0565fbbfab
This restores functionality that has been broken since the introduction of XModuleDescriptor/XModule proxying (part of the XBlock transition). It generates a CSV of all answers for all content of type "problem" in a given course, with a row per (problem part, answer). The format is: url_name, display name, answer id, answer, count Example values: url_name = "7f1b1523a55848cd9f5c93eb8cbabcf7" display name = "Problem 1: Something Hard" answer id = i4x-JediAcdmy-LTSB304-problem-7f1b1523a55848cd9f5c93eb8cbabcf7_2_1 answer = "Use the Force" count = 1138 Since it only grabs things of type "problem", it will not return results for things like self/peer-assessments. Any Loncapa problem types will show up (so multiple choice, text input, numeric, etc.) Instead of crawling the course tree and instantiating the appropriate CapaModule objects to grab state, this version draws directly from StudentModule. This lets us skip a lot of processing and most importantly lets us generate the answer distribution without causing side-effects (since XBlocks auto-save state). It also lets us take advantage of a read-replica database if one is available, to minimize locking concerns. There are minor changes to the legacy dashboard around CSV charset encoding and a change to OptionResponseXMLFactory to make it more unicode friendly. Answer distribution output is now also sorted, to group together answers for the same content piece. Note that this does not introduce celery into the process. Answer distributions are still only available for small courses. This was originally created to fix [LMS-922], but it also addresses [LMS-811] and possibly other areas in the legacy dashboard where CSV downloads break due to character encoding issues.
521 lines
21 KiB
Python
521 lines
21 KiB
Python
# Compute grades using real division, with no integer truncation
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from __future__ import division
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from collections import defaultdict
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import json
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import random
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import logging
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from contextlib import contextmanager
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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 django.db import transaction
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from django.test.client import RequestFactory
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from dogapi import dog_stats_api
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from courseware import courses
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from courseware.model_data import FieldDataCache
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from xblock.fields import Scope
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from xmodule import graders
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from xmodule.graders import Score
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from xmodule.modulestore.django import modulestore
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from xmodule.modulestore.exceptions import ItemNotFoundError
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from .models import StudentModule
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from .module_render import get_module, get_module_for_descriptor
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log = logging.getLogger("edx.courseware")
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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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if module is None:
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return []
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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 answer_distributions(course_id):
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"""
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Given a course_id, return answer distributions in the form of a dictionary
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mapping:
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(problem url_name, problem display_name, problem_id) -> {dict: answer -> count}
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Answer distributions are found by iterating through all StudentModule
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entries for a given course with type="problem" and a grade that is not null.
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This means that we only count LoncapaProblems that people have submitted.
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Other types of items like ORA or sequences will not be collected. Empty
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Loncapa problem state that gets created from runnig the progress page is
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also not counted.
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This method accesses the StudentModule table directly instead of using the
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CapaModule abstraction. The main reason for this is so that we can generate
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the report without any side-effects -- we don't have to worry about answer
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distribution potentially causing re-evaluation of the student answer. This
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also allows us to use the read-replica database, which reduces risk of bad
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locking behavior. And quite frankly, it makes this a lot less confusing.
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Also, we're pulling all available records from the database for this course
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rather than crawling through a student's course-tree -- the latter could
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potentially cause us trouble with A/B testing. The distribution report may
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not be aware of problems that are not visible to the user being used to
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generate the report.
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This method will try to use a read-replica database if one is available.
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"""
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# dict: { module.module_state_key : (url_name, display_name) }
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state_keys_to_problem_info = {} # For caching, used by url_and_display_name
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def url_and_display_name(module_state_key):
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"""
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For a given module_state_key, return the problem's url and display_name.
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Handle modulestore access and caching. This method ignores permissions.
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May throw an ItemNotFoundError if there is no content that corresponds
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to this module_state_key.
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"""
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problem_store = modulestore()
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if module_state_key not in state_keys_to_problem_info:
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problems = problem_store.get_items(module_state_key, course_id=course_id, depth=1)
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if not problems:
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# Likely means that the problem was deleted from the course
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# after the student had answered. We log this suspicion where
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# this exception is caught.
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raise ItemNotFoundError(
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"Answer Distribution: Module {} not found for course {}"
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.format(module_state_key, course_id)
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)
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problem = problems[0]
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problem_info = (problem.url_name, problem.display_name_with_default)
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state_keys_to_problem_info[module_state_key] = problem_info
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return state_keys_to_problem_info[module_state_key]
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# Iterate through all problems submitted for this course in no particular
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# order, and build up our answer_counts dict that we will eventually return
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answer_counts = defaultdict(lambda: defaultdict(int))
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for module in StudentModule.all_submitted_problems_read_only(course_id):
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try:
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state_dict = json.loads(module.state) if module.state else {}
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raw_answers = state_dict.get("student_answers", {})
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except ValueError:
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log.error(
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"Answer Distribution: Could not parse module state for " +
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"StudentModule id={}, course={}".format(module.id, course_id)
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)
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continue
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# Each problem part has an ID that is derived from the
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# module.module_state_key (with some suffix appended)
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for problem_part_id, raw_answer in raw_answers.items():
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# Convert whatever raw answers we have (numbers, unicode, None, etc.)
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# to be unicode values. Note that if we get a string, it's always
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# unicode and not str -- state comes from the json decoder, and that
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# always returns unicode for strings.
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answer = unicode(raw_answer)
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try:
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url, display_name = url_and_display_name(module.module_state_key)
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except ItemNotFoundError:
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msg = "Answer Distribution: Item {} referenced in StudentModule {} " + \
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"for user {} in course {} not found; " + \
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"This can happen if a student answered a question that " + \
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"was later deleted from the course. This answer will be " + \
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"omitted from the answer distribution CSV."
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log.warning(
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msg.format(module.module_state_key, module.id, module.student_id, course_id)
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)
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continue
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answer_counts[(url, display_name, problem_part_id)][answer] += 1
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return answer_counts
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@transaction.commit_manually
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def grade(student, request, course, keep_raw_scores=False):
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"""
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Wraps "_grade" with the manual_transaction context manager just in case
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there are unanticipated errors.
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"""
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with manual_transaction():
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return _grade(student, request, course, keep_raw_scores)
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def _grade(student, request, course, keep_raw_scores):
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"""
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Unwrapped version of "grade"
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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
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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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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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# 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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# combinedopenended)
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should_grade_section = any(
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descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']
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)
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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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if not should_grade_section:
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with manual_transaction():
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should_grade_section = StudentModule.objects.filter(
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student=student,
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module_state_key__in=[
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descriptor.location for descriptor in section['xmoduledescriptors']
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]
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).exists()
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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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with manual_transaction():
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field_data_cache = FieldDataCache([descriptor], course.id, student)
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return get_module_for_descriptor(student, request, descriptor, field_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)
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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.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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@transaction.commit_manually
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def progress_summary(student, request, course):
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"""
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Wraps "_progress_summary" with the manual_transaction context manager just
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in case there are unanticipated errors.
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"""
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with manual_transaction():
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return _progress_summary(student, request, course)
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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):
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"""
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Unwrapped version of "progress_summary".
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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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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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with manual_transaction():
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field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
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course.id, student, course, depth=None
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)
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# TODO: We need the request to pass into here. If we could
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# forego that, our arguments would be simpler
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course_module = get_module_for_descriptor(student, request, course, field_data_cache, course.id)
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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.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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with manual_transaction():
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if section_module.hide_from_toc:
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continue
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graded = section_module.graded
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scores = []
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module_creator = section_module.xmodule_runtime.get_module
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for module_descriptor in yield_dynamic_descriptor_descendents(section_module, 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)
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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.format if section_module.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.due,
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'graded': graded,
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})
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chapters.append({
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'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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})
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return chapters
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def get_score(course_id, user, problem_descriptor, module_creator):
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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 FieldDataCache
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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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if problem is None:
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return (None, None)
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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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try:
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student_module = StudentModule.objects.get(
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student=user,
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course_id=course_id,
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module_state_key=problem_descriptor.location
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)
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except StudentModule.DoesNotExist:
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student_module = None
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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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@contextmanager
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def manual_transaction():
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"""A context manager for managing manual transactions"""
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try:
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yield
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except Exception:
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transaction.rollback()
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log.exception('Due to an error, this transaction has been rolled back')
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raise
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else:
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transaction.commit()
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def iterate_grades_for(course_id, students):
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"""Given a course_id and an iterable of students (User), yield a tuple of:
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(student, gradeset, err_msg) for every student enrolled in the course.
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If an error occured, gradeset will be an empty dict and err_msg will be an
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exception message. If there was no error, err_msg is an empty string.
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The gradeset is a dictionary with the following fields:
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- 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)
|
|
- raw_scores: contains scores for every graded module
|
|
"""
|
|
course = courses.get_course_by_id(course_id)
|
|
|
|
# We make a fake request because grading code expects to be able to look at
|
|
# the request. We have to attach the correct user to the request before
|
|
# grading that student.
|
|
request = RequestFactory().get('/')
|
|
|
|
for student in students:
|
|
with dog_stats_api.timer('lms.grades.iterate_grades_for', tags=['action:{}'.format(course_id)]):
|
|
try:
|
|
request.user = student
|
|
# Grading calls problem rendering, which calls masquerading,
|
|
# which checks session vars -- thus the empty session dict below.
|
|
# It's not pretty, but untangling that is currently beyond the
|
|
# scope of this feature.
|
|
request.session = {}
|
|
gradeset = grade(student, request, course)
|
|
yield student, gradeset, ""
|
|
except Exception as exc: # pylint: disable=broad-except
|
|
# Keep marching on even if this student couldn't be graded for
|
|
# some reason, but log it for future reference.
|
|
log.exception(
|
|
'Cannot grade student %s (%s) in course %s because of exception: %s',
|
|
student.username,
|
|
student.id,
|
|
course_id,
|
|
exc.message
|
|
)
|
|
yield student, {}, exc.message
|