def handle_progress_event(block, event_type, event):
"""
tie into the CourseCompletions datamodels that are exposed in the api_manager djangoapp
"""
if not settings.FEATURES.get("ALLOW_STUDENT_STATE_UPDATES_ON_CLOSED_COURSE", True):
# if a course has ended, don't register progress events
course = modulestore().get_course(course_id, depth=0)
now = datetime.now(UTC())
if course.end is not None and now > course.end:
return
user_id = event.get('user_id', user.id)
if not user_id:
return
# Send a signal out to any listeners who are waiting for score change
# events.
SCORE_CHANGED.send(
sender=None,
points_possible=event.get('max_value'),
points_earned=event.get('value'),
user_id=user_id,
course_id=unicode(course_id),
usage_id=unicode(descriptor.location)
)
CourseModuleCompletion.objects.get_or_create(
user_id=user_id,
course_id=course_id,
content_id=unicode(descriptor.location)
)
def handle_grade_event(block, event_type, event): # pylint: disable=unused-argument
"""
Manages the workflow for recording and updating of student module grade state
"""
user_id = event.get('user_id', user.id)
# Construct the key for the module
key = KeyValueStore.Key(
scope=Scope.user_state,
user_id=user_id,
block_scope_id=descriptor.location,
field_name='grade'
)
student_module = field_data_cache.find_or_create(key)
# Update the grades
student_module.grade = event.get('value')
student_module.max_grade = event.get('max_value')
# Save all changes to the underlying KeyValueStore
student_module.save()
# Bin score into range and increment stats
score_bucket = get_score_bucket(student_module.grade, student_module.max_grade)
tags = [
u"org:{}".format(course_id.org),
u"course:{}".format(course_id),
u"score_bucket:{0}".format(score_bucket)
]
if grade_bucket_type is not None:
tags.append('type:%s' % grade_bucket_type)
dog_stats_api.increment("lms.courseware.question_answered", tags=tags)
# Cycle through the milestone fulfillment scenarios to see if any are now applicable
# thanks to the updated grading information that was just submitted
_fulfill_content_milestones(
user,
course_id,
descriptor.location,
)
# Send a signal out to any listeners who are waiting for score change
# events.
SCORE_CHANGED.send(
sender=None,
points_possible=event['max_value'],
points_earned=event['value'],
user_id=user_id,
course_id=unicode(course_id),
usage_id=unicode(descriptor.location)
)
def handle_grade_event(block, event_type, event): # pylint: disable=unused-argument
"""
Manages the workflow for recording and updating of student module grade state
"""
user_id = user.id
grade = event.get('value')
max_grade = event.get('max_value')
set_score(
user_id,
descriptor.location,
grade,
max_grade,
)
# Bin score into range and increment stats
score_bucket = get_score_bucket(grade, max_grade)
tags = [
u"org:{}".format(course_id.org),
u"course:{}".format(course_id),
u"score_bucket:{0}".format(score_bucket)
]
if grade_bucket_type is not None:
tags.append('type:%s' % grade_bucket_type)
dog_stats_api.increment("lms.courseware.question_answered", tags=tags)
# Cycle through the milestone fulfillment scenarios to see if any are now applicable
# thanks to the updated grading information that was just submitted
_fulfill_content_milestones(
user,
course_id,
descriptor.location,
)
# Send a signal out to any listeners who are waiting for score change
# events.
SCORE_CHANGED.send(
sender=None,
points_possible=event['max_value'],
points_earned=event['value'],
user_id=user_id,
course_id=unicode(course_id),
usage_id=unicode(descriptor.location)
)