def test_weighted_grading(self):
scores = []
Score.__sub__ = lambda me, other: (
me.earned - other.earned) + (me.possible - other.possible)
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(all_total, Score(
earned=0, possible=0, graded=False, section="summary"))
self.assertEqual(graded_total, Score(
earned=0, possible=0, graded=True, section="summary"))
scores.append(Score(
earned=0, possible=5, graded=False, section="summary"))
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(all_total, Score(
earned=0, possible=5, graded=False, section="summary"))
self.assertEqual(graded_total, Score(
earned=0, possible=0, graded=True, section="summary"))
scores.append(Score(
earned=3, possible=5, graded=True, section="summary"))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(all_total, Score(
earned=3, possible=10, graded=False, section="summary"))
self.assertAlmostEqual(graded_total, Score(
earned=3, possible=5, graded=True, section="summary"))
scores.append(Score(
earned=2, possible=5, graded=True, section="summary"))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(all_total, Score(
earned=5, possible=15, graded=False, section="summary"))
self.assertAlmostEqual(graded_total, Score(
earned=5, possible=10, graded=True, section="summary"))
def test_weighted_grading(self):
scores = []
Score.__sub__ = lambda me, other: (me.earned - other.earned) + (me.possible - other.possible)
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(all_total, Score(earned=0, possible=0, graded=False, section="summary", module_id=None))
self.assertEqual(graded_total, Score(earned=0, possible=0, graded=True, section="summary", module_id=None))
scores.append(Score(earned=0, possible=5, graded=False, section="summary", module_id=None))
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(all_total, Score(earned=0, possible=5, graded=False, section="summary", module_id=None))
self.assertEqual(graded_total, Score(earned=0, possible=0, graded=True, section="summary", module_id=None))
scores.append(Score(earned=3, possible=5, graded=True, section="summary", module_id=None))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(all_total, Score(earned=3, possible=10, graded=False, section="summary", module_id=None))
self.assertAlmostEqual(
graded_total, Score(earned=3, possible=5, graded=True, section="summary", module_id=None)
)
scores.append(Score(earned=2, possible=5, graded=True, section="summary", module_id=None))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(all_total, Score(earned=5, possible=15, graded=False, section="summary", module_id=None))
self.assertAlmostEqual(
graded_total, Score(earned=5, possible=10, graded=True, section="summary", module_id=None)
)
def test_weighted_grading(self):
scores = []
agg_fields = dict(display_name="aggregated_score", module_id=None, attempted=False)
prob_fields = dict(
display_name="problem_score", module_id=None, raw_earned=0, raw_possible=0, weight=0, attempted=False,
)
# No scores
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded)
scores.append(ProblemScore(weighted_earned=0, weighted_possible=5, graded=False, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=5, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) = 3/10 total, 3/5 graded
prob_fields['attempted'] = True
agg_fields['attempted'] = True
scores.append(ProblemScore(weighted_earned=3, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3, tw_possible=10, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3, tw_possible=5, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) + (2/5 graded) = 5/15 total, 5/10 graded
scores.append(ProblemScore(weighted_earned=2, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5, tw_possible=15, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5, tw_possible=10, graded=True, **agg_fields),
)
def test_weighted_grading(self):
scores = []
agg_fields = dict(first_attempted=None)
prob_fields = dict(raw_earned=0, raw_possible=0, weight=0, first_attempted=None)
# No scores
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded)
scores.append(ProblemScore(weighted_earned=0, weighted_possible=5, graded=False, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=5, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) = 3/10 total, 3/5 graded
now = datetime.now()
prob_fields['first_attempted'] = now
agg_fields['first_attempted'] = now
scores.append(ProblemScore(weighted_earned=3, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3, tw_possible=10, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3, tw_possible=5, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) + (2/5 graded) = 5/15 total, 5/10 graded
scores.append(ProblemScore(weighted_earned=2, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5, tw_possible=15, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5, tw_possible=10, graded=True, **agg_fields),
)
def test_weighted_grading(self):
scores = []
agg_fields = dict(first_attempted=None)
prob_fields = dict(raw_earned=0, raw_possible=0, weight=0, first_attempted=None)
# No scores
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded)
scores.append(ProblemScore(weighted_earned=0, weighted_possible=5, graded=False, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=5, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) = 3/10 total, 3/5 graded
now = datetime.now()
prob_fields['first_attempted'] = now
agg_fields['first_attempted'] = now
scores.append(ProblemScore(weighted_earned=3, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3, tw_possible=10, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3, tw_possible=5, graded=True, **agg_fields),
)
# (0/5 non-graded) + (3/5 graded) + (2/5 graded) = 5/15 total, 5/10 graded
scores.append(ProblemScore(weighted_earned=2, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores)
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5, tw_possible=15, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5, tw_possible=10, graded=True, **agg_fields),
)
def __init__(self, subsection, course_structure, submissions_scores,
csm_scores):
self.problem_scores = OrderedDict()
for block_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=subsection.location,
):
problem_score = self._compute_block_score(block_key,
course_structure,
submissions_scores,
csm_scores)
# TODO: Remove as part of EDUCATOR-4602.
if str(block_key.course_key) == 'course-v1:UQx+BUSLEAD5x+2T2019':
log.info(
u'Calculated problem score ***{}*** for block ***{!s}***'
u' in subsection ***{}***.'.format(problem_score,
block_key,
subsection.location))
if problem_score:
self.problem_scores[block_key] = problem_score
all_total, graded_total = graders.aggregate_scores(
list(self.problem_scores.values()))
# TODO: Remove as part of EDUCATOR-4602.
if str(subsection.location.course_key
) == 'course-v1:UQx+BUSLEAD5x+2T2019':
log.info(
u'Calculated aggregate all_total ***{}***'
u' and grade_total ***{}*** for subsection ***{}***'.format(
all_total, graded_total, subsection.location))
super(CreateSubsectionGrade, self).__init__(subsection, all_total,
graded_total)
def test_weighted_grading(self):
scores = []
agg_fields = dict(display_name="aggregated_score", module_id=None)
prob_fields = dict(display_name="problem_score", module_id=None, raw_earned=0, raw_possible=0, weight=0)
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
scores.append(ProblemScore(weighted_earned=0, weighted_possible=5, graded=False, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0, tw_possible=5, graded=False, **agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0, tw_possible=0, graded=True, **agg_fields),
)
scores.append(ProblemScore(weighted_earned=3, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3, tw_possible=10, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3, tw_possible=5, graded=True, **agg_fields),
)
scores.append(ProblemScore(weighted_earned=2, weighted_possible=5, graded=True, **prob_fields))
all_total, graded_total = aggregate_scores(scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5, tw_possible=15, graded=False, **agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5, tw_possible=10, graded=True, **agg_fields),
)
def compute(self, student, course_structure, scores_client, submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(student, descendant_key, course_structure, scores_client, submissions_scores)
self.all_total, self.graded_total = graders.aggregate_scores(self.scores, self.display_name, self.location)
def __init__(self, subsection, course_structure, submissions_scores, csm_scores):
self.problem_scores = OrderedDict()
for block_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=subsection.location,
):
problem_score = self._compute_block_score(block_key, course_structure, submissions_scores, csm_scores)
if problem_score:
self.problem_scores[block_key] = problem_score
all_total, graded_total = graders.aggregate_scores(list(self.problem_scores.values()))
super(CreateSubsectionGrade, self).__init__(subsection, all_total, graded_total)
def init_from_structure(self, student, course_structure, submissions_scores, csm_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(descendant_key, course_structure, submissions_scores, csm_scores)
self.all_total, self.graded_total = graders.aggregate_scores(self.scores)
self._log_event(log.debug, u"init_from_structure", student)
return self
def __init__(self, subsection, course_structure, submissions_scores, csm_scores):
self.problem_scores = OrderedDict()
for block_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=subsection.location,
):
problem_score = self._compute_block_score(block_key, course_structure, submissions_scores, csm_scores)
if problem_score:
self.problem_scores[block_key] = problem_score
all_total, graded_total = graders.aggregate_scores(self.problem_scores.values())
super(CreateSubsectionGrade, self).__init__(subsection, all_total, graded_total)
def init_from_structure(self, student, course_structure, scores_client, submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
assert self._scores is None
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(
student, descendant_key, course_structure, scores_client, submissions_scores, persisted_values={},
)
self.all_total, self.graded_total = graders.aggregate_scores(self.scores, self.display_name, self.location)
self._log_event(log.warning, u"init_from_structure", student)
def compute(self, student, course_structure, scores_client, submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
try:
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(student, descendant_key, course_structure, scores_client, submissions_scores)
finally:
# self.scores may hold outdated data, force it to refresh on next access
lazy.invalidate(self, 'scores')
self.all_total, self.graded_total = graders.aggregate_scores(self.scores, self.display_name, self.location)
def compute(self, student, course_structure, scores_client,
submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
try:
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(student, descendant_key,
course_structure, scores_client,
submissions_scores)
finally:
# self.scores may hold outdated data, force it to refresh on next access
lazy.invalidate(self, 'scores')
self.all_total, self.graded_total = graders.aggregate_scores(
self.scores, self.display_name, self.location)
def init_from_structure(self, student, course_structure, scores_client,
submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
assert self._scores is None
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
self._compute_block_score(
student,
descendant_key,
course_structure,
scores_client,
submissions_scores,
persisted_values={},
)
self.all_total, self.graded_total = graders.aggregate_scores(
self.scores, self.display_name, self.location)
self._log_event(log.warning, u"init_from_structure", student)
def compute(self, student, course_structure, scores_client, submissions_scores):
"""
Compute the grade of this subsection for the given student and course.
"""
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=self.location,
):
descendant = course_structure[descendant_key]
if not getattr(descendant, 'has_score', False):
continue
(earned, possible) = get_score(
student,
descendant,
scores_client,
submissions_scores,
)
if earned is None and possible is None:
continue
# cannot grade a problem with a denominator of 0
descendant_graded = descendant.graded if possible > 0 else False
self.locations_to_scores[descendant.location] = Score(
earned,
possible,
descendant_graded,
block_metadata_utils.display_name_with_default_escaped(descendant),
descendant.location,
)
self.all_total, self.graded_total = graders.aggregate_scores(
self.scores, self.display_name,
)
def _grade(student, request, course, keep_raw_scores):
"""
Unwrapped version of "grade"
This grades a student as quickly as possible. It returns the
output from the course grader, augmented with the final letter
grade. The keys in the output are:
course: a CourseDescriptor
- grade : A final letter grade.
- percent : The final percent for the class (rounded up).
- section_breakdown : A breakdown of each section that makes
up the grade. (For display)
- grade_breakdown : A breakdown of the major components that
make up the final grade. (For display)
- keep_raw_scores : if True, then value for key 'raw_scores' contains scores
for every graded module
More information on the format is in the docstring for CourseGrader.
"""
grading_context = course.grading_context
raw_scores = []
totaled_scores = {}
# This next complicated loop is just to collect the totaled_scores, which is
# passed to the grader
for section_format, sections in grading_context['graded_sections'].iteritems():
format_scores = []
for section in sections:
section_descriptor = section['section_descriptor']
section_name = section_descriptor.display_name_with_default
# some problems have state that is updated independently of interaction
# with the LMS, so they need to always be scored. (E.g. foldit.,
# combinedopenended)
should_grade_section = any(
descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']
)
# If we haven't seen a single problem in the section, we don't have to grade it at all! We can assume 0%
if not should_grade_section:
with manual_transaction():
should_grade_section = StudentModule.objects.filter(
student=student,
module_state_key__in=[
descriptor.location for descriptor in section['xmoduledescriptors']
]
).exists()
if should_grade_section:
scores = []
def create_module(descriptor):
'''creates an XModule instance given a descriptor'''
# TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
with manual_transaction():
field_data_cache = FieldDataCache([descriptor], course.id, student)
return get_module_for_descriptor(student, request, descriptor, field_data_cache, course.id)
for module_descriptor in yield_dynamic_descriptor_descendents(section_descriptor, create_module):
(correct, total) = get_score(course.id, student, module_descriptor, create_module)
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1), total + 1)
else:
correct = total
graded = module_descriptor.graded
if not total > 0:
#We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
_, graded_total = graders.aggregate_scores(scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name)
#Add the graded total to totaled_scores
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.exception("Unable to grade a section with a total possible score of zero. " +
str(section_descriptor.location))
totaled_scores[section_format] = format_scores
grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)
# We round the grade here, to make sure that the grade is an whole percentage and
# doesn't get displayed differently than it gets grades
grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100
letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])
grade_summary['grade'] = letter_grade
grade_summary['totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
if keep_raw_scores:
grade_summary['raw_scores'] = raw_scores # way to get all RAW scores out to instructor
# so grader can be double-checked
return grade_summary
def grade(student,
request,
course,
field_data_cache=None,
keep_raw_scores=False):
"""
This grades a student as quickly as possible. It returns the
output from the course grader, augmented with the final letter
grade. The keys in the output are:
course: a CourseDescriptor
- grade : A final letter grade.
- percent : The final percent for the class (rounded up).
- section_breakdown : A breakdown of each section that makes
up the grade. (For display)
- grade_breakdown : A breakdown of the major components that
make up the final grade. (For display)
- keep_raw_scores : if True, then value for key 'raw_scores' contains scores for every graded module
More information on the format is in the docstring for CourseGrader.
"""
grading_context = course.grading_context
raw_scores = []
if field_data_cache is None:
field_data_cache = FieldDataCache(grading_context['all_descriptors'],
course.id, student)
totaled_scores = {}
# This next complicated loop is just to collect the totaled_scores, which is
# passed to the grader
for section_format, sections in grading_context[
'graded_sections'].iteritems():
format_scores = []
for section in sections:
section_descriptor = section['section_descriptor']
section_name = section_descriptor.display_name_with_default
should_grade_section = False
# If we haven't seen a single problem in the section, we don't have to grade it at all! We can assume 0%
for moduledescriptor in section['xmoduledescriptors']:
# some problems have state that is updated independently of interaction
# with the LMS, so they need to always be scored. (E.g. foldit.)
if moduledescriptor.always_recalculate_grades:
should_grade_section = True
break
# Create a fake key to pull out a StudentModule object from the FieldDataCache
key = DjangoKeyValueStore.Key(Scope.user_state, student.id,
moduledescriptor.location, None)
if field_data_cache.find(key):
should_grade_section = True
break
if should_grade_section:
scores = []
def create_module(descriptor):
'''creates an XModule instance given a descriptor'''
# TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
return get_module_for_descriptor(student, request,
descriptor,
field_data_cache,
course.id)
for module_descriptor in yield_dynamic_descriptor_descendents(
section_descriptor, create_module):
(correct,
total) = get_score(course.id, student, module_descriptor,
create_module, field_data_cache)
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1),
total + 1)
else:
correct = total
graded = module_descriptor.graded
if not total > 0:
#We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
scores.append(
Score(correct, total, graded,
module_descriptor.display_name_with_default))
_, graded_total = graders.aggregate_scores(
scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name)
#Add the graded total to totaled_scores
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.exception(
"Unable to grade a section with a total possible score of zero. "
+ str(section_descriptor.location))
totaled_scores[section_format] = format_scores
grade_summary = course.grader.grade(
totaled_scores,
generate_random_scores=settings.GENERATE_PROFILE_SCORES)
# We round the grade here, to make sure that the grade is an whole percentage and
# doesn't get displayed differently than it gets grades
grade_summary['percent'] = round(grade_summary['percent'] * 100 +
0.05) / 100
letter_grade = grade_for_percentage(course.grade_cutoffs,
grade_summary['percent'])
grade_summary['grade'] = letter_grade
grade_summary[
'totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
if keep_raw_scores:
grade_summary[
'raw_scores'] = raw_scores # way to get all RAW scores out to instructor
# so grader can be double-checked
return grade_summary
def progress_summary(student, request, course, field_data_cache):
"""
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
field_data_cache: A FieldDataCache initialized with all
instance_modules for the student
If the student does not have access to load the course module, this function
will return None.
"""
# TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
course_module = get_module(student,
request,
course.location,
field_data_cache,
course.id,
depth=None)
if not course_module:
# This student must not have access to the course.
return None
chapters = []
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
if section_module.hide_from_toc:
continue
# Same for sections
graded = section_module.graded
scores = []
module_creator = section_module.system.get_module
for module_descriptor in yield_dynamic_descriptor_descendents(
section_module.descriptor, module_creator):
course_id = course.id
(correct, total) = get_score(course_id, student,
module_descriptor, module_creator,
field_data_cache)
if correct is None and total is None:
continue
scores.append(
Score(correct, total, graded,
module_descriptor.display_name_with_default))
scores.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.format if section_module.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'section_total': section_total,
'format': module_format,
'due': section_module.due,
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections
})
return chapters
def _progress_summary(student, request, course):
"""
Unwrapped version of "progress_summary".
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, student, course, depth=None
)
# TODO: We need the request to pass into here. If we could
# forego that, our arguments would be simpler
course_module = get_module_for_descriptor(student, request, course, field_data_cache, course.id)
if not course_module:
# This student must not have access to the course.
return None
course_module = getattr(course_module, '_x_module', course_module)
submissions_scores = sub_api.get_scores(course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id))
chapters = []
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
with manual_transaction():
if section_module.hide_from_toc:
continue
graded = section_module.graded
scores = []
module_creator = section_module.xmodule_runtime.get_module
for module_descriptor in yield_dynamic_descriptor_descendents(section_module, module_creator):
course_id = course.id
(correct, total) = get_score(
course_id, student, module_descriptor, module_creator, scores_cache=submissions_scores
)
if correct is None and total is None:
continue
scores.append(
Score(
correct,
total,
graded,
module_descriptor.display_name_with_default,
module_descriptor.location
)
)
scores.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.format if section_module.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'section_total': section_total,
'format': module_format,
'due': section_module.due,
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections
})
return chapters
def progress_summary(student, request, course, model_data_cache):
"""
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
model_data_cache: A ModelDataCache initialized with all
instance_modules for the student
If the student does not have access to load the course module, this function
will return None.
"""
# TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
course_module = get_module(student, request, course.location, model_data_cache, course.id, depth=None)
if not course_module:
# This student must not have access to the course.
return None
chapters = []
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.lms.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
if section_module.lms.hide_from_toc:
continue
graded = section_module.lms.graded
scores = []
module_creator = section_module.system.get_module
for module_descriptor in yield_dynamic_descriptor_descendents(section_module.descriptor, module_creator):
course_id = course.id
(correct, total) = get_score(course_id, student, module_descriptor, module_creator, model_data_cache)
if correct is None and total is None:
continue
scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
scores.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.lms.format if section_module.lms.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'unlock_term': section_module.unlock_term,
'section_total': section_total,
'format': module_format,
'unlocked': True,
'due': section_module.lms.due,
'graded': graded,
})
chapters.append({'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections})
chapters = set_locks(chapters)
return chapters
def _progress_summary(student, request, course, field_data_cache=None, scores_client=None):
"""
Unwrapped version of "progress_summary".
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
if field_data_cache is None:
field_data_cache = field_data_cache_for_grading(course, student)
if scores_client is None:
scores_client = ScoresClient.from_field_data_cache(field_data_cache)
course_module = get_module_for_descriptor(
student, request, course, field_data_cache, course.id, course=course
)
if not course_module:
return None
course_module = getattr(course_module, '_x_module', course_module)
submissions_scores = sub_api.get_scores(course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id))
max_scores_cache = MaxScoresCache.create_for_course(course)
# For the moment, we have to get scorable_locations from field_data_cache
# and not from scores_client, because scores_client is ignorant of things
# in the submissions API. As a further refactoring step, submissions should
# be hidden behind the ScoresClient.
max_scores_cache.fetch_from_remote(field_data_cache.scorable_locations)
chapters = []
locations_to_children = defaultdict(list)
locations_to_weighted_scores = {}
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
with manual_transaction():
if section_module.hide_from_toc:
continue
graded = section_module.graded
scores = []
module_creator = section_module.xmodule_runtime.get_module
for module_descriptor in yield_dynamic_descriptor_descendants(
section_module, student.id, module_creator
):
locations_to_children[module_descriptor.parent].append(module_descriptor.location)
(correct, total) = get_score(
student,
module_descriptor,
module_creator,
scores_client,
submissions_scores,
max_scores_cache,
)
if correct is None and total is None:
continue
weighted_location_score = Score(
correct,
total,
graded,
module_descriptor.display_name_with_default,
module_descriptor.location
)
scores.append(weighted_location_score)
locations_to_weighted_scores[module_descriptor.location] = weighted_location_score
scores.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.format if section_module.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'section_total': section_total,
'format': module_format,
'due': section_module.due,
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections
})
max_scores_cache.push_to_remote()
return ProgressSummary(chapters, locations_to_weighted_scores, locations_to_children)
def get_grades(course,student):
field_data_cache = field_data_cache_for_grading(course, student)
scores_client = ScoresClient.from_field_data_cache(field_data_cache)
grading_context = course.grading_context
raw_scores = []
keep_raw_scores = None
# Dict of item_ids -> (earned, possible) point tuples. This *only* grabs
# scores that were registered with the submissions API, which for the moment
# means only openassessment (edx-ora2)
submissions_scores = sub_api.get_scores(
course.id.to_deprecated_string(),
anonymous_id_for_user(student, course.id)
)
max_scores_cache = MaxScoresCache.create_for_course(course)
## Fix for grading certificate subsection issue
tem_set = set()
for set_item in field_data_cache.scorable_locations:
set_item = set_item.version_agnostic()
set_item = set_item.replace(branch=None)
tem_set.update([set_item])
field_data_cache.scorable_locations = tem_set
# For the moment, we have to get scorable_locations from field_data_cache
# and not from scores_client, because scores_client is ignorant of things
# in the submissions API. As a further refactoring step, submissions should
# be hidden behind the ScoresClient.
max_scores_cache.fetch_from_remote(field_data_cache.scorable_locations)
totaled_scores = {}
graded_total = []
# This next complicated loop is just to collect the totaled_scores, which is
# passed to the grader
for section_format, sections in grading_context['graded_sections'].iteritems():
format_scores = []
for section in sections:
section_descriptor = section['section_descriptor']
section_name = section_descriptor.display_name_with_default
# some problems have state that is updated independently of interaction
# with the LMS, so they need to always be scored. (E.g. foldit.,
# combinedopenended)
should_grade_section = any(
descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']
)
# If there are no problems that always have to be regraded, check to
# see if any of our locations are in the scores from the submissions
# API. If scores exist, we have to calculate grades for this section.
if not should_grade_section:
should_grade_section = any(
descriptor.location.to_deprecated_string() in submissions_scores
for descriptor in section['xmoduledescriptors']
)
## Fix for grading certificate subsection issue
for descriptor in section['xmoduledescriptors']:
descriptor.location = descriptor.location.version_agnostic()
descriptor.location = descriptor.location.replace(branch=None)
if not should_grade_section:
should_grade_section = any(
descriptor.location in scores_client
for descriptor in section['xmoduledescriptors']
)
# If we haven't seen a single problem in the section, we don't have
# to grade it at all! We can assume 0%
if should_grade_section:
scores = []
try:
create_module = section_descriptor.xmodule_runtime.get_module
except :
def create_module(descriptor):
'''creates an XModule instance given a descriptor'''
#TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
pass
for module_descriptor in yield_dynamic_descriptor_descendants(section_descriptor, student.id, create_module):
(correct, total) = get_score(
student,
module_descriptor,
create_module,
scores_client,
submissions_scores,
max_scores_cache,
)
print "total==============",total
print "correct================",correct
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1), total + 1)
else:
correct = total
graded = module_descriptor.graded
if not total > 0:
#We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
#scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
scores.append(Score(correct,total,graded,module_descriptor.display_name_with_default,module_descriptor.location))
_, graded_total = graders.aggregate_scores(scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name,None)
#Add the graded total to totaled_scores
if graded_total:
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.info("Unable to grade a section with a total possible score of zero. " +
str(section_descriptor.location))
totaled_scores[section_format] = format_scores
grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)
# We round the grade here, to make sure that the grade is an whole percentage and
# doesn't get displayed differently than it gets grades
grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100
letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])
grade_summary['grade'] = letter_grade
grade_summary['totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
if keep_raw_scores:
grade_summary['raw_scores'] = raw_scores # way to get all RAW scores out to instructor
max_scores_cache.push_to_remote() # so grader can be double-checked
return grade_summary
def progress_summary(student,
request,
course,
field_data_cache=None,
scores_client=None,
grading_type='vertical'):
"""
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
if field_data_cache is None:
field_data_cache = field_data_cache_for_grading(
course, student)
if scores_client is None:
scores_client = ScoresClient.from_field_data_cache(
field_data_cache)
course_module = get_module_for_descriptor(student,
request,
course,
field_data_cache,
course.id,
course=course)
if not course_module:
return None
course_module = getattr(course_module, '_x_module', course_module)
submissions_scores = sub_api.get_scores(
course.id.to_deprecated_string(),
anonymous_id_for_user(student, course.id))
max_scores_cache = MaxScoresCache.create_for_course(course)
# For the moment, we have to get scorable_locations from field_data_cache
# and not from scores_client, because scores_client is ignorant of things
# in the submissions API. As a further refactoring step, submissions should
# be hidden behind the ScoresClient.
max_scores_cache.fetch_from_remote(field_data_cache.scorable_locations)
blocks_stack = [course_module]
blocks_dict = {}
while blocks_stack:
curr_block = blocks_stack.pop()
with manual_transaction():
# Skip if the block is hidden
if curr_block.hide_from_toc:
continue
key = unicode(curr_block.scope_ids.usage_id)
children = curr_block.get_display_items(
) if curr_block.category != grading_type else []
block = {
'display_name':
curr_block.display_name_with_default,
'block_type':
curr_block.category,
'url_name':
curr_block.url_name,
'children':
[unicode(child.scope_ids.usage_id) for child in children],
}
if curr_block.category == grading_type:
graded = curr_block.graded
scores = []
module_creator = curr_block.xmodule_runtime.get_module
for module_descriptor in yield_dynamic_descriptor_descendants(
curr_block, student.id, module_creator):
(correct, total) = get_score(
student,
module_descriptor,
module_creator,
scores_client,
submissions_scores,
max_scores_cache,
)
if correct is None and total is None:
continue
scores.append(
Score(correct, total, graded,
module_descriptor.display_name_with_default,
module_descriptor.location))
scores.reverse()
total, _ = aggregate_scores(
scores, curr_block.display_name_with_default)
module_format = curr_block.format if curr_block.format is not None else ''
block.update({
'scores': scores,
'total': total,
'format': module_format,
'due': curr_block.due,
'graded': graded,
})
blocks_dict[key] = block
# Add this blocks children to the stack so that we can traverse them as well.
blocks_stack.extend(children)
max_scores_cache.push_to_remote()
return {
'root': unicode(course.scope_ids.usage_id),
'blocks': blocks_dict,
}
def _calculate_totaled_scores(
student,
grading_context_result,
submissions_scores,
scores_client,
keep_raw_scores,
):
"""
Returns the totaled scores, which can be passed to the grader.
"""
raw_scores = []
totaled_scores = {}
for section_format, sections in grading_context_result['all_graded_sections'].iteritems():
format_scores = []
for section_info in sections:
section = section_info['section_block']
section_name = block_metadata_utils.display_name_with_default(section)
with outer_atomic():
# Check to
# see if any of our locations are in the scores from the submissions
# API. If scores exist, we have to calculate grades for this section.
should_grade_section = any(
unicode(descendant.location) in submissions_scores
for descendant in section_info['scored_descendants']
)
if not should_grade_section:
should_grade_section = any(
descendant.location in scores_client
for descendant in section_info['scored_descendants']
)
# If we haven't seen a single problem in the section, we don't have
# to grade it at all! We can assume 0%
if should_grade_section:
scores = []
for descendant in section_info['scored_descendants']:
(correct, total) = get_score(
student,
descendant,
scores_client,
submissions_scores,
)
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1), total + 1)
else:
correct = total
graded = descendant.graded
if not total > 0:
# We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
scores.append(
Score(
correct,
total,
graded,
block_metadata_utils.display_name_with_default_escaped(descendant),
descendant.location
)
)
__, graded_total = graders.aggregate_scores(scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name, None)
# Add the graded total to totaled_scores
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.info(
"Unable to grade a section with a total possible score of zero. " +
str(section.location)
)
totaled_scores[section_format] = format_scores
return totaled_scores, raw_scores
def test_weighted_grading(self):
scores = []
agg_fields = dict(display_name="aggregated_score", module_id=None)
prob_fields = dict(display_name="problem_score",
module_id=None,
raw_earned=0,
raw_possible=0,
weight=0)
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=False,
**agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=True,
**agg_fields),
)
scores.append(
ProblemScore(weighted_earned=0,
weighted_possible=5,
graded=False,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0,
tw_possible=5,
graded=False,
**agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=True,
**agg_fields),
)
scores.append(
ProblemScore(weighted_earned=3,
weighted_possible=5,
graded=True,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3,
tw_possible=10,
graded=False,
**agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3,
tw_possible=5,
graded=True,
**agg_fields),
)
scores.append(
ProblemScore(weighted_earned=2,
weighted_possible=5,
graded=True,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5,
tw_possible=15,
graded=False,
**agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5,
tw_possible=10,
graded=True,
**agg_fields),
)
def test_weighted_grading(self):
scores = []
agg_fields = dict(display_name="aggregated_score",
module_id=None,
attempted=False)
prob_fields = dict(
display_name="problem_score",
module_id=None,
raw_earned=0,
raw_possible=0,
weight=0,
attempted=False,
)
# No scores
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=False,
**agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=True,
**agg_fields),
)
# (0/5 non-graded)
scores.append(
ProblemScore(weighted_earned=0,
weighted_possible=5,
graded=False,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertEqual(
all_total,
AggregatedScore(tw_earned=0,
tw_possible=5,
graded=False,
**agg_fields),
)
self.assertEqual(
graded_total,
AggregatedScore(tw_earned=0,
tw_possible=0,
graded=True,
**agg_fields),
)
# (0/5 non-graded) + (3/5 graded) = 3/10 total, 3/5 graded
prob_fields['attempted'] = True
agg_fields['attempted'] = True
scores.append(
ProblemScore(weighted_earned=3,
weighted_possible=5,
graded=True,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=3,
tw_possible=10,
graded=False,
**agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=3,
tw_possible=5,
graded=True,
**agg_fields),
)
# (0/5 non-graded) + (3/5 graded) + (2/5 graded) = 5/15 total, 5/10 graded
scores.append(
ProblemScore(weighted_earned=2,
weighted_possible=5,
graded=True,
**prob_fields))
all_total, graded_total = aggregate_scores(
scores, display_name=agg_fields['display_name'])
self.assertAlmostEqual(
all_total,
AggregatedScore(tw_earned=5,
tw_possible=15,
graded=False,
**agg_fields),
)
self.assertAlmostEqual(
graded_total,
AggregatedScore(tw_earned=5,
tw_possible=10,
graded=True,
**agg_fields),
)
def progress_summary(student, request, course, field_data_cache=None, scores_client=None, grading_type='vertical'):
"""
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
if field_data_cache is None:
field_data_cache = field_data_cache_for_grading(course, student)
if scores_client is None:
scores_client = ScoresClient.from_field_data_cache(field_data_cache)
course_module = get_module_for_descriptor(
student, request, course, field_data_cache, course.id, course=course
)
if not course_module:
return None
course_module = getattr(course_module, '_x_module', course_module)
submissions_scores = sub_api.get_scores(
course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id)
)
max_scores_cache = MaxScoresCache.create_for_course(course)
# For the moment, we have to get scorable_locations from field_data_cache
# and not from scores_client, because scores_client is ignorant of things
# in the submissions API. As a further refactoring step, submissions should
# be hidden behind the ScoresClient.
max_scores_cache.fetch_from_remote(field_data_cache.scorable_locations)
blocks_stack = [course_module]
blocks_dict = {}
while blocks_stack:
curr_block = blocks_stack.pop()
with manual_transaction():
# Skip if the block is hidden
if curr_block.hide_from_toc:
continue
key = unicode(curr_block.scope_ids.usage_id)
children = curr_block.get_display_items() if curr_block.category != grading_type else []
block = {
'display_name': curr_block.display_name_with_default,
'block_type': curr_block.category,
'url_name': curr_block.url_name,
'children': [unicode(child.scope_ids.usage_id) for child in children],
}
if curr_block.category == grading_type:
graded = curr_block.graded
scores = []
module_creator = curr_block.xmodule_runtime.get_module
for module_descriptor in yield_dynamic_descriptor_descendants(
curr_block, student.id, module_creator
):
(correct, total) = get_score(
student,
module_descriptor,
module_creator,
scores_client,
submissions_scores,
max_scores_cache,
)
if correct is None and total is None:
continue
scores.append(
Score(
correct,
total,
graded,
module_descriptor.display_name_with_default,
module_descriptor.location
)
)
scores.reverse()
total, _ = aggregate_scores(scores, curr_block.display_name_with_default)
module_format = curr_block.format if curr_block.format is not None else ''
block.update({
'scores': scores,
'total': total,
'format': module_format,
'due': curr_block.due,
'graded': graded,
})
blocks_dict[key] = block
# Add this blocks children to the stack so that we can traverse them as well.
blocks_stack.extend(children)
max_scores_cache.push_to_remote()
return {
'root': unicode(course.scope_ids.usage_id),
'blocks': blocks_dict,
}
def _aggregate_scores(self):
return graders.aggregate_scores(self.problem_scores.values())
def summary(student, course, course_structure=None):
"""
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
- None if the student does not have access to load the course module.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
"""
if course_structure is None:
course_structure = get_course_blocks(student, course.location)
if not len(course_structure):
return ProgressSummary()
scorable_locations = [
block_key for block_key in course_structure
if possibly_scored(block_key)
]
with outer_atomic():
scores_client = ScoresClient.create_for_locations(
course.id, student.id, scorable_locations)
# We need to import this here to avoid a circular dependency of the form:
# XBlock --> submissions --> Django Rest Framework error strings -->
# Django translation --> ... --> courseware --> submissions
from submissions import api as sub_api # installed from the edx-submissions repository
with outer_atomic():
submissions_scores = sub_api.get_scores(
unicode(course.id), anonymous_id_for_user(student, course.id))
# Check for gated content
gated_content = gating_api.get_gated_content(course, student)
chapters = []
locations_to_weighted_scores = {}
for chapter_key in course_structure.get_children(
course_structure.root_block_usage_key):
chapter = course_structure[chapter_key]
sections = []
for section_key in course_structure.get_children(chapter_key):
if unicode(section_key) in gated_content:
continue
section = course_structure[section_key]
graded = getattr(section, 'graded', False)
scores = []
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=section_key,
):
descendant = course_structure[descendant_key]
(correct, total) = get_score(
student,
descendant,
scores_client,
submissions_scores,
)
if correct is None and total is None:
continue
weighted_location_score = Score(
correct, total, graded,
block_metadata_utils.display_name_with_default_escaped(
descendant), descendant.location)
scores.append(weighted_location_score)
locations_to_weighted_scores[
descendant.location] = weighted_location_score
escaped_section_name = block_metadata_utils.display_name_with_default_escaped(
section)
section_total, _ = graders.aggregate_scores(
scores, escaped_section_name)
sections.append({
'display_name':
escaped_section_name,
'url_name':
block_metadata_utils.url_name_for_block(section),
'scores':
scores,
'section_total':
section_total,
'format':
getattr(section, 'format', ''),
'due':
getattr(section, 'due', None),
'graded':
graded,
})
chapters.append({
'course':
course.display_name_with_default_escaped,
'display_name':
block_metadata_utils.display_name_with_default_escaped(chapter),
'url_name':
block_metadata_utils.url_name_for_block(chapter),
'sections':
sections
})
return ProgressSummary(chapters, locations_to_weighted_scores,
course_structure.get_children)
def _progress_summary(student, request, course):
"""
Unwrapped version of "progress_summary".
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, student, course, depth=None
)
# TODO: We need the request to pass into here. If we could
# forego that, our arguments would be simpler
course_module = get_module_for_descriptor(student, request, course, field_data_cache, course.id)
if not course_module:
# This student must not have access to the course.
return None
chapters = []
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
with manual_transaction():
if section_module.hide_from_toc:
continue
graded = section_module.graded
scores = []
module_creator = section_module.xmodule_runtime.get_module
for module_descriptor in yield_dynamic_descriptor_descendents(section_module, module_creator):
course_id = course.id
(correct, total) = get_score(course_id, student, module_descriptor, module_creator)
if correct is None and total is None:
continue
scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
scores.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.format if section_module.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'section_total': section_total,
'format': module_format,
'due': get_extended_due_date(section_module),
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections
})
return chapters
def _progress_summary(student, course, course_structure=None):
"""
Unwrapped version of "progress_summary".
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
- None if the student does not have access to load the course module.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
"""
if course_structure is None:
course_structure = get_course_blocks(student, course.location)
if not len(course_structure):
return None
scorable_locations = [block_key for block_key in course_structure if possibly_scored(block_key)]
with outer_atomic():
scores_client = ScoresClient.create_for_locations(course.id, student.id, scorable_locations)
# We need to import this here to avoid a circular dependency of the form:
# XBlock --> submissions --> Django Rest Framework error strings -->
# Django translation --> ... --> courseware --> submissions
from submissions import api as sub_api # installed from the edx-submissions repository
with outer_atomic():
submissions_scores = sub_api.get_scores(
unicode(course.id), anonymous_id_for_user(student, course.id)
)
# Check for gated content
gated_content = gating_api.get_gated_content(course, student)
chapters = []
locations_to_weighted_scores = {}
for chapter_key in course_structure.get_children(course_structure.root_block_usage_key):
chapter = course_structure[chapter_key]
sections = []
for section_key in course_structure.get_children(chapter_key):
if unicode(section_key) in gated_content:
continue
section = course_structure[section_key]
graded = getattr(section, 'graded', False)
scores = []
for descendant_key in course_structure.post_order_traversal(
filter_func=possibly_scored,
start_node=section_key,
):
descendant = course_structure[descendant_key]
(correct, total) = get_score(
student,
descendant,
scores_client,
submissions_scores,
)
if correct is None and total is None:
continue
weighted_location_score = Score(
correct,
total,
graded,
block_metadata_utils.display_name_with_default_escaped(descendant),
descendant.location
)
scores.append(weighted_location_score)
locations_to_weighted_scores[descendant.location] = weighted_location_score
escaped_section_name = block_metadata_utils.display_name_with_default_escaped(section)
section_total, _ = graders.aggregate_scores(scores, escaped_section_name)
sections.append({
'display_name': escaped_section_name,
'url_name': block_metadata_utils.url_name_for_block(section),
'scores': scores,
'section_total': section_total,
'format': getattr(section, 'format', ''),
'due': getattr(section, 'due', None),
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default_escaped,
'display_name': block_metadata_utils.display_name_with_default_escaped(chapter),
'url_name': block_metadata_utils.url_name_for_block(chapter),
'sections': sections
})
return ProgressSummary(chapters, locations_to_weighted_scores, course_structure.get_children)
def _grade(student, request, course, keep_raw_scores, field_data_cache, scores_client):
"""
Unwrapped version of "grade"
This grades a student as quickly as possible. It returns the
output from the course grader, augmented with the final letter
grade. The keys in the output are:
course: a CourseDescriptor
- grade : A final letter grade.
- percent : The final percent for the class (rounded up).
- section_breakdown : A breakdown of each section that makes
up the grade. (For display)
- grade_breakdown : A breakdown of the major components that
make up the final grade. (For display)
- keep_raw_scores : if True, then value for key 'raw_scores' contains scores
for every graded module
More information on the format is in the docstring for CourseGrader.
"""
if field_data_cache is None:
with manual_transaction():
field_data_cache = field_data_cache_for_grading(course, student)
if scores_client is None:
scores_client = ScoresClient.from_field_data_cache(field_data_cache)
# Dict of item_ids -> (earned, possible) point tuples. This *only* grabs
# scores that were registered with the submissions API, which for the moment
# means only openassessment (edx-ora2)
submissions_scores = sub_api.get_scores(course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id))
max_scores_cache = MaxScoresCache.create_for_course(course)
# For the moment, we have to get scorable_locations from field_data_cache
# and not from scores_client, because scores_client is ignorant of things
# in the submissions API. As a further refactoring step, submissions should
# be hidden behind the ScoresClient.
max_scores_cache.fetch_from_remote(field_data_cache.scorable_locations)
grading_context = course.grading_context
raw_scores = []
totaled_scores = {}
# This next complicated loop is just to collect the totaled_scores, which is
# passed to the grader
for section_format, sections in grading_context['graded_sections'].iteritems():
format_scores = []
for section in sections:
section_descriptor = section['section_descriptor']
section_name = section_descriptor.display_name_with_default
# some problems have state that is updated independently of interaction
# with the LMS, so they need to always be scored. (E.g. foldit.,
# combinedopenended)
should_grade_section = any(
descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']
)
# If there are no problems that always have to be regraded, check to
# see if any of our locations are in the scores from the submissions
# API. If scores exist, we have to calculate grades for this section.
if not should_grade_section:
should_grade_section = any(
descriptor.location.to_deprecated_string() in submissions_scores
for descriptor in section['xmoduledescriptors']
)
if not should_grade_section:
should_grade_section = any(
descriptor.location in scores_client
for descriptor in section['xmoduledescriptors']
)
# If we haven't seen a single problem in the section, we don't have
# to grade it at all! We can assume 0%
if should_grade_section:
scores = []
def create_module(descriptor):
'''creates an XModule instance given a descriptor'''
# TODO: We need the request to pass into here. If we could forego that, our arguments
# would be simpler
return get_module_for_descriptor(
student, request, descriptor, field_data_cache, course.id, course=course
)
descendants = yield_dynamic_descriptor_descendants(section_descriptor, student.id, create_module)
for module_descriptor in descendants:
(correct, total) = get_score(
student,
module_descriptor,
create_module,
scores_client,
submissions_scores,
max_scores_cache,
)
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1), total + 1)
else:
correct = total
graded = module_descriptor.graded
if not total > 0:
# We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
scores.append(
Score(
correct,
total,
graded,
module_descriptor.display_name_with_default,
module_descriptor.location
)
)
__, graded_total = graders.aggregate_scores(scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name, None)
#Add the graded total to totaled_scores
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.info(
"Unable to grade a section with a total possible score of zero. " +
str(section_descriptor.location)
)
totaled_scores[section_format] = format_scores
# Grading policy might be overriden by a CCX, need to reset it
course.set_grading_policy(course.grading_policy)
grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)
# We round the grade here, to make sure that the grade is an whole percentage and
# doesn't get displayed differently than it gets grades
grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100
letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])
grade_summary['grade'] = letter_grade
grade_summary['totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
if keep_raw_scores:
# way to get all RAW scores out to instructor
# so grader can be double-checked
grade_summary['raw_scores'] = raw_scores
max_scores_cache.push_to_remote()
return grade_summary
def _calculate_totaled_scores(
student,
grading_context_result,
submissions_scores,
scores_client,
keep_raw_scores,
):
"""
Returns a tuple of totaled scores and raw scores, which can be passed to the grader.
"""
raw_scores = []
totaled_scores = {}
for section_format, sections in grading_context_result['all_graded_sections'].iteritems():
format_scores = []
for section_info in sections:
section = section_info['section_block']
section_name = block_metadata_utils.display_name_with_default(section)
with outer_atomic():
# Check to
# see if any of our locations are in the scores from the submissions
# API. If scores exist, we have to calculate grades for this section.
should_grade_section = any(
unicode(descendant.location) in submissions_scores
for descendant in section_info['scored_descendants']
)
if not should_grade_section:
should_grade_section = any(
descendant.location in scores_client
for descendant in section_info['scored_descendants']
)
# If we haven't seen a single problem in the section, we don't have
# to grade it at all! We can assume 0%
if should_grade_section:
scores = []
for descendant in section_info['scored_descendants']:
(correct, total) = get_score(
student,
descendant,
scores_client,
submissions_scores,
)
if correct is None and total is None:
continue
if settings.GENERATE_PROFILE_SCORES: # for debugging!
if total > 1:
correct = random.randrange(max(total - 2, 1), total + 1)
else:
correct = total
graded = descendant.graded
if not total > 0:
# We simply cannot grade a problem that is 12/0, because we might need it as a percentage
graded = False
scores.append(
Score(
correct,
total,
graded,
block_metadata_utils.display_name_with_default_escaped(descendant),
descendant.location
)
)
__, graded_total = graders.aggregate_scores(scores, section_name)
if keep_raw_scores:
raw_scores += scores
else:
graded_total = Score(0.0, 1.0, True, section_name, None)
# Add the graded total to totaled_scores
if graded_total.possible > 0:
format_scores.append(graded_total)
else:
log.info(
"Unable to grade a section with a total possible score of zero. " +
str(section.location)
)
totaled_scores[section_format] = format_scores
return totaled_scores, raw_scores
def _aggregate_scores(self):
return graders.aggregate_scores(self.problem_scores.values())
def _progress_summary(student, request, course):
"""
Unwrapped version of "progress_summary".
This pulls a summary of all problems in the course.
Returns
- courseware_summary is a summary of all sections with problems in the course.
It is organized as an array of chapters, each containing an array of sections,
each containing an array of scores. This contains information for graded and
ungraded problems, and is good for displaying a course summary with due dates,
etc.
Arguments:
student: A User object for the student to grade
course: A Descriptor containing the course to grade
If the student does not have access to load the course module, this function
will return None.
"""
with manual_transaction():
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, student, course, depth=None
)
# TODO: We need the request to pass into here. If we could
# forego that, our arguments would be simpler
course_module = get_module_for_descriptor(student, request, course, field_data_cache, course.id)
if not course_module:
# This student must not have access to the course.
return None
submissions_scores = sub_api.get_scores(course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id))
chapters = []
# Don't include chapters that aren't displayable (e.g. due to error)
for chapter_module in course_module.get_display_items():
# Skip if the chapter is hidden
if chapter_module.hide_from_toc:
continue
sections = []
for section_module in chapter_module.get_display_items():
# Skip if the section is hidden
with manual_transaction():
if section_module.hide_from_toc:
continue
graded = section_module.graded
scores = []
module_creator = section_module.xmodule_runtime.get_module
inforExerc = []
# Adicionarei mais algumas informacoes para que seja possivel pegar o que foi digitado pelo aluno
# A quantidade de tentativas, que opcao o usuario escolheu ou que informacao foi digitada
for module_descriptor in yield_dynamic_descriptor_descendents(section_module, module_creator):
course_id = course.id
(correct, total) = get_score(
course_id, student, module_descriptor, module_creator, scores_cache=submissions_scores
)
tentativas, valorDigitado, idProblem = get_InforExerc(
course_id, student, module_descriptor
)
if correct is None and total is None:
continue
print "USUARIO: ", student
print "TENTATIVAS ", tentativas, " VALOR DIGITADO: ", valorDigitado
print "CORRECT: ", correct, " total ", total
print "IDPROBLEM: ", idProblem
vals=[]
vals.append(tentativas)
vals.append(valorDigitado)
vals.append(idProblem)
inforExerc.append(vals)
scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
scores.reverse()
inforExerc.reverse()
section_total, _ = graders.aggregate_scores(
scores, section_module.display_name_with_default)
module_format = section_module.format if section_module.format is not None else ''
sections.append({
'display_name': section_module.display_name_with_default,
'url_name': section_module.url_name,
'scores': scores,
'inforExerc': inforExerc,
'section_total': section_total,
'format': module_format,
'due': get_extended_due_date(section_module),
'graded': graded,
})
chapters.append({
'course': course.display_name_with_default,
'display_name': chapter_module.display_name_with_default,
'url_name': chapter_module.url_name,
'sections': sections
})
return chapters
