e791e84ea7
stores_state is now only used in a single test, in conjunction with has_score. In practice, it's never the case that stores_state is false when has_score is true, so we can delete stores_state entirely, and just use has_score for the grading test.
410 lines
16 KiB
Python
410 lines
16 KiB
Python
# Compute grades using real division, with no integer truncation
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from __future__ import division
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import random
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import logging
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from collections import defaultdict
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from django.conf import settings
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from django.contrib.auth.models import User
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from .model_data import ModelDataCache, LmsKeyValueStore
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from xblock.core import Scope
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from .module_render import get_module, get_module_for_descriptor
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from xmodule import graders
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from xmodule.capa_module import CapaModule
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from xmodule.graders import Score
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from .models import StudentModule
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log = logging.getLogger("mitx.courseware")
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def yield_module_descendents(module):
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stack = module.get_display_items()
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stack.reverse()
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while len(stack) > 0:
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next_module = stack.pop()
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stack.extend(next_module.get_display_items())
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yield next_module
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def yield_dynamic_descriptor_descendents(descriptor, module_creator):
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"""
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This returns all of the descendants of a descriptor. If the descriptor
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has dynamic children, the module will be created using module_creator
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and the children (as descriptors) of that module will be returned.
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"""
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def get_dynamic_descriptor_children(descriptor):
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if descriptor.has_dynamic_children():
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module = module_creator(descriptor)
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return module.get_child_descriptors()
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else:
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return descriptor.get_children()
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stack = [descriptor]
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while len(stack) > 0:
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next_descriptor = stack.pop()
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stack.extend(get_dynamic_descriptor_children(next_descriptor))
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yield next_descriptor
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def yield_problems(request, course, student):
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"""
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Return an iterator over capa_modules that this student has
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potentially answered. (all that student has answered will definitely be in
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the list, but there may be others as well).
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"""
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grading_context = course.grading_context
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descriptor_locations = (descriptor.location.url() for descriptor in grading_context['all_descriptors'])
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existing_student_modules = set(StudentModule.objects.filter(
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module_state_key__in=descriptor_locations
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).values_list('module_state_key', flat=True))
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sections_to_list = []
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for _, sections in grading_context['graded_sections'].iteritems():
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for section in sections:
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section_descriptor = section['section_descriptor']
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# If the student hasn't seen a single problem in the section, skip it.
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for moduledescriptor in section['xmoduledescriptors']:
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if moduledescriptor.location.url() in existing_student_modules:
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sections_to_list.append(section_descriptor)
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break
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model_data_cache = ModelDataCache(sections_to_list, course.id, student)
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for section_descriptor in sections_to_list:
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section_module = get_module(student, request,
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section_descriptor.location, model_data_cache,
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course.id)
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if section_module is None:
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# student doesn't have access to this module, or something else
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# went wrong.
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# log.debug("couldn't get module for student {0} for section location {1}"
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# .format(student.username, section_descriptor.location))
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continue
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for problem in yield_module_descendents(section_module):
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if isinstance(problem, CapaModule):
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yield problem
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def answer_distributions(request, course):
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"""
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Given a course_descriptor, compute frequencies of answers for each problem:
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Format is:
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dict: (problem url_name, problem display_name, problem_id) -> (dict : answer -> count)
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TODO (vshnayder): this is currently doing a full linear pass through all
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students and all problems. This will be just a little slow.
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"""
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counts = defaultdict(lambda: defaultdict(int))
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enrolled_students = User.objects.filter(courseenrollment__course_id=course.id)
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for student in enrolled_students:
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for capa_module in yield_problems(request, course, student):
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for problem_id in capa_module.lcp.student_answers:
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# Answer can be a list or some other unhashable element. Convert to string.
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answer = str(capa_module.lcp.student_answers[problem_id])
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key = (capa_module.url_name, capa_module.display_name_with_default, problem_id)
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counts[key][answer] += 1
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return counts
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def grade(student, request, course, model_data_cache=None, keep_raw_scores=False):
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"""
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This grades a student as quickly as possible. It returns the
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output from the course grader, augmented with the final letter
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grade. The keys in the output are:
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course: a CourseDescriptor
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- grade : A final letter grade.
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- percent : The final percent for the class (rounded up).
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- section_breakdown : A breakdown of each section that makes
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up the grade. (For display)
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- grade_breakdown : A breakdown of the major components that
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make up the final grade. (For display)
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- keep_raw_scores : if True, then value for key 'raw_scores' contains scores for every graded module
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More information on the format is in the docstring for CourseGrader.
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"""
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grading_context = course.grading_context
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raw_scores = []
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if model_data_cache is None:
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model_data_cache = ModelDataCache(grading_context['all_descriptors'], course.id, student)
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totaled_scores = {}
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# This next complicated loop is just to collect the totaled_scores, which is
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# passed to the grader
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for section_format, sections in grading_context['graded_sections'].iteritems():
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format_scores = []
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for section in sections:
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section_descriptor = section['section_descriptor']
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section_name = section_descriptor.display_name_with_default
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should_grade_section = False
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# If we haven't seen a single problem in the section, we don't have to grade it at all! We can assume 0%
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for moduledescriptor in section['xmoduledescriptors']:
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# some problems have state that is updated independently of interaction
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# with the LMS, so they need to always be scored. (E.g. foldit.)
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if moduledescriptor.always_recalculate_grades:
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should_grade_section = True
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break
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# Create a fake key to pull out a StudentModule object from the ModelDataCache
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key = LmsKeyValueStore.Key(
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Scope.user_state,
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student.id,
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moduledescriptor.location,
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None
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)
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if model_data_cache.find(key):
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should_grade_section = True
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break
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if should_grade_section:
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scores = []
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def create_module(descriptor):
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'''creates an XModule instance given a descriptor'''
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# TODO: We need the request to pass into here. If we could forego that, our arguments
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# would be simpler
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return get_module_for_descriptor(student, request, descriptor, model_data_cache, course.id)
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for module_descriptor in yield_dynamic_descriptor_descendents(section_descriptor, create_module):
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(correct, total) = get_score(course.id, student, module_descriptor, create_module, model_data_cache)
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if correct is None and total is None:
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continue
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if settings.GENERATE_PROFILE_SCORES: # for debugging!
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if total > 1:
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correct = random.randrange(max(total - 2, 1), total + 1)
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else:
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correct = total
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graded = module_descriptor.lms.graded
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if not total > 0:
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#We simply cannot grade a problem that is 12/0, because we might need it as a percentage
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graded = False
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scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
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_, graded_total = graders.aggregate_scores(scores, section_name)
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if keep_raw_scores:
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raw_scores += scores
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else:
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graded_total = Score(0.0, 1.0, True, section_name)
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#Add the graded total to totaled_scores
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if graded_total.possible > 0:
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format_scores.append(graded_total)
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else:
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log.exception("Unable to grade a section with a total possible score of zero. " +
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str(section_descriptor.location))
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totaled_scores[section_format] = format_scores
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grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)
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# We round the grade here, to make sure that the grade is an whole percentage and
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# doesn't get displayed differently than it gets grades
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grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100
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letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])
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grade_summary['grade'] = letter_grade
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grade_summary['totaled_scores'] = totaled_scores # make this available, eg for instructor download & debugging
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if keep_raw_scores:
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grade_summary['raw_scores'] = raw_scores # way to get all RAW scores out to instructor
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# so grader can be double-checked
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return grade_summary
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def grade_for_percentage(grade_cutoffs, percentage):
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"""
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Returns a letter grade as defined in grading_policy (e.g. 'A' 'B' 'C' for 6.002x) or None.
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Arguments
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- grade_cutoffs is a dictionary mapping a grade to the lowest
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possible percentage to earn that grade.
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- percentage is the final percent across all problems in a course
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"""
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letter_grade = None
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# Possible grades, sorted in descending order of score
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descending_grades = sorted(grade_cutoffs, key=lambda x: grade_cutoffs[x], reverse=True)
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for possible_grade in descending_grades:
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if percentage >= grade_cutoffs[possible_grade]:
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letter_grade = possible_grade
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break
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return letter_grade
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# TODO: This method is not very good. It was written in the old course style and
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# then converted over and performance is not good. Once the progress page is redesigned
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# to not have the progress summary this method should be deleted (so it won't be copied).
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def progress_summary(student, request, course, model_data_cache):
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"""
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This pulls a summary of all problems in the course.
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Returns
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- courseware_summary is a summary of all sections with problems in the course.
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It is organized as an array of chapters, each containing an array of sections,
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each containing an array of scores. This contains information for graded and
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ungraded problems, and is good for displaying a course summary with due dates,
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etc.
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Arguments:
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student: A User object for the student to grade
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course: A Descriptor containing the course to grade
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model_data_cache: A ModelDataCache initialized with all
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instance_modules for the student
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If the student does not have access to load the course module, this function
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will return None.
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"""
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# TODO: We need the request to pass into here. If we could forego that, our arguments
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# would be simpler
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course_module = get_module(student, request, course.location, model_data_cache, course.id, depth=None)
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if not course_module:
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# This student must not have access to the course.
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return None
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chapters = []
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# Don't include chapters that aren't displayable (e.g. due to error)
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for chapter_module in course_module.get_display_items():
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# Skip if the chapter is hidden
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if chapter_module.lms.hide_from_toc:
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continue
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sections = []
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for section_module in chapter_module.get_display_items():
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# Skip if the section is hidden
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if section_module.lms.hide_from_toc:
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continue
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# Same for sections
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graded = section_module.lms.graded
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scores = []
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module_creator = section_module.system.get_module
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for module_descriptor in yield_dynamic_descriptor_descendents(section_module.descriptor, module_creator):
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course_id = course.id
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(correct, total) = get_score(course_id, student, module_descriptor, module_creator, model_data_cache)
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if correct is None and total is None:
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continue
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scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))
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scores.reverse()
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section_total, _ = graders.aggregate_scores(
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scores, section_module.display_name_with_default)
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module_format = section_module.lms.format if section_module.lms.format is not None else ''
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sections.append({
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'display_name': section_module.display_name_with_default,
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'url_name': section_module.url_name,
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'scores': scores,
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'section_total': section_total,
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'format': module_format,
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'due': section_module.lms.due,
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'graded': graded,
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})
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chapters.append({'course': course.display_name_with_default,
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'display_name': chapter_module.display_name_with_default,
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'url_name': chapter_module.url_name,
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'sections': sections})
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return chapters
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def get_score(course_id, user, problem_descriptor, module_creator, model_data_cache):
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"""
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Return the score for a user on a problem, as a tuple (correct, total).
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e.g. (5,7) if you got 5 out of 7 points.
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If this problem doesn't have a score, or we couldn't load it, returns (None,
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None).
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user: a Student object
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problem_descriptor: an XModuleDescriptor
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module_creator: a function that takes a descriptor, and returns the corresponding XModule for this user.
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Can return None if user doesn't have access, or if something else went wrong.
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cache: A ModelDataCache
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"""
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if not user.is_authenticated():
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return (None, None)
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# some problems have state that is updated independently of interaction
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# with the LMS, so they need to always be scored. (E.g. foldit.)
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if problem_descriptor.always_recalculate_grades:
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problem = module_creator(problem_descriptor)
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score = problem.get_score()
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if score is not None:
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return (score['score'], score['total'])
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else:
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return (None, None)
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if not problem_descriptor.has_score:
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# These are not problems, and do not have a score
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return (None, None)
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# Create a fake KeyValueStore key to pull out the StudentModule
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key = LmsKeyValueStore.Key(
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Scope.user_state,
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user.id,
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problem_descriptor.location,
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None
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)
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student_module = model_data_cache.find(key)
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if student_module is not None and student_module.max_grade is not None:
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correct = student_module.grade if student_module.grade is not None else 0
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total = student_module.max_grade
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else:
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# If the problem was not in the cache, or hasn't been graded yet,
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# we need to instantiate the problem.
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# Otherwise, the max score (cached in student_module) won't be available
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problem = module_creator(problem_descriptor)
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if problem is None:
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return (None, None)
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correct = 0.0
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total = problem.max_score()
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# Problem may be an error module (if something in the problem builder failed)
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# In which case total might be None
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if total is None:
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return (None, None)
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# Now we re-weight the problem, if specified
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weight = problem_descriptor.weight
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if weight is not None:
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if total == 0:
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log.exception("Cannot reweight a problem with zero total points. Problem: " + str(student_module))
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return (correct, total)
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correct = correct * weight / total
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total = weight
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return (correct, total)
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