COMP SCI 1010 - Puzzle Based Learning

North Terrace Campus - Semester 1 - 2018

The focus of this course is on getting students to think about framing and solving unstructured problems (those that are not encountered at the end of some textbook chapter). The general objective is to increase the student's mathematical awareness and problem-solving skills by discussing a variety of puzzles. The puzzle-based learning approach has a long tradition as the first mathematical puzzles were found in Sumerian texts that date back to around 2,500 BC The puzzles selected for the course satisfy most of the following criteria: a) Generality: educational puzzles explain some universal mathematical problem-solving principles; b) Simplicity: educational puzzles are easy to state and easy to remember; c) Eureka factor: educational puzzles often frustrate the problem-solver! Eventually a Eureka! moment is reached. The Eureka factor also implies that educational puzzles often have elementary solutions that are not obvious; d) Entertainment factor: educational puzzles are very entertaining! Such educational puzzles are used to illustrate basic concepts of critical thinking, mathematics, and problem-solving. The course presents some problem-solving rules and covers issues of understanding the problem and the role of intuition in problem-solving activities. Further, some mathematical problem-solving principles are discussed and elements of modelling, constraint-processing, optimization, probability, statistics, simulation, pattern recognition, and strategy are introduced.

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Course Details

Course Code	COMP SCI 1010
Course	Puzzle Based Learning
Coordinating Unit	Computer Science
Term	Semester 1
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	Y
Assumed Knowledge	SACE level 2 Maths Sciences
Assessment	Written exam, assignments

Course Staff

Course Coordinator: Professor Gustavo Carneiro

Course Timetable

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

Course Learning Outcomes

On successful completion of this course students will be able to:

1	Explain algorithms for massive data sets and methodologies in the context of data mining.
2	Demonstrate the ability to match various algorithms for particular classes of problems.
3	Apply and develop algorithms as a part of software development for mining big data.

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)	3
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	1-2
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	2-3
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	1-3
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	1-3

Learning Resources

Required Resources

There is the course textbook:

Puzzle-based Learning: Introduction to critical thinking, mathematics, and problem solving, Z Michalewicz & M Michalewicz, Hybrid Publishers Pty Ltd

Recommended Resources

Students are expected to attend lectures, collaborative sessions and also their supervised practical sessions. These practical sessions will be crucial to developing your understanding of the course material, and will provide access to additional assistance from practical supervisors

Online Learning

Copies of lecture notes, lecture recordings and additional resources will be provided online through the myuni page at . Discussion forums will also be made available on the this website. Students are expected to check the forums website frequently for announcements and new resources.

Learning & Teaching Modes

This course aims to introduce students to a range of fundamental skills that they will need as professional Engineers. The course will be taught through a combination of lectures and tutorials.

Many examples will be worked on during the tutorials. The tutorial sessions will require students to individually prepare solutions to set questions which can then be worked on and assessed during the session. The purpose of these tutorials is for students to apply the examples and theoretical concepts discussed in lectures.

Workload

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

Students are expected to attend all scheduled classes. In addition to the schedule contact hours, students are expected to spend 4-6 additional hours per week in preparation of assignment work, and reviewing lecture material.

Learning Activities Summary

Week	Date	Topic	Lecturer	Assignment	Tutorial
1	29/2 2/3	Introduction to PBL The problem: what are you after?	GC GC
2	7/3 9/3	Intuition: how good is it? Intuition: how good is it?	GC GC	Assignment 1 (due on 16/3)	Tutorial 1
3	14/3 16/3	Public Holiday Modelling: let’s think about the problem a bit more	GC GC	Assignment 1 (due on 23/3)	Tutorial 2
4	21/3 23/3	Modelling: let’s think about the problem a bit more Some mathematical principles	GC GC	Assignment 3 (due on 30/3)	Tutorial 3
5	28/3 30/3	Public Holiday Some mathematical principles	GC GC	Assignment 4 (due on 6/4)	Tutorial 4
6	4/4 6/4	Constraints: How old are my children? Challenge Day I	GC GC	Assignment 5 (due on 27/4)	Tutorial 5
	11/4	Semester break
	18/4	Semester break
7	25/4 27/4	Optimization: what is the best arrangement? Optimization: what is the best arrangement?	GC GC	Assignment 6 (due on 4/5)	Tutorial 6
8	2/5 4/5	Probability: coins, dices, box and bears Probability: coins, dices, box and bears	GC GC	Assignment 7 (due on 11/5)	Tutorial 7
9	9/5 11/5	Statistically speaking Statistically speaking	GC GC	Assignment 8 (due on 18/5)	Tutorial 8
10	16/5 18/5	Let’s simulate Let’s simulate	GC GC	Assignment 9 (due on 25/5)	Tutorial 9
11	23/5 25/5	Pattern recognition: what is next? Pattern recognition: what is next?	GC GC	Assignment 10 (due on 1/6)	Tutorial 10
12	30/5 1/6	Course Review Challenge Day II	GC GC		Tutorial 11

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 Summary

Assessment Task	Weighting (%)	Individual/ Group	Formative/ Summative	Due (week)*	Hurdle criteria	Learning outcomes	CBOK Alignment**
Weekly assignments	37	Individual	Formative / Summative	Weeks 3-12		1. 2. 3.	1.1 1.2 2.1 2.2 2.4
Tutorial participation	3	Individual	Formative	Weeks 2-12		3.	1.1 1.2 2.2 2.3 2.4
Exam	60	Individual	Summative	Week 12	Min 40%	1. 2. 3.	1.1 1.2 2.2 2.4
Total	100

* The specific due date for each assessment task will be available on MyUni.

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 Solving

1.1 Abstraction

1.2 Design

2. Professional Knowledge

2.1 Ethics

2.2 Professional expectations

2.3 Teamwork concepts & issues

2.4 Interpersonal communications

2.5 Societal issues

2.6 Understanding of ICT profession

3. Technology resources

3.1 Hardware & Software

3.2 Data & information

3.3 Networking

4. Technology Building

4.1 Programming

4.2 Human factors

4.3 Systems development

4.4 Systems acquisition

5. ICT Management

5.1 IT governance & organisational

5.2 IT project management

5.3 Service management

5.4 Security management

Assessment Related Requirements

Students must obtain at least 40% in the written exam component, and 50% overall, to pass the course.

Assessment Detail

Written exam: this will be a 2 hour open book exam. Questions will test the students’ understanding of concepts presented throughout the course, and their ability to put them to use to solve problems.

Tutorial and class participation: intended to assess the student’s knowledge in practical application of the concepts taught in lectures, specifically in designing and developing puzzle solutions.

Assignments: both formative and summative and extend the work done in the tutorial sessions. Assignments are used to help assess whether the required graduate attributes are being developed. Written feedback will be provided for some of the assessment work. Assignments are due one week after they are released.

Assessment	Type	Proportion of that Assessment	Due Week	Learning Objectives					CBOK Mappping*
					Problem Solving Abstraction	Problem Solving Design	Ethics	Professionalism	Teamwork concepts	Interpersonnal Communications	Societal Issues	HistoryandStatus of Discipline
Assignment 1	Formative and Summative	10%	week3	1,2,3	5	5	3	3		3
Assignment 2	Formative and Summative	10%	week4	1,2,3	5	5	3	3		3
Assignment 3	Formative and Summative	10%	week5	1,2,3	5	5	3	3		3
Assignment 4	Formative and Summative	10%	week6	1,2,3	5	5	3	3		3
Assignment 5	Formative and Summative	10%	week7	1,2,3	5	5	3	3		3
Assignment 6	Formative and Summative	10%	week8	1,2,3	5	5	3	3		3
Assignment 7	Formative and Summative	10%	week9	1,2,3	5	5	3	3		3
Assignment 8	Formative and Summative	10%	week10	1,2,3	5	5	3	3		3
Assignment 9	Formative and Summative	10%	week11	1,2,3	5	5	3	3		3
Assignment 10	Formative and Summative	10%	week12	1,2,3	5	5	3	3		3
Tutorial and Class Participation	Formative	100%	NA	3	5	5		3	3	3
Written Exam	Summative	100%	Exam Period	1,2,3	5	5		3		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).

Submission

Practical exercises will be assessed during the tutorial sessions.

Assignments will be submitted online, please refer to each assignment description for details.

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 .

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 (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.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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鈥檚 disciplinary procedures.

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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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