Remarks on the Grading System and Collaboration Policy

This guide was developed around 2006 by Dept. of Computing Science Curriculum Committee, Chaired by H. James Hoover, Associate-Chair Undergraduate.

We created this guide as a way of aligning student, faculty, and institutional expectations with respect to grading and collaboration. The grading part enables professors to explain why they assigned a particular grade, and students to understand or even appeal their grade. The collaboration part provides a standard set of terms that makes it clear what kinds of collaboration is possible in a course.

For additional information see the current Compouting Science course policies.

Grading System

Your final grade in a course will be based on the instructor's interpretation of the grading system as defined in the University Calendar's Academic Regulations. Instructors do not necessarily use a pre-defined function of your final mark to compute your final grade. We always use our judgement of how the class final marks reflect mastery of the course material. We believe that this produces a fair and consistent evaluation of students, and our extensive past experience supports this.

There need not be a direct linear numerical correspondence between the percentage marks you obtain during assessment, and the final grade in the course. The marks on the various components are just measures that contribute to the final judgement by the professor, and that is why a simple "average mark" does not fully reflect how much material a student has mastered.

Here is roughly how we interpret the descriptors associated with the letter grades for undergraduate students. The interpretation is also affected by the level of the course. An introductory course has a different notion of original thinking than does a senior level advanced topics course.

Letter	Descriptor	Interpretation
A-, A, A+	Excellent	Consistently original thinking that extends the material, demonstrated depth and breadth in the material, ability to integrate material with other subjects, ability to analyse and synthesize material at various levels of abstraction.
B-, B, B+	Good	Like an A, but not consistent over time, or weak in a specific area.
C-, C, C+	Satisfactory	Understand the core material but not its subtleties, can apply it to simple situations on own and to more complex situations with hints, evidence that the material has changed the way of thinking.
D+	Poor	Understand some of the core material but not its subtleties, can apply it to simple situations but often needs assistance, evidence that the material has had some change on the way of thinking.
D	Minimal Pass	Shows some understanding of parts of the material, cannot apply it without some direction, little evidence that the material has changed the way of thinking.
F	Failure	Little evidence of understanding of even the surface issues, poor analysis and synthesis, inability to apply the material.

Here is the conversion table we use at the U of A for computing your GPA:

Letter	A+	A	A-	B+	B	B-	C+	C	C-	D+	D	F
GPV	4.0	4.0	3.7	3.3	3.0	2.7	2.3	2.0	1.7	1.3	1.0	0.0

Also see 61.6 University of Alberta Marking and Grading Guidelines.

Collaboration Policy

Version 1.7, 2012-08-01

This policy outlines the various kinds of collaboration that you are permitted when working on the assignments for a course.

Collaboration on assignments is encouraged. Recent studies show that pair-programming is a very effective way for students to master computing science concepts. But when collaborating, you must always properly acknowledge the sources you used and people you worked with. This is the most effective way to avoid plagiarism.

The rules for working with other students vary between courses and assignments within each course. So that you know what rules to follow, each deliverable (assignment, paper, program, presentation, etc.) in the course should be identified as having one of the following choices of collaboration model:

1. Solo Effort - you are not to talk to anyone else. All sources you used must be cited.

2. Consultation - you can talk to anyone else, but you must write up the solution on your own, and acknowledge who you talked to. All sources used must be cited. Details may vary from course to course, so it is important to understand what kind of consultation is allowed. See the next section for an example.

3. Full Collaboration - you can work with other students as permitted, you must identify your partners, and all sources used must be cited. Depending on the nature of the deliverable, the students you are permitted to work with may be restricted to those in your lab, or your lecture section. Also, depending on how your lectures and labs are organized for marking, you may be permitted to submit only one copy of the deliverable.

4. Teamwork - you are working in a team, the particular rules for the team depend on the nature of the course. Teams generally persist for the duration of the course, and will have one or more self-assessments in addition to deliverables. Any specific further details on the collaboration will be provided in the description of the deliverable.

Regardless of the collaboration method allowed, you must always properly acknowledge the sources you used and people you worked with. Your professors reserve the right to give you an exam (oral, written, or both) to determine the degree that you participated in the making of the deliverable, and how well you understand what was submitted. For example, you may be asked to explain any code that was submitted and why you choose to write it that way. This may impact the mark that you receive for the deliverable.

Note that this potential additional questioning about your deliverable is part of the assessment process, both summative (for marks) and formative (for feedback to you and us). It is intended to give us additional information about what you have learned. So, whenever you submit a deliverable, especially if you collaborate, you should be prepared for an individual inspection/walkthrough in which you explain what every line of your code, assignment, design, documentation etc. does and why you choose to write it that way.

Example of Consultation Collaboration in a Programming Course (CMPUT 201)

This course uses a Consultation Collaboration model as follows:

1. You can freely discuss the concepts and solutions with your 201 classmates.

2. Limit discussions among students to an informal verbal level. We do NOT allow exchanging any written text, code, or giving detailed step by step verbal advice.

3. Limit discussion to be among students taking the current 201 course, not students who took 201 in earlier terms or other students. Collaboration is a two-way process that benefits both sides.

4. Individually develop your own solution for assignments and exercises. Submit only your own work for evaluation.

5. Do not give other students access to your solutions and do not seek access to other's solutions. This is considered plagiarism.

6. All sources of information used, e.g., books, websites, students you talked to etc., must be cited in your README file for each assignment. If student A cites student B, then B should also cite A as collaborator.

7. Study the Computing Science policy regarding Academic Integrity, Collaboration Policy and Plagiarism and make sure that you are familiar with the definition of plagiarism and cheating in the Code of Student Behaviour.

8. We will use powerful automated tools such as MOSS to compare submitted work from current as well as previous courses and flag potential cases of plagiarism.

9. All suspected cases of plagiarism will be forwarded to the Dean's office and thoroughly investigated. Receiving a low mark for work not completed is a far superior alternative to this process and its possible long-term consequences to your career.