def on_start(submission_uuid):
"""
Creates a new student training workflow.
This function should be called to indicate that a submission has entered the
student training workflow part of the assessment process.
Args:
submission_uuid (str): The submission UUID for the student that is
initiating training.
Returns:
None
Raises:
StudentTrainingInternalError: Raised when an error occurs persisting the
Student Training Workflow
"""
try:
StudentTrainingWorkflow.create_workflow(submission_uuid)
except Exception as ex:
msg = (
"An internal error has occurred while creating the learner "
"training workflow for submission UUID {}".format(submission_uuid)
)
logger.exception(msg)
raise StudentTrainingInternalError(msg) from ex
def get_num_completed(submission_uuid):
"""
Get the number of training examples the student has assessed successfully.
Args:
submission_uuid (str): The UUID of the student's submission.
Returns:
int: The number of completed training examples
Raises:
StudentTrainingInternalError
Example usage:
>>> get_num_completed("5443ebbbe2297b30f503736e26be84f6c7303c57")
2
"""
try:
try:
workflow = StudentTrainingWorkflow.objects.get(submission_uuid=submission_uuid)
except StudentTrainingWorkflow.DoesNotExist:
return 0
else:
return workflow.num_completed
except DatabaseError as ex:
msg = (
"An unexpected error occurred while "
"retrieving the learner training workflow status for submission UUID {}"
).format(submission_uuid)
logger.exception(msg)
raise StudentTrainingInternalError(msg) from ex
def assess_training_example(submission_uuid, options_selected, update_workflow=True):
"""
Assess a training example and update the workflow.
This must be called *after* `get_training_example()`.
Args:
submission_uuid (str): The UUID of the student's submission.
options_selected (dict): The options the student selected.
Keyword Arguments:
update_workflow (bool): If true, mark the current item complete
if the student has assessed the example correctly.
Returns:
corrections (dict): Dictionary containing the correct
options for criteria the student scored incorrectly.
Raises:
StudentTrainingRequestError
StudentTrainingInternalError
Example usage:
>>> options_selected = {
>>> 'vocabulary': 'good',
>>> 'grammar': 'excellent'
>>> }
>>> assess_training_example("5443ebbbe2297b30f503736e26be84f6c7303c57", options_selected)
{'grammar': 'poor'}
"""
# Find a workflow for the student
try:
workflow = StudentTrainingWorkflow.objects.get(submission_uuid=submission_uuid)
# Find the item the student is currently working on
item = workflow.current_item
if item is None:
msg = (
"No items are available in the learner training workflow associated with "
"submission UUID {}"
).format(submission_uuid)
raise StudentTrainingRequestError(msg)
# Check the student's scores against the staff's scores.
corrections = item.check_options(options_selected)
# Mark the item as complete if the student's selection
# matches the instructor's selection
if update_workflow and not corrections:
item.mark_complete()
return corrections
except StudentTrainingWorkflow.DoesNotExist as ex:
msg = "Could not find learner training workflow for submission UUID {}".format(submission_uuid)
raise StudentTrainingRequestError(msg) from ex
except DatabaseError as ex:
msg = (
"An error occurred while comparing the learner's assessment "
"to the training example. The submission UUID for the learner is {}"
).format(submission_uuid)
logger.exception(msg)
raise StudentTrainingInternalError(msg) from ex
def get_training_example(submission_uuid, rubric, examples):
"""
Retrieve a training example for the student to assess.
This will implicitly create a workflow for the student if one does not yet exist.
NOTE: We include the rubric in the returned dictionary to handle
the case in which the instructor changes the rubric definition
while the student is assessing the training example. Once a student
starts on a training example, the student should see the same training
example consistently. However, the next training example the student
retrieves will use the updated rubric.
Args:
submission_uuid (str): The UUID of the student's submission.
rubric (dict): Serialized rubric model.
examples (list): List of serialized training examples.
Returns:
dict: The training example with keys "answer", "rubric", and "options_selected".
If no training examples are available (the student has already assessed every example,
or no examples are defined), returns None.
Raises:
StudentTrainingInternalError
Example usage:
>>> examples = [
>>> {
>>> 'answer': {
>>> 'parts': {
>>> [
>>> {'text:' 'Answer part 1'},
>>> {'text:' 'Answer part 2'},
>>> {'text:' 'Answer part 3'}
>>> ]
>>> }
>>> },
>>> 'options_selected': {
>>> 'vocabulary': 'good',
>>> 'grammar': 'poor'
>>> }
>>> }
>>> ]
>>>
>>> get_training_example("5443ebbbe2297b30f503736e26be84f6c7303c57", rubric, examples)
{
'answer': {
'parts': {
[
{'text:' 'Answer part 1'},
{'text:' 'Answer part 2'},
{'text:' 'Answer part 3'}
]
}
},
'rubric': {
"prompts": [
{"description": "Prompt 1"},
{"description": "Prompt 2"},
{"description": "Prompt 3"}
],
"criteria": [
{
"order_num": 0,
"name": "vocabulary",
"prompt": "How varied is the vocabulary?",
"options": options
},
{
"order_num": 1,
"name": "grammar",
"prompt": "How correct is the grammar?",
"options": options
}
],
},
'options_selected': {
'vocabulary': 'good',
'grammar': 'excellent'
}
}
"""
try:
# Validate the training examples
errors = validate_training_examples(rubric, examples)
if errors:
msg = (
"Training examples do not match the rubric (submission UUID is {uuid}): {errors}"
).format(uuid=submission_uuid, errors="\n".join(errors))
raise StudentTrainingRequestError(msg)
# Get or create the workflow
workflow = StudentTrainingWorkflow.get_workflow(submission_uuid=submission_uuid)
if not workflow:
raise StudentTrainingRequestError(
u"No learner training workflow found for submission {}".format(submission_uuid)
)
# Get or create the training examples
examples = deserialize_training_examples(examples, rubric)
# Pick a training example that the student has not yet completed
# If the student already started a training example, then return that instead.
next_example = workflow.next_training_example(examples)
return None if next_example is None else serialize_training_example(next_example)
except (InvalidRubric, InvalidRubricSelection, InvalidTrainingExample) as ex:
logger.exception(
u"Could not deserialize training examples for submission UUID {}".format(submission_uuid)
)
raise StudentTrainingRequestError(ex) from ex
except sub_api.SubmissionNotFoundError as ex:
msg = u"Could not retrieve the submission with UUID {}".format(submission_uuid)
logger.exception(msg)
raise StudentTrainingRequestError(msg) from ex
except DatabaseError as ex:
msg = (
u"Could not retrieve a training example for the learner with submission UUID {}"
).format(submission_uuid)
logger.exception(msg)
raise StudentTrainingInternalError(msg) from ex