Submission of assignments will be for the most part via iLearn; presentations associated with assignments will be given and assessed during class time.

For policy on late assignments, see Policies and Procedures.

CLASSES

Each week of COMP777 has a three-hour session which is a mix of lecture (typically for the first two hours), tutorial and practical session. For details of days, times and rooms, consult the University timetables webpage (http://www.timetables.mq.edu.au).

REQUIRED AND RECOMMENDED TEXTS AND/OR MATERIALS

There is no set text for the unit.  We will be providing pointers to reading material over the course of the unit.

The unit has some parallels with the freely available Software Carpentry course.  We'll be using those resources as supplementary ones for the unit.

UNIT WEBPAGE AND TECHNOLOGY USED AND REQUIRED

Web Home Page

COMP777 will make extensive use of the iLearn course management system, including for delivery of class materials, discussion boards, submission of work and access to marks and feedback. Students should check the iLearn site (https://ilearn.mq.edu.au) regularly for unit updates.

Questions and general queries regarding the content of this unit, its lectures or mixed classes, or its assignments should be posted to the discussion boards on the COMP777 iLearn site. In particular, any questions which are of interest to all students in this unit should be posted to one of these discussion boards, so that everyone can benefit from the answers. Questions of a private nature should be directed to the unit teaching staff.

Technology Used and Required

The practical work in this unit involves programming in the Python language (http://www.python.org/) which is widely used for the sorts of scripting purposes covered in this unit. Python can be downloaded free of charge for a range of operating systems from the Python website.

The unit will use Python 2.7.

Note that as this is a master's unit, there will be some self-directed learning.  We do not expect that you will know Python before the unit starts, but will pick up the necessary elements in the first few weeks of the unit; we will give pointers to resources for learning Python, will include snippets of Python in lecture notes where relevant to computational experiments, and will have set exercises for discussion during class.

The unit will also use Amazon's Mechanical Turk for data gathering, and a range of other tools.

COMP777 will be graded according to the following general descriptions of the letter grades as specified by Macquarie University.

• High Distinction (HD, 85-100): provides consistent evidence of deep and critical understanding in relation to the learning outcomes. There is substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critical evaluation of problems, their solutions and their implications; creativity in application as appropriate to the discipline.

• Distinction (D, 75-84): provides evidence of integration and evaluation of critical ideas, principles and theories, distinctive insight and ability in applying relevant skills and concepts in relation to learning outcomes. There is demonstration of frequent originality in defining and analysing issues or problems and providing solutions; and the use of means of communication appropriate to the discipline and the audience.

• Credit (Cr, 65-74): provides evidence of learning that goes beyond replication of content knowledge or skills relevant to the learning outcomes. There is demonstration of substantial understanding of fundamental concepts in the field of study and the ability to apply these concepts in a variety of contexts; convincing argumentation with appropriate coherent justification; communication of ideas fluently and clearly in terms of the conventions of the discipline..

• Pass (P, 50-64): provides sufficient evidence of the achievement of learning outcomes. There is demonstration of understanding and application of fundamental concepts of the field of study; routine argumentation with acceptable justification; communication of information and ideas adequately in terms of the conventions of the discipline. The learning attainment is considered satisfactory or adequate or competent or capable in relation to the specified outcomes.

• Fail (F, 0-49): does not provide evidence of attainment of learning outcomes. There is missing or partial or superficial or faulty understanding and application of the fundamental concepts in the field of study; missing, undeveloped, inappropriate or confusing argumentation; incomplete, confusing or lacking communication of ideas in ways that give little attention to the conventions of the discipline.

The standards of achievement that will be used to assess each of the assessment tasks with respect to the letter grades are as follows.

Learning Outcome 1: Apply a knowledge of programming in carrying out research tasks in a relevant discipline.

Learning Outcome 2: Articulate clearly a coherent argument in written and oral form to a variety of audiences.

Learning Outcome 3: Apply a knowledge of the principles of ethical conduct of research, including an examination of the role of open access to data and publications.

Learning Outcome 4: Recognise the requirements for open access dictated by journals and granting bodies in the appropriate research area.

Learning Outcome 5: Demonstrate best practice in document preparation and management in research.

These assessment standards will be used to give a numeric mark out of 100 to each assessment submission during marking. The mark will correspond to a letter grade for that task according to the University guidelines. The final raw mark for the unit will be calculated by combining the marks for all assessment tasks according to the percentage weightings shown in the assessment summary. 

We will look at your overall performance on all assessments when determining your final grade. A total raw mark of at least 50% in each of the assessment tasks will be sufficient to pass the unit. Students who do not meet this cut-off will be examined on a case-by-case basis.

On occasion your raw mark for the unit may not be the same as the Standardised Numeric Grade (SNG) which you receive as the final result. Under University Senate guidelines, raw marks may be scaled to ensure that there is a degree of comparability across the university, so that units with the same past performances of their students should achieve similar results.