def parse_activity_scores(self, activity_attempt):
'''
Processes activity scores recieved from the mapper.
This is called in the mapper callback function. Each time a student attempts
a GCB question or a Quizly exercise, a tag-assessment Event is created.
This processes such events to extract the number of attempts the student
made and the answers.
Events are time-stamped and recorded by user_id. They include the instance_id
of the Component that triggered the Event. Both GCB questions and Quizly
exercises have an instance_id.
However, Quizly exercises don't have question_id and need special processing.
Use the Dashboard to see what the data looks like for Events:
https://console.cloud.google.com/datastore/entities/query?
project=ram8647&ns=ns_mobileCSP&kind=EventEntity
'''
if activity_attempt.source == 'tag-assessment':
data = transforms.loads(activity_attempt.data)
instance_id = data['instanceid']
if GLOBAL_DEBUG:
logging.debug('***********RAM************** data[instanceid] = ' + instance_id)
timestamp = int(
(activity_attempt.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
# Get information about the course's questions (doesn't include Quizly exercises yet)
questions = self.params['questions_by_usage_id']
valid_question_ids = self.params['valid_question_ids']
assessment_weights = self.params['assessment_weights']
group_to_questions = self.params['group_to_questions']
student = Student.get_by_user_id(activity_attempt.user_id)
# Get this student's answers so far
student_answers = self.activity_scores.get(student.email, {})
if GLOBAL_DEBUG:
logging.debug('***RAM*** student answers = ' + str(student_answers))
answers = event_transforms.unpack_check_answers( # No Quizly answers in here
data, questions, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
# Add the score to right lesson
# NOTE: This was throwing an exception on Quizly exercises. Shouldn't happen now
try:
# If the event is tag-assessment and has no quid, it's a Quizly exercise
if not 'quid' in data:
self.parse_quizly_scores(data, instance_id, timestamp, student, student_answers)
else:
self.parse_question_scores(instance_id, questions, student_answers, answers, student, timestamp)
except Exception as e:
logging.error('***********RAM************** bad instance_id: %s %s\n%s', str(instance_id), e, traceback.format_exc())
if GLOBAL_DEBUG:
logging.debug('***RAM*** activity_scores ' + str(self.activity_scores))
return self.activity_scores
def map(cls, event):
"""Extract question responses from all event types providing them."""
if event.source not in (
'submit-assessment',
'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
valid_question_ids = params['valid_question_ids']
group_to_questions = params['group_to_questions']
assessment_weights = params['assessment_weights']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
yield (RawAnswersGenerator.TOTAL_STUDENTS, event.user_id)
# Each answer is a namedtuple; convert to a list for pack/unpack
# journey through the map/reduce shuffle stage.
result = [list(answer) for answer in answers]
for key in cls._generate_keys(event, event.user_id):
yield (key, result)
def map(cls, event):
"""Extract question responses from all event types providing them."""
if event.source not in ('submit-assessment', 'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
valid_question_ids = params['valid_question_ids']
group_to_questions = params['group_to_questions']
assessment_weights = params['assessment_weights']
timestamp = int((event.recorded_on -
datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids,
assessment_weights, group_to_questions, timestamp)
yield (RawAnswersGenerator.TOTAL_STUDENTS, event.user_id)
# Each answer is a namedtuple; convert to a list for pack/unpack
# journey through the map/reduce shuffle stage.
result = [list(answer) for answer in answers]
for key in cls._generate_keys(event, event.user_id):
yield (key, result)
def parse_activity_scores(self, activity_attempt):
if activity_attempt.source == 'tag-assessment':
data = transforms.loads(activity_attempt.data)
timestamp = int(
(activity_attempt.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
questions = self.params['questions_by_usage_id']
valid_question_ids = self.params['valid_question_ids']
assessment_weights = self.params['assessment_weights']
group_to_questions = self.params['group_to_questions']
student_answers = self.activity_scores.get(Student.get_student_by_user_id(activity_attempt.user_id).email, {})
answers = event_transforms.unpack_check_answers(
data, questions, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
#add score to right lesson
question_info = questions[data['instanceid']]
unit_answers = student_answers.get(question_info['unit'], {})
lesson_answers = unit_answers.get(question_info['lesson'], {})
for answer in answers:
question_answer_dict = {}
question_answer_dict['unit_id'] = answer.unit_id
question_answer_dict['lesson_id'] = answer.lesson_id
question_answer_dict['sequence'] = answer.sequence
question_answer_dict['question_id'] = answer.question_id
question_answer_dict['question_type'] = answer.question_type
question_answer_dict['timestamp'] = answer.timestamp
question_answer_dict['answers'] = answer.answers
question_answer_dict['score'] = answer.score
question_answer_dict['weighted_score'] = answer.weighted_score
question_answer_dict['tallied'] = answer.tallied
if answer.sequence in lesson_answers and lesson_answers[answer.sequence] < timestamp:
lesson_answers[answer.sequence] = question_answer_dict
elif answer.sequence not in lesson_answers:
lesson_answers[answer.sequence] = question_answer_dict
unit_answers[question_info['lesson']] = lesson_answers
student_answers[question_info['unit']] = unit_answers
self.activity_scores[Student.get_student_by_user_id(activity_attempt.user_id).email] = student_answers
return self.activity_scores
def map(event):
"""Extract question responses from all event types providing them."""
if event.source not in (
'submit-assessment',
'attempt-lesson',
'tag-assessment'):
return
# Fetch global params set up in build_additional_mapper_params(), above.
params = context.get().mapreduce_spec.mapper.params
questions_info = params['questions_by_usage_id']
group_to_questions = params['group_to_questions']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, answer_data, timestamp)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, content, timestamp)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, group_to_questions, timestamp)
yield (event.user_id, [list(answer) for answer in answers])
def parse_activity_scores(self, activity_attempt):
'''
Processes activity scores recieved from the mapper.
This is called in the mapper callback function. Each time a student attempts
a GCB question or a Quizly exercise, a tag-assessment Event is created.
This processes such events to extract the number of attempts the student
made and the answers.
Events are time-stamped and recorded by user_id. They include the instance_id
of the Component that triggered the Event. Both GCB questions and Quizly
exercises have an instance_id.
However, Quizly exercises don't have question_id and need special processing.
Use the Dashboard to see what the data looks like for Events:
https://console.cloud.google.com/datastore/entities/query?
project=ram8647&ns=ns_mobileCSP&kind=EventEntity
'''
if activity_attempt.source == 'tag-assessment':
data = transforms.loads(activity_attempt.data)
instance_id = data['instanceid']
if GLOBAL_DEBUG:
logging.debug(
'***********RAM************** data[instanceid] = ' +
instance_id)
timestamp = int((activity_attempt.recorded_on -
datetime.datetime(1970, 1, 1)).total_seconds())
# Get information about the course's questions (doesn't include Quizly exercises yet)
questions = self.params['questions_by_usage_id']
valid_question_ids = self.params['valid_question_ids']
assessment_weights = self.params['assessment_weights']
group_to_questions = self.params['group_to_questions']
student = Student.get_by_user_id(activity_attempt.user_id)
# Get this student's answers so far
student_answers = self.activity_scores.get(student.email, {})
if GLOBAL_DEBUG:
logging.debug('***RAM*** student answers = ' +
str(student_answers))
answers = event_transforms.unpack_check_answers( # No Quizly answers in here
data, questions, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
# Add the score to right lesson
# NOTE: This was throwing an exception on Quizly exercises. Shouldn't happen now
try:
# If the event is tag-assessment and has no quid, it's a Quizly exercise
if not 'quid' in data:
self.parse_quizly_scores(data, instance_id, timestamp,
student, student_answers)
else:
self.parse_question_scores(instance_id, questions,
student_answers, answers,
student, timestamp)
except Exception as e:
logging.error(
'***********RAM************** bad instance_id: %s %s\n%s',
str(instance_id), e, traceback.format_exc())
if GLOBAL_DEBUG:
logging.debug('***RAM*** activity_scores ' +
str(self.activity_scores))
return self.activity_scores
def process_event(cls, event, static_params):
questions_info = static_params['questions_by_usage_id']
valid_question_ids = static_params['valid_question_ids']
group_to_questions = static_params['group_to_questions']
assessment_weights = static_params['assessment_weights']
timestamp = int(
(event.recorded_on - datetime.datetime(1970, 1, 1)).total_seconds())
content = transforms.loads(event.data)
answers = None
if event.source == 'submit-assessment':
answer_data = content.get('values', {})
# TODO(mgainer): handle assessment-as-form submissions. Current
# implementation only understands Question and QuestionGroup;
# forms are simply submitted as lists of fields.
# TODO(mgainer): Handle peer-review scoring
if not isinstance(answer_data, dict):
return
version = answer_data.get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, answer_data, timestamp)
else:
logging.warning('Unexpected version %s in submit-assessment '
'event handling', version)
elif event.source == 'attempt-lesson':
# Very odd that the version should be in the answers map....
version = content.get('answers', {}).get('version')
if version == '1.5':
answers = event_transforms.unpack_student_answer_1_5(
questions_info, valid_question_ids, assessment_weights,
group_to_questions, content, timestamp)
else:
logging.warning('Unexpected version %s in attempt-lesson '
'event handling', version)
elif event.source == 'tag-assessment':
answers = event_transforms.unpack_check_answers(
content, questions_info, valid_question_ids, assessment_weights,
group_to_questions, timestamp)
if not answers:
return None
answer_dicts = []
total_weighted_score = 0.0
for answer in answers:
if not isinstance(answer.answers, (tuple, list)):
stringified_answers = [unicode(answer.answers)]
else:
stringified_answers = [unicode(a) for a in answer.answers]
answer_dict = {
'question_id': answer.question_id,
'responses': stringified_answers,
}
answer_dict['score'] = float(answer.score)
answer_dict['weighted_score'] = float(answer.weighted_score)
total_weighted_score += answer.weighted_score
answer_dicts.append(answer_dict)
submission = {
'timestamp': answers[0].timestamp,
'answers': answer_dicts,
}
submission['weighted_score'] = total_weighted_score
assessment = {
'unit_id': str(answers[0].unit_id),
'lesson_id': str(answers[0].lesson_id),
'submissions': [submission],
}
return assessment