectures, Lecture Exercises, Out-of-class practice, Laboratory sessions, Practical Exams.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The workload is approximately 12 hours per week during semester time. This consists of an average of 4 hours of contact time and the remainder is for out of class practice and working on assignments.
Learning Activities SummaryLearning activities will consist of:
- In-lecture exericess done alone and in groups - solving algorithmic problems - reflecting and improving on problem solving processes.
- Laboratory sessions solving algorthmic practice exercises - applying newly learned techniques - focusing on gaps in skills.
- Practical Exams - solving algorithmic problems in a timely manner
- Out-of-class-practice including reflection and design
- Programming Assignment(s).
We will also examine a range of broad algorithmic categories including some or all of:
brute force, recursion, dynamic programming, divide and conquer, graph algorithms
Problem solving processes includes some or all of:
- proposing and winnowing solutions
- estimating efficiency
- formulating test plans,
- problem decomposition,
- formulating hypotheses
- isolating effects
Specific Course RequirementsPart of the assessment of this course is the requirement that you complete practice exercises with some frequency and regularity. As such the course expects that you are able to engage in a small to moderate amount of daily effort to complete exercises and reflect on your practice. This frequency and regularity of practice and reflection forms a small but integral part of your assessment.
It is also expected that you attend lectures, laboratory sessions and practical exams.
Small Group Discovery ExperienceNot applicable to this course.
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 SummaryAssessment for this course consists of two main components
- Practical exams - done in laboratory sessions - counting for 45% of your marks.
- Other continuous assessment including:
- Programming Assignment - counting for 15% of your marks
- Deliberate Practice - counting for 20% of your marks
- Reflection Surveys - counting for 10% of your marks
- Lab Participation (including diagnostic exam) - counting for 8% of your marks
- Lecture quizzes - counting for 2% of your marks
Assessment Related RequirementsA minimum score of 40% is required in the deliberate practice section of the course.
Failure to achieve this score will result your course mark being capped at 44F with opportunity for additional assessment being awarded at the discretion of the school.
You are also expected to attend a minimum of 80% of laboratory session times (including prac exams). Application for exemptions based on medical and/or compassionate grounds must be made to the course coordinator.
- Three summative practical exams - roughly evenly spread through the rest of semester - 15% each.
- Each practical exam has 3 questions of graduated difficulty
- 50% for the first question answered, 35% for the second question answered, 15% for the third question answered.
- Questions are submitted to the automatic assessment system. Instant feedback is given. Multiple submissions are allowed. Partial marks can be granted.
- One Programming Assignment (summative assessment): 15%
- Deliberate Practice (summative assessment): 20%
- Continous Assessment
- Consists of Practice Exericise marks and Journal Entries
- Partially automatically assessed with manual checking of Journal entries.
- Mark is collated at the end of each week - first collation is at end of week 2
- Laboratory sessions (formative assessment): 8 - sessions totaling 8%
- Includes diagnostic exam in week 2.
- Marks for participation with group and participation in activities.
- Reflective Surveys (summative assessment): 4 questionaires - one after each practical exam and the diagnostic exam: total 10%
- In Lecture Quizzes: held during eight - randomly selected lecture sessions (formative): 2%
- Marks for participation
SubmissionPractical exams will be submitted via the web submission system.
Practice exercises and journal entries will be submitted via the web submission system.
Details of these will be announced in lectures and linked to the course forums.
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.
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