def _determine_next_submission_to_grade(self, submissions_to_grade,
course_id):
"""
Helper function that
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
@param submissions_to_grade:
@param course_id:
@return:
"""
found = False
sub_id = 0
submission_grader_counts = [
p['num_graders'] for p in submissions_to_grade
]
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(
self.student_id, course_id)
fallback_sub_id = None
for i in xrange(len(submission_ids)):
minimum_index = submission_grader_counts.index(
min(submission_grader_counts))
grade_item = Submission.objects.get(
id=int(submission_ids[minimum_index]))
previous_graders = [
p.grader_id for p in grade_item.get_successful_peer_graders()
]
if self.student_id not in previous_graders:
found = True
sub_id = grade_item.id
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(
profile_dict, grade_item.student_id, course_id)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
def _determine_next_submission_to_grade(self, submissions_to_grade, course_id):
"""
Helper function that
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
@param submissions_to_grade:
@param course_id:
@return:
"""
found = False
sub_id = 0
submission_grader_counts = [p['num_graders'] for p in submissions_to_grade]
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(self.student_id, course_id)
fallback_sub_id = None
for i in xrange(len(submission_ids)):
minimum_index = submission_grader_counts.index(min(submission_grader_counts))
grade_item = Submission.objects.get(id=int(submission_ids[minimum_index]))
previous_graders = [p.grader_id for p in grade_item.get_successful_peer_graders()]
if self.student_id not in previous_graders:
found = True
sub_id = grade_item.id
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(profile_dict, grade_item.student_id, course_id)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
def get_single_peer_grading_item(location, grader_id):
"""
Gets peer grading for a given location and grader.
Returns one submission id corresponding to the location and the grader.
Input:
location - problem location.
grader_id - student id of the peer grader
Returns:
found - Boolean indicating whether or not something to grade was found
sub_id - If found, the id of a submission to grade
"""
found = False
sub_id = 0
to_be_graded = peer_grading_submissions_pending_for_location(
location, grader_id)
#Do some checks to ensure that there are actually items to grade
if to_be_graded is not None:
to_be_graded_length = to_be_graded.count()
if to_be_graded_length > 0:
course_id = to_be_graded[0].course_id
submissions_to_grade = (to_be_graded.filter(
grader__status_code=GraderStatus.success,
grader__grader_type__in=[
"PE", "BC"
]).exclude(grader__grader_id=grader_id).annotate(
num_graders=Count('grader')).values(
"num_graders", "id").order_by("num_graders")[:50])
if submissions_to_grade is not None:
submission_grader_counts = [
p['num_graders'] for p in submissions_to_grade
]
#log.debug("Submissions to grade with graders: {0} {1}".format(submission_grader_counts, submissions_to_grade))
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(
grader_id, course_id)
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
fallback_sub_id = None
for i in xrange(0, len(submission_ids)):
#log.debug("Looping through graders, on {0}".format(i))
minimum_index = submission_grader_counts.index(
min(submission_grader_counts))
grade_item = Submission.objects.get(
id=int(submission_ids[minimum_index]))
previous_graders = [
p.grader_id
for p in grade_item.get_successful_peer_graders()
]
if grader_id not in previous_graders:
found = True
sub_id = grade_item.id
#Insert timing initialization code
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
initialize_timing(sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(
profile_dict, grade_item.student_id, course_id)
log.debug(similarity_score)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
initialize_timing(sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
initialize_timing(fallback_sub_id)
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
def get_single_peer_grading_item(location, grader_id):
"""
Gets peer grading for a given location and grader.
Returns one submission id corresponding to the location and the grader.
Input:
location - problem location.
grader_id - student id of the peer grader
Returns:
found - Boolean indicating whether or not something to grade was found
sub_id - If found, the id of a submission to grade
"""
found = False
sub_id = 0
to_be_graded = peer_grading_submissions_pending_for_location(location, grader_id)
#Do some checks to ensure that there are actually items to grade
if to_be_graded is not None:
to_be_graded_length = to_be_graded.count()
if to_be_graded_length > 0:
course_id = to_be_graded[0].course_id
submissions_to_grade = (to_be_graded
.filter(grader__status_code=GraderStatus.success, grader__grader_type__in=["PE","BC"])
.exclude(grader__grader_id=grader_id)
.annotate(num_graders=Count('grader'))
.values("num_graders", "id")
.order_by("num_graders")[:50])
if submissions_to_grade is not None:
submission_grader_counts = [p['num_graders'] for p in submissions_to_grade]
#log.debug("Submissions to grade with graders: {0} {1}".format(submission_grader_counts, submissions_to_grade))
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(grader_id, course_id)
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
fallback_sub_id = None
for i in xrange(0, len(submission_ids)):
#log.debug("Looping through graders, on {0}".format(i))
minimum_index = submission_grader_counts.index(min(submission_grader_counts))
grade_item = Submission.objects.get(id=int(submission_ids[minimum_index]))
previous_graders = [p.grader_id for p in grade_item.get_successful_peer_graders()]
if grader_id not in previous_graders:
found = True
sub_id = grade_item.id
#Insert timing initialization code
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
initialize_timing(sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(profile_dict, grade_item.student_id, course_id)
log.debug(similarity_score)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
initialize_timing(sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
initialize_timing(fallback_sub_id)
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
def next_item(self):
"""
Gets peer grading for a given location and grader.
Returns one submission id corresponding to the location and the grader.
Input:
location - problem location.
grader_id - student id of the peer grader
Returns:
found - Boolean indicating whether or not something to grade was found
sub_id - If found, the id of a submission to grade
"""
found = False
sub_id = 0
to_be_graded = self.pending()
#Do some checks to ensure that there are actually items to grade
if to_be_graded.count()>0:
course_id = to_be_graded[0].course_id
submissions_to_grade = (to_be_graded
.annotate(num_graders=Count('grader'))
.values("num_graders", "id")
.order_by("date_created")[:50])
submission_grader_counts = [p['num_graders'] for p in submissions_to_grade]
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(self.student_id, course_id)
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
fallback_sub_id = None
for i in xrange(len(submission_ids)):
minimum_index = submission_grader_counts.index(min(submission_grader_counts))
grade_item = Submission.objects.get(id=int(submission_ids[minimum_index]))
previous_graders = [p.grader_id for p in grade_item.get_successful_peer_graders()]
if self.student_id not in previous_graders:
found = True
sub_id = grade_item.id
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(profile_dict, grade_item.student_id, course_id)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
def next_item(self):
"""
Gets peer grading for a given location and grader.
Returns one submission id corresponding to the location and the grader.
Input:
location - problem location.
grader_id - student id of the peer grader
Returns:
found - Boolean indicating whether or not something to grade was found
sub_id - If found, the id of a submission to grade
"""
found = False
sub_id = 0
to_be_graded = self.pending()
#Do some checks to ensure that there are actually items to grade
if to_be_graded.count() > 0:
course_id = to_be_graded[0].course_id
submissions_to_grade = (to_be_graded.annotate(
num_graders=Count('grader')).values(
"num_graders", "id").order_by("date_created")[:50])
submission_grader_counts = [
p['num_graders'] for p in submissions_to_grade
]
submission_ids = [p['id'] for p in submissions_to_grade]
student_profile_success, profile_dict = utilize_student_metrics.get_student_profile(
self.student_id, course_id)
#Ensure that student hasn't graded this submission before!
#Also ensures that all submissions are searched through if student has graded the minimum one
fallback_sub_id = None
for i in xrange(len(submission_ids)):
minimum_index = submission_grader_counts.index(
min(submission_grader_counts))
grade_item = Submission.objects.get(
id=int(submission_ids[minimum_index]))
previous_graders = [
p.grader_id
for p in grade_item.get_successful_peer_graders()
]
if self.student_id not in previous_graders:
found = True
sub_id = grade_item.id
if fallback_sub_id is None:
fallback_sub_id = grade_item.id
if not student_profile_success:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
success, similarity_score = utilize_student_metrics.get_similarity_score(
profile_dict, grade_item.student_id, course_id)
if similarity_score <= settings.PEER_GRADER_MIN_SIMILARITY_FOR_MATCHING:
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, sub_id
else:
if len(submission_ids) > 1:
submission_ids.pop(minimum_index)
submission_grader_counts.pop(minimum_index)
if found:
grade_item = Submission.objects.get(id=fallback_sub_id)
grade_item.state = SubmissionState.being_graded
grade_item.save()
return found, fallback_sub_id
return found, sub_id
