Personalized exercises¶

Personalized exercises contain some parts that are not the same for every student. Usually, the structure of the exercise is the same for everyone, but, for example, some input data is different in each instance of the exercise. The instances are usually randomly generated. Each student is assigned one instance and multiple students may be assigned the same instance. The instance assigned to the student remains the same even after refreshing the exercise page.

A personalized exercise defines a generator program that generates N instances to the exercise. The instances are generated before the start of the course. The mooc-grader CLI command python manage.py pregenerate_exercises runs the generator programs and saves the instance files in the correct directory under the mooc-grader. In theory, the instance files could also be created by some other way in the correct location. The CLI command has some arguments to modify its behaviour: they are printed with the --help argument. The run-mooc-grader container (used in local testing and development) should automatically call the generators if there are any personalized exercises in the course.

An instance contains some files that the grader program can use while grading (the implementation of the exercise may decide freely how those instance files are used). The instance files may also be used to add content to the exercise description shown to the student. A student is connected to one instance (the choice feels random but is based on their user IDs). It is also possible to force the instance to change for the student, e.g., after every five submissions (“you get five attempts before you must start over”). That kind of regeneration of the instance is disabled by default.

Example exercises¶

Here we have two very simple example personalized exercises. Their graders work in containers and the exercises define generator programs that create random instances.

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Personalized exercises¶

Example exercises¶