COMP SCI 2203 - Problem Solving & Software Development
North Terrace Campus - Semester 2 - 2018
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)Tutors
Samuel Lewis, Yu Liu
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 OutcomesOn successful completion of this course students will be able to:
1 Recognise the broad algorithmic category to which a problem belongs, e.g. brute-force, recursive, dynamic programming, divide-and-conquer 2 Formulate a short solution sketch to a programming problem 3 Demonstrate the ability to quickly assess the efficiency of a proposed solution with respect to expected input data 4 Build your own process of design, testing, experimentation and programming 5 Apply your own process to the timely production of solutions to a range of programming problems 6 Complete practice examples with reasonable frequency in a timely manner 7 Demonstrate the ability to relflect in detail on your own programming performance and software development processes in a frequent, timely and useful manner 8 Designing and/or selecting new practice exercises in to address gaps in performance highlighted by your reflections
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: 1.2 1.3 1.5 1.6 2.1 2.2 2.3 2.4 3.3 3.4 3.5
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, defuse 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.
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 Task Weighting (%) Individual/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Programming
12 Individual Summative 1. 2. 3. 4. 5. 6. 7. 8. 1.1 1.2 2.6 3.1 3.2 4.1 4.2 4.3 Deliberate practice 25 Individual Formative Weeks 2-12 Min 20% 1. 2. 4. 5. 6. 7. 8. 1.1 1.2 2.6 3.1 3.2 4.1 4.2 4.3 Reflection surveys 8 Individual Summative Weeks 2-12 1. 8. 1.1 1.2 2.6 3.1 3.2 4.1 4.2 4.3 Laboratory sessions 8 Group Formative Weeks 1-12 1. 2. 3. 7. 8. 1.1 1.2 2.1 2.2 2.3 2.4 2.6 3.1 3.2 4.1 4.2 4.3 Lecture quizzes 2 Individual Formative Weeks 2-12 1. 8. 1.1 1.2 2.1 2.2 2.6 Practical exams 45 Individual Summative Weeks 4,8,12 1. 2. 3. 5. 6. 1.1 1.2 Total 100
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.
**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:
1. Problem Solving1.1 Abstraction1.2 Design
2. Professional Knowledge2.1 Ethics2.2 Professional expectations2.3 Teamwork concepts & issues2.4 Interpersonal communications2.5 Societal issues2.6 Understanding of ICT profession
3. Technology resources3.1 Hardware & Software3.2 Data & information3.3 Networking
4. Technology Building4.1 Programming4.2 Human factors4.3 Systems development4.4 Systems acquisition
5. ICT Management5.1 IT governance & organisational5.2 IT project management5.3 Service management5.4 Security management
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 - on weeks 4,8 and 12 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): 12%
- Deliberate Practice (summative assessment): 25%
- Continous Assessment
- Consists of Practice Exericise marks and Journal Entries
- Partially automatically assessed with manual checking of Journal entries.
- Mark collated at the end of each week for 10 weeks - first collation is at end of week 2
- Laboratory sessions (formative assessment): 8 - sessions totaling 8%
- Any session that is not an exam (or diagnostic exam)
- 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 8%
- In Lecture Quizzes: held during up to 4 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.
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
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- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
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- Coursework Academic Programs Policy
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- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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