def test_grade_essay_feedback_only_criterion(self):
# Modify the rubric to include a feedback-only criterion
# (a criterion with no options, just written feedback)
rubric = copy.deepcopy(RUBRIC)
rubric['criteria'].append({
'name': 'feedback only',
'prompt': 'feedback',
'options': []
})
# Train classifiers for the rubric
train_classifiers(rubric, self.CLASSIFIER_SCORE_OVERRIDES)
# Schedule a grading task and retrieve the assessment
ai_api.on_init(self.submission_uuid, rubric=rubric, algorithm_id=ALGORITHM_ID)
assessment = ai_api.get_latest_assessment(self.submission_uuid)
# Verify that the criteria with options were given scores
# (from the score override used by our fake classifiers)
self.assertEqual(assessment['parts'][0]['criterion']['name'], u"vøȼȺƀᵾłȺɍɏ")
self.assertEqual(assessment['parts'][0]['option']['points'], 1)
self.assertEqual(assessment['parts'][1]['criterion']['name'], u"ﻭɼค๓๓คɼ")
self.assertEqual(assessment['parts'][1]['option']['points'], 2)
# Verify that the criteria with no options (only feedback)
# has no score and empty feedback
self.assertEqual(assessment['parts'][2]['criterion']['name'], u"feedback only")
self.assertIs(assessment['parts'][2]['option'], None)
self.assertEqual(assessment['parts'][2]['feedback'], u"")
# Check the scores by criterion dict
score_dict = ai_api.get_assessment_scores_by_criteria(self.submission_uuid)
self.assertEqual(score_dict[u"vøȼȺƀᵾłȺɍɏ"], 1)
self.assertEqual(score_dict[u"ﻭɼค๓๓คɼ"], 2)
self.assertEqual(score_dict['feedback only'], 0)
def test_grade_essay_all_feedback_only_criteria(self):
# Modify the rubric to include only feedback-only criteria
rubric = copy.deepcopy(RUBRIC)
for criterion in rubric['criteria']:
criterion['options'] = []
# Train classifiers for the rubric
train_classifiers(rubric, {})
# Schedule a grading task and retrieve the assessment
ai_api.on_init(self.submission_uuid,
rubric=rubric,
algorithm_id=ALGORITHM_ID)
assessment = ai_api.get_latest_assessment(self.submission_uuid)
# Verify that all assessment parts have feedback set to an empty string
for part in assessment['parts']:
self.assertEqual(part['feedback'], u"")
# Check the scores by criterion dict
# Since none of the criteria had options, the scores should all default to 0
score_dict = ai_api.get_assessment_scores_by_criteria(
self.submission_uuid)
self.assertItemsEqual(score_dict, {
u"vøȼȺƀᵾłȺɍɏ": 0,
u"ﻭɼค๓๓คɼ": 0,
})
def test_get_assessment_scores_by_criteria(self):
ai_api.on_init(self.submission_uuid, rubric=RUBRIC, algorithm_id=ALGORITHM_ID)
# Verify that we got the scores we provided to the stub AI algorithm
assessment = ai_api.get_latest_assessment(self.submission_uuid)
assessment_score_dict = ai_api.get_assessment_scores_by_criteria(self.submission_uuid)
for part in assessment['parts']:
criterion_name = part['option']['criterion']['name']
expected_score = self.CLASSIFIER_SCORE_OVERRIDES[criterion_name]['score_override']
self.assertEqual(assessment_score_dict[criterion_name], expected_score)
def test_get_assessment_scores_by_criteria(self):
ai_api.on_init(self.submission_uuid,
rubric=RUBRIC,
algorithm_id=ALGORITHM_ID)
# Verify that we got the scores we provided to the stub AI algorithm
assessment = ai_api.get_latest_assessment(self.submission_uuid)
assessment_score_dict = ai_api.get_assessment_scores_by_criteria(
self.submission_uuid)
for part in assessment['parts']:
criterion_name = part['option']['criterion']['name']
expected_score = self.CLASSIFIER_SCORE_OVERRIDES[criterion_name][
'score_override']
self.assertEqual(assessment_score_dict[criterion_name],
expected_score)
def test_grade_essay_feedback_only_criterion(self):
# Modify the rubric to include a feedback-only criterion
# (a criterion with no options, just written feedback)
rubric = copy.deepcopy(RUBRIC)
rubric['criteria'].append({
'name': 'feedback only',
'prompt': 'feedback',
'options': []
})
# Train classifiers for the rubric
train_classifiers(rubric, self.CLASSIFIER_SCORE_OVERRIDES)
# Schedule a grading task and retrieve the assessment
ai_api.on_init(self.submission_uuid,
rubric=rubric,
algorithm_id=ALGORITHM_ID)
assessment = ai_api.get_latest_assessment(self.submission_uuid)
# Verify that the criteria with options were given scores
# (from the score override used by our fake classifiers)
self.assertEqual(assessment['parts'][0]['criterion']['name'],
u"vøȼȺƀᵾłȺɍɏ")
self.assertEqual(assessment['parts'][0]['option']['points'], 1)
self.assertEqual(assessment['parts'][1]['criterion']['name'],
u"ﻭɼค๓๓คɼ")
self.assertEqual(assessment['parts'][1]['option']['points'], 2)
# Verify that the criteria with no options (only feedback)
# has no score and empty feedback
self.assertEqual(assessment['parts'][2]['criterion']['name'],
u"feedback only")
self.assertIs(assessment['parts'][2]['option'], None)
self.assertEqual(assessment['parts'][2]['feedback'], u"")
# Check the scores by criterion dict
score_dict = ai_api.get_assessment_scores_by_criteria(
self.submission_uuid)
self.assertEqual(score_dict[u"vøȼȺƀᵾłȺɍɏ"], 1)
self.assertEqual(score_dict[u"ﻭɼค๓๓คɼ"], 2)
self.assertEqual(score_dict['feedback only'], 0)
def test_grade_essay_all_feedback_only_criteria(self):
# Modify the rubric to include only feedback-only criteria
rubric = copy.deepcopy(RUBRIC)
for criterion in rubric['criteria']:
criterion['options'] = []
# Train classifiers for the rubric
train_classifiers(rubric, {})
# Schedule a grading task and retrieve the assessment
ai_api.on_init(self.submission_uuid, rubric=rubric, algorithm_id=ALGORITHM_ID)
assessment = ai_api.get_latest_assessment(self.submission_uuid)
# Verify that all assessment parts have feedback set to an empty string
for part in assessment['parts']:
self.assertEqual(part['feedback'], u"")
# Check the scores by criterion dict
# Since none of the criteria had options, the scores should all default to 0
score_dict = ai_api.get_assessment_scores_by_criteria(self.submission_uuid)
self.assertItemsEqual(score_dict, {
u"vøȼȺƀᵾłȺɍɏ": 0,
u"ﻭɼค๓๓คɼ": 0,
})
def grade_details(
self, submission_uuid, peer_assessments, self_assessment, example_based_assessment, staff_assessment,
is_staff=False
):
"""
Returns details about the grade assigned to the submission.
Args:
submission_uuid (str): The id of the submission being graded.
peer_assessments (list of dict): Serialized assessment models from the peer API.
self_assessment (dict): Serialized assessment model from the self API
example_based_assessment (dict): Serialized assessment model from the example-based API
staff_assessment (dict): Serialized assessment model from the staff API
is_staff (bool): True if the grade details are being displayed to staff, else False.
Default value is False (meaning grade details are being shown to the learner).
Returns:
A dictionary with full details about the submission's grade.
Example:
{
criteria: [{
'label': 'Test name',
'name': 'f78ac7d4ca1e4134b0ba4b40ca212e72',
'prompt': 'Test prompt',
'order_num': 2,
'options': [...]
'feedback': [
'Good job!',
'Excellent work!',
]
}],
additional_feedback: [{
}]
...
}
"""
criteria = copy.deepcopy(self.rubric_criteria_with_labels)
def has_feedback(assessments):
"""
Returns True if at least one assessment has feedback.
Args:
assessments: A list of assessments
Returns:
Returns True if at least one assessment has feedback.
"""
return any(
assessment.get('feedback', None) or has_feedback(assessment.get('individual_assessments', []))
for assessment in assessments
)
max_scores = peer_api.get_rubric_max_scores(submission_uuid)
median_scores = None
assessment_steps = self.assessment_steps
if staff_assessment:
median_scores = staff_api.get_assessment_scores_by_criteria(submission_uuid)
elif "peer-assessment" in assessment_steps:
median_scores = peer_api.get_assessment_median_scores(submission_uuid)
elif "example-based-assessment" in assessment_steps:
median_scores = ai_api.get_assessment_scores_by_criteria(submission_uuid)
elif "self-assessment" in assessment_steps:
median_scores = self_api.get_assessment_scores_by_criteria(submission_uuid)
for criterion in criteria:
criterion_name = criterion['name']
# Record assessment info for the current criterion
criterion['assessments'] = self._graded_assessments(
submission_uuid, criterion,
assessment_steps,
staff_assessment,
peer_assessments,
example_based_assessment,
self_assessment,
is_staff=is_staff,
)
# Record whether there is any feedback provided in the assessments
criterion['has_feedback'] = has_feedback(criterion['assessments'])
# Although we prevent course authors from modifying criteria post-release,
# it's still possible for assessments created by course staff to
# have criteria that differ from the current problem definition.
# It's also possible to circumvent the post-release restriction
# if course authors directly import a course into Studio.
# If this happens, we simply leave the score blank so that the grade
# section can render without error.
criterion['median_score'] = median_scores.get(criterion_name, '')
criterion['total_value'] = max_scores.get(criterion_name, '')
return {
'criteria': criteria,
'additional_feedback': self._additional_feedback(
staff_assessment=staff_assessment,
peer_assessments=peer_assessments,
self_assessment=self_assessment,
),
}
def render_grade_complete(self, workflow):
"""
Render the grade complete state.
Args:
workflow (dict): The serialized Workflow model.
Returns:
tuple of context (dict), template_path (string)
"""
# Peer specific stuff...
assessment_steps = self.assessment_steps
submission_uuid = workflow['submission_uuid']
example_based_assessment = None
self_assessment = None
feedback = None
peer_assessments = []
has_submitted_feedback = False
if "peer-assessment" in assessment_steps:
feedback = peer_api.get_assessment_feedback(submission_uuid)
peer_assessments = [
self._assessment_grade_context(asmnt)
for asmnt in peer_api.get_assessments(submission_uuid)
]
has_submitted_feedback = feedback is not None
if "self-assessment" in assessment_steps:
self_assessment = self._assessment_grade_context(
self_api.get_assessment(submission_uuid))
if "example-based-assessment" in assessment_steps:
example_based_assessment = self._assessment_grade_context(
ai_api.get_latest_assessment(submission_uuid))
feedback_text = feedback.get('feedback', '') if feedback else ''
student_submission = sub_api.get_submission(submission_uuid)
# We retrieve the score from the workflow, which in turn retrieves
# the score for our current submission UUID.
# We look up the score by submission UUID instead of student item
# to ensure that the score always matches the rubric.
# It's possible for the score to be `None` even if the workflow status is "done"
# when all the criteria in the rubric are feedback-only (no options).
score = workflow['score']
context = {
'score':
score,
'feedback_text':
feedback_text,
'student_submission':
student_submission,
'peer_assessments':
peer_assessments,
'self_assessment':
self_assessment,
'example_based_assessment':
example_based_assessment,
'rubric_criteria':
self._rubric_criteria_grade_context(peer_assessments,
self_assessment),
'has_submitted_feedback':
has_submitted_feedback,
'allow_file_upload':
self.allow_file_upload,
'file_url':
self.get_download_url_from_submission(student_submission)
}
# Update the scores we will display to the user
# Note that we are updating a *copy* of the rubric criteria stored in
# the XBlock field
max_scores = peer_api.get_rubric_max_scores(submission_uuid)
median_scores = None
if "peer-assessment" in assessment_steps:
median_scores = peer_api.get_assessment_median_scores(
submission_uuid)
elif "self-assessment" in assessment_steps:
median_scores = self_api.get_assessment_scores_by_criteria(
submission_uuid)
elif "example-based-assessment" in assessment_steps:
median_scores = ai_api.get_assessment_scores_by_criteria(
submission_uuid)
if median_scores is not None and max_scores is not None:
for criterion in context["rubric_criteria"]:
# Although we prevent course authors from modifying criteria post-release,
# it's still possible for assessments created by course staff to
# have criteria that differ from the current problem definition.
# It's also possible to circumvent the post-release restriction
# if course authors directly import a course into Studio.
# If this happens, we simply leave the score blank so that the grade
# section can render without error.
criterion["median_score"] = median_scores.get(
criterion["name"], '')
criterion["total_value"] = max_scores.get(
criterion["name"], '')
return ('openassessmentblock/grade/oa_grade_complete.html', context)
def render_grade_complete(self, workflow):
"""
Render the grade complete state.
Args:
workflow (dict): The serialized Workflow model.
Returns:
tuple of context (dict), template_path (string)
"""
# Peer specific stuff...
assessment_steps = self.assessment_steps
submission_uuid = workflow['submission_uuid']
example_based_assessment = None
self_assessment = None
feedback = None
peer_assessments = []
has_submitted_feedback = False
if "peer-assessment" in assessment_steps:
feedback = peer_api.get_assessment_feedback(submission_uuid)
peer_assessments = [
self._assessment_grade_context(asmnt)
for asmnt in peer_api.get_assessments(submission_uuid)
]
has_submitted_feedback = feedback is not None
if "self-assessment" in assessment_steps:
self_assessment = self._assessment_grade_context(
self_api.get_assessment(submission_uuid)
)
if "example-based-assessment" in assessment_steps:
example_based_assessment = self._assessment_grade_context(
ai_api.get_latest_assessment(submission_uuid)
)
feedback_text = feedback.get('feedback', '') if feedback else ''
student_submission = sub_api.get_submission(submission_uuid)
# We retrieve the score from the workflow, which in turn retrieves
# the score for our current submission UUID.
# We look up the score by submission UUID instead of student item
# to ensure that the score always matches the rubric.
# It's possible for the score to be `None` even if the workflow status is "done"
# when all the criteria in the rubric are feedback-only (no options).
score = workflow['score']
context = {
'score': score,
'feedback_text': feedback_text,
'student_submission': student_submission,
'peer_assessments': peer_assessments,
'self_assessment': self_assessment,
'example_based_assessment': example_based_assessment,
'rubric_criteria': self._rubric_criteria_grade_context(peer_assessments, self_assessment),
'has_submitted_feedback': has_submitted_feedback,
'allow_file_upload': self.allow_file_upload,
'allow_latex': self.allow_latex,
'file_url': self.get_download_url_from_submission(student_submission)
}
# Update the scores we will display to the user
# Note that we are updating a *copy* of the rubric criteria stored in
# the XBlock field
max_scores = peer_api.get_rubric_max_scores(submission_uuid)
median_scores = None
if "peer-assessment" in assessment_steps:
median_scores = peer_api.get_assessment_median_scores(submission_uuid)
elif "self-assessment" in assessment_steps:
median_scores = self_api.get_assessment_scores_by_criteria(submission_uuid)
elif "example-based-assessment" in assessment_steps:
median_scores = ai_api.get_assessment_scores_by_criteria(submission_uuid)
if median_scores is not None and max_scores is not None:
for criterion in context["rubric_criteria"]:
# Although we prevent course authors from modifying criteria post-release,
# it's still possible for assessments created by course staff to
# have criteria that differ from the current problem definition.
# It's also possible to circumvent the post-release restriction
# if course authors directly import a course into Studio.
# If this happens, we simply leave the score blank so that the grade
# section can render without error.
criterion["median_score"] = median_scores.get(criterion["name"], '')
criterion["total_value"] = max_scores.get(criterion["name"], '')
return ('openassessmentblock/grade/oa_grade_complete.html', context)
def test_error_getting_assessment_scores(self, mock_filter):
mock_filter.side_effect = DatabaseError("Oh no!")
ai_api.get_assessment_scores_by_criteria(self.submission_uuid)
def render_grade_complete(self, workflow):
"""
Render the grade complete state.
Args:
workflow (dict): The serialized Workflow model.
Returns:
tuple of context (dict), template_path (string)
"""
# Peer specific stuff...
assessment_steps = self.assessment_steps
submission_uuid = workflow['submission_uuid']
example_based_assessment = None
self_assessment = None
feedback = None
peer_assessments = []
has_submitted_feedback = False
if "peer-assessment" in assessment_steps:
feedback = peer_api.get_assessment_feedback(submission_uuid)
peer_assessments = peer_api.get_assessments(submission_uuid)
has_submitted_feedback = feedback is not None
if "self-assessment" in assessment_steps:
self_assessment = self_api.get_assessment(submission_uuid)
if "example-based-assessment" in assessment_steps:
example_based_assessment = ai_api.get_latest_assessment(submission_uuid)
feedback_text = feedback.get('feedback', '') if feedback else ''
student_submission = sub_api.get_submission(submission_uuid)
# We retrieve the score from the workflow, which in turn retrieves
# the score for our current submission UUID.
# We look up the score by submission UUID instead of student item
# to ensure that the score always matches the rubric.
# It's possible for the score to be `None` even if the workflow status is "done"
# when all the criteria in the rubric are feedback-only (no options).
score = workflow['score']
context = {
'score': score,
'feedback_text': feedback_text,
'student_submission': student_submission,
'peer_assessments': peer_assessments,
'self_assessment': self_assessment,
'example_based_assessment': example_based_assessment,
'rubric_criteria': self._rubric_criteria_with_feedback(peer_assessments),
'has_submitted_feedback': has_submitted_feedback,
'allow_file_upload': self.allow_file_upload,
'file_url': self.get_download_url_from_submission(student_submission)
}
# Update the scores we will display to the user
# Note that we are updating a *copy* of the rubric criteria stored in
# the XBlock field
max_scores = peer_api.get_rubric_max_scores(submission_uuid)
median_scores = None
if "peer-assessment" in assessment_steps:
median_scores = peer_api.get_assessment_median_scores(submission_uuid)
elif "self-assessment" in assessment_steps:
median_scores = self_api.get_assessment_scores_by_criteria(submission_uuid)
elif "example-based-assessment" in assessment_steps:
median_scores = ai_api.get_assessment_scores_by_criteria(submission_uuid)
if median_scores is not None and max_scores is not None:
for criterion in context["rubric_criteria"]:
criterion["median_score"] = median_scores[criterion["name"]]
criterion["total_value"] = max_scores[criterion["name"]]
return ('openassessmentblock/grade/oa_grade_complete.html', context)
def test_error_getting_assessment_scores(self, mock_filter):
mock_filter.side_effect = DatabaseError("Oh no!")
ai_api.get_assessment_scores_by_criteria(self.submission_uuid)
