def add_header_text_to_card(card, user_exercise):
"""
Adds header text to a problem card based on
A/B test buckets for the "intervention" set of experiments.
"""
# use the same ab_test categories for all experiments
core_categories = ["learning_dashboard", "exercises"]
# the unique identifier for this user
bingo_id = identity.identity()
# get top level A/B test condition
primary_condition = experiments.CoreMetrics.ab_test(
"intervention top level",
alternative_params={
"no header": 4,
"intervention": 1,
"combination": 1},
core_categories=core_categories)
event_log.log_event('x.intervention.primary', primary_condition)
if primary_condition == "no header":
# student is in control condition
card.growthHeader = ""
intervention_logging(card, user_exercise)
return
# choose the frequency with which to insert a header
# above exercises
frequency_condition = experiments.CoreMetrics.ab_test(
"intervention frequency",
alternative_params={
"0-10": 25,
"10-25": 25,
"25-75": 25,
"75-100": 25},
core_categories=core_categories)
# by having continuous frequencies, we can smoothly plot effectiveness
# vs. frequency later
if frequency_condition == "0-10":
frequency = hash_to_rand(bingo_id, 'freq') * 0.1
elif frequency_condition == "10-25":
frequency = hash_to_rand(bingo_id, 'freq') * 0.15 + 0.1
elif frequency_condition == "25-75":
frequency = hash_to_rand(bingo_id, 'freq') * 0.50 + 0.25
elif frequency_condition == "75-100":
frequency = hash_to_rand(bingo_id, 'freq') * 0.25 + 0.75
event_log.log_event('x.intervention.frequency', frequency_condition)
if frequency < random.random():
# no header for this exercise
card.growthHeader = ""
event_log.log_event('x.intervention.frequency.skip', 'True')
intervention_logging(card, user_exercise)
return
# choose what kind of psychological intervention to apply
alternative_params = {
"repeat experiment": 1,
"whatwhyhow": 1,
"individual explanation": 1}
if primary_condition == "intervention":
intervention_type_condition = experiments.CoreMetrics.ab_test(
"intervention type",
alternative_params=alternative_params,
core_categories=core_categories)
elif primary_condition == "combination":
# in the combination condition, set the subexperiment at
# random each time
intervention_type_condition = random_weighted_choice(
alternative_params)
event_log.log_event('x.intervention.type', intervention_type_condition)
if intervention_type_condition == 'repeat experiment':
# repeat experiment means conditions which are duplicated from the
# prior growth mindset experiment.
# This is a nested ab test.
# the positive statement had a small negative effect in the last
# experiment, but it's good to have as a control, so we'll only
# assign a small fraction of students to it this time
alternative_params = {
"growth mindset": 10,
"growth mindset + link": 10,
"science statement": 10,
"positive statement": 1,
}
if primary_condition == "intervention":
intervention_repeat_condition = experiments.CoreMetrics.ab_test(
"intervention repeat experiment",
alternative_params=alternative_params,
core_categories=core_categories)
elif primary_condition == "combination":
# in the combination condition, set the subexperiment at
# random each time
intervention_repeat_condition = random_weighted_choice(
alternative_params)
event_log.log_event('x.intervention.repeat.type',
intervention_repeat_condition)
# set the message text for each condition
if intervention_repeat_condition == "growth mindset":
message_text = random.choice(growth_messages)
card.growthHeader = "<p><em>" + message_text + "</em></p>"
elif intervention_repeat_condition == "growth mindset + link":
message_text = random.choice(growth_messages)
message_text = (i18n._('<p><em>%(message)s</em>'
'   <FONT SIZE="-5">'
'<a href=/brainworkout_1 target="_blank">'
'LEARN MORE</a>'
'</FONT></p>', message=message_text))
elif intervention_repeat_condition == "science statement":
message_text = random.choice(science_messages)
message_text = "<p><em>" + message_text + "</em></p>"
elif intervention_repeat_condition == "positive statement":
message_text = random.choice(positive_messages)
message_text = "<p><em>" + message_text + "</em></p>"
elif intervention_type_condition == 'whatwhyhow':
# nearly identical to the whatwhyhow condition that was run in the
# metacognitive experiment
message_text = whatwhyhow_body
elif intervention_type_condition == 'individual explanation':
# more light weight explanation effect hints.
# choose the target of address for the explanation.
alternative_params = {
"self": 1,
"teacher": 1,
"sal": 1,
"friend": 1,
}
if primary_condition == "intervention":
target_condition = experiments.CoreMetrics.ab_test(
"intervention repeat experiment",
alternative_params=alternative_params,
core_categories=core_categories)
elif primary_condition == "combination":
# in the combination condition, set the subexperiment at
# random each time
target_condition = random_weighted_choice(alternative_params)
target_text = explanation_prompt_target_text[target_condition]
question_text = random.choice(explanation_prompt_messages)
message_text = target_text + question_text
event_log.log_event('x.intervention.explanation.target',
target_condition)
event_log.log_event('x.intervention.explanation.question',
question_text)
# choose whether there's a dropdown
dropdown_condition = experiments.CoreMetrics.ab_test(
"intervention dropdown",
alternative_params={
"none": 1,
"message": 1,
"specific": 1
},
core_categories=core_categories)
if intervention_type_condition == "whatwhyhow" and (
dropdown_condition == 'none'):
# whatwhyhow is a lot of text, and should always appear in a dropdown
dropdown_condition = 'specific'
event_log.log_event('x.intervention.dropdown', dropdown_condition)
# choose the teaser text for the dropdown
# TODO add bold+color to all dropdown text?
if dropdown_condition == "message":
dropdown_text = i18n._("[Click here for a message.]")
elif dropdown_condition == "specific":
if intervention_type_condition == 'repeat experiment':
if intervention_repeat_condition == "science statement":
dropdown_text = i18n._("[Click to learn a fun science fact.]")
else:
dropdown_text = i18n._(
"[Click to read a brief motivational message.]")
elif intervention_type_condition == 'whatwhyhow':
dropdown_text = i18n._('[Click to learn about the "<span class=' +
'"hint_purple" style="font-weight: bold">What? Why? How?' +
'</span>" strategy.]')
elif intervention_type_condition == 'individual explanation':
dropdown_text = i18n._("[Click for a learning strategy question.]")
# combine the dropdown label and the message text
if dropdown_condition == 'none':
# just the message text
card.growthHeader = message_text
else:
card.growthHeader = """
<p>
<a href="#" class="show-subhint"
data-subhint="intervention-learn-more"
data-hidden-text="Hide Information">
%s
</a>
</p>
<div class="subhint" id="intervention-learn-more">
%s
</div>""" % (dropdown_text, message_text)
intervention_logging(card, user_exercise)
"""
Code to take a problem card, and select and add motivational text to
it. For use in the header text intervention A/B test.
"""
import event_log
import experiments
from intl import i18n
import intl.request
import random
from gae_bingo import identity
import hashlib
explanation_prompt_target_text = {
"self": i18n._("While you are solving the problem, ask yourself: "),
"teacher": i18n._("What would you tell your teacher if they asked you "
"while you were solving the problem: "),
"sal": i18n._("What would you say if Sal asked you while you were solving "
"the problem: "),
"friend": i18n._("While you are solving the problem, how would you answer "
"another student who asked: "),
}
explanation_prompt_messages = [
i18n._("What are you doing or thinking right now?"),
i18n._("Why is what you are currently doing helpful?"),
i18n._("Why is what you are currently doing useful for achieving your "
"goal?"),
i18n._("How well is your current approach to this problem working?"),
i18n._("For each step, what does this step mean to you?"),
