{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from __future__ import division\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from collections import OrderedDict\n",
"\n",
"import plotly\n",
"import plotly.offline as py\n",
"import plotly.graph_objs as go\n",
"import plotly.tools as tls\n",
"\n",
"py.init_notebook_mode() # graphs charts inline (IPython)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Course structure: Climate vs. China300.1x\n",
"### By chapter"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def query_moduleActivity(course_id):\n",
" \"\"\"\n",
" Give the course_id, query # students with any activity, # students attempted any problem \n",
" and # students watched any video for each chapter of the course\n",
" \"\"\"\n",
" # query # students attempted any problem for each chapter of the course, exclude those with less than 20 attempts\n",
" query = \"\"\"\n",
" Select course_id, sub.index As index, module_id, chapter_name, exact_count_distinct(user_id) As tried_problem\n",
" From\n",
" (SELECT p.course_id As course_id, p.user_id As user_id, c2.index As index, \n",
" c2.module_id As module_id, c2.name As chapter_name\n",
" FROM [{0}.problem_analysis] p\n",
" Left Join [{0}.course_axis] c1\n",
" on p.problem_url_name = c1.url_name\n",
" Left Join [{0}.course_axis] c2\n",
" On c1.chapter_mid = c2.module_id) sub\n",
" Group By course_id, index, module_id, chapter_name\n",
" Order By index\"\"\".format(course_id)\n",
" tried_problem = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
" tried_problem = tried_problem[tried_problem.tried_problem > 20]\n",
"\n",
" # query # students watched any video for each chapter of the course, exclude those with less than 20 views\n",
" query = \"\"\"\n",
" Select course_id, index, module_id, chapter_name, exact_count_distinct(username) As watched_video\n",
" From\n",
" (SELECT c1.course_id As course_id, v.username As username, c2.index As index, \n",
" c2.module_id As module_id, c2.name As chapter_name\n",
" FROM [{0}.video_stats_day] v\n",
" Left Join [{0}.course_axis] c1\n",
" on v.video_id = c1.url_name\n",
" Left Join [{0}.course_axis] c2\n",
" On c1.chapter_mid = c2.module_id) sub\n",
" Group By course_id, index, module_id, chapter_name\n",
" Order By index\"\"\".format(course_id)\n",
" watched_video = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
" watched_video = watched_video[watched_video.watched_video > 20]\n",
"\n",
" # query # students with any activity for each chapter of the course, excluding those with less than 20 active students\n",
" query = \"\"\"\n",
" Select sub.course_id As course_id, sub.module_id As module_id, \n",
" c.name As chapter_name, c.index As index, sub.nactive As nactive\n",
" From [{0}.course_axis] c\n",
" Join \n",
" (Select course_id As course_id, Regexp_replace(module_id,'i4x://', '') As module_id, \n",
" exact_count_distinct(student_id) As nactive\n",
" From [{0}.studentmodule]\n",
" Where module_type = 'chapter' \n",
" Group By course_id, module_id) sub\n",
" On sub.module_id = c.module_id\n",
" Order By index\"\"\".format(course_id)\n",
" nactive = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
" nactive = nactive[nactive.nactive > 20]\n",
" \n",
" # merge watched_video, tried_problem, nactive \n",
" module_activity = watched_video.merge(tried_problem, how='outer').merge(nactive, how='outer').fillna(0)\n",
" return module_activity[module_activity.chapter_name != 0].sort_values('index').set_index('chapter_name')\n",
" \n",
"module_activity = query_moduleActivity('UBCx__Climate101x__3T2015')\n",
"# create a list of course indices to make sure courses are ordered in the visualization\n",
"indices = module_activity.index"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def query_cs(course_id, indices): \n",
" \"\"\"\n",
" Given course_id (e.g. 'UBCx__Climate1x__1T2016'), \n",
" return a list of all the course items (graded_problem, self_test, video, assignment, chapter) \n",
" from course_axis table ordered by index.\n",
" IMPORTANT: Need to update course_axis in SPD1x first (delete items that belong to SPD2x and SPD3x)\n",
" \"\"\"\n",
" query = \"\"\"\n",
" SELECT\n",
" Case \n",
" When c1.category='problem' And c1.graded='true' Then 'graded_problem'\n",
" When c1.category='problem' And c1.graded!='true' Then 'self_test' \n",
" Else c1.category\n",
" End As category, c1.index As index, c1.name As name,\n",
" c1.url_name As url_name, c2.name As chapter\n",
" FROM [[{0}.course_axis] c1\n",
" Left Join [{0}.course_axis] c2\n",
" On c1.chapter_mid = c2.module_id\n",
" Where c1.category in ('video', 'problem', 'openassessment', 'chapter')\n",
" Order By c1.index\"\"\".format(course_id)\n",
"\n",
" structure = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
" structure = structure[(structure.name.isin(indices)) | (structure.chapter.isin(indices))]\n",
" \n",
" query = \"\"\"\n",
" Select problem_url_name, exact_count_distinct(item.answer_id) As num\n",
" From [{0}.problem_analysis]\n",
" Group By problem_url_name\"\"\".format(course_id)\n",
" nQuestions = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
" structure = structure.merge(nQuestions, left_on='url_name', right_on='problem_url_name', how='left')\\\n",
" .drop('problem_url_name', axis=1)\n",
" structure.num = structure.num.fillna(1)\n",
" return structure\n",
"\n",
"course_structure = query_cs('UBCx__Climate101x__3T2015', indices)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# count # of videos, graded_problems, self_test and assigments for each chapter and order te courses\n",
"cs_chapter = course_structure.groupby(['chapter', 'category']).num.sum().unstack('category')\\\n",
".reindex(indices).dropna(how='all')\n",
"\n",
"cols = []\n",
"# some courses don't have all the items\n",
"for col in ['video', 'graded_problem', 'self_test', 'openassessment']:\n",
" if col in cs_chapter.columns.values:\n",
" cols.append(col)\n",
"cs_chapter = cs_chapter[cols]\n",
"cs_chapter['chapter'] = np.nan\n",
"cs_chapter.fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"module_activity2 = query_moduleActivity('UBCx__China300_1x__1T2016')\n",
"# create a list of course indices to make sure courses are ordered in the visualization\n",
"indices2 = module_activity2.index\n",
"course_structure2 = query_cs('UBCx__China300_1x__1T2016', indices2)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# count # of videos, graded_problems, self_test and assigments for each chapter and order te courses\n",
"cs_chapter2 = course_structure2.groupby(['chapter', 'category']).num.sum().unstack('category')\\\n",
".reindex(indices2).dropna(how='all')\n",
"\n",
"cols = []\n",
"# some courses don't have all the items\n",
"for col in ['video', 'graded_problem', 'self_test', 'openassessment']:\n",
" if col in cs_chapter2.columns.values:\n",
" cols.append(col)\n",
"cs_chapter2 = cs_chapter2[cols]\n",
"cs_chapter2['chapter'] = np.nan\n",
"cs_chapter2.fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"module_activity = module_activity.reindex(cs_chapter.index)\n",
"fig = tls.make_subplots(rows=1, cols=2, print_grid=False, \n",
" subplot_titles=('UBCx/Climate101x/3T2015', 'UBCx/China300.1x/1T2016'))\n",
"\n",
"colors = {'video': 'rgb(202,178,214)', 'graded_problem': 'rgb(66,146,198)', \n",
" 'self_test': 'rgb(166,206,227)', 'openassessment': 'rgb(116,196,118)', 'chapter': 'rgb(0, 0, 0)'}\n",
"\n",
"for i in range(0, cs_chapter.shape[1]):\n",
" fig.append_trace(go.Bar(x=cs_chapter.index, y=cs_chapter.ix[:, i],\n",
" marker=dict(color=colors[cs_chapter.columns[i]]), name=cs_chapter.columns[i]), 1, 1)\n",
" \n",
"for i in range(0, cs_chapter2.shape[1]):\n",
" fig.append_trace(go.Bar(x=cs_chapter2.index, y=cs_chapter2.ix[:, i], \n",
" marker=dict(color=colors[cs_chapter.columns[i]]), name=cs_chapter.columns[i], showlegend=False), 1, 2)\n",
"\n",
"fig['layout']['yaxis1'].update(tickfont=dict(size=8), showgrid=False)\n",
"fig['layout']['yaxis2'].update(showticklabels=False, showgrid=False)\n",
"fig['layout']['xaxis1'].update(showgrid=False)\n",
"fig['layout']['xaxis2'].update(showgrid=False)\n",
"fig['layout']['legend'].update(x=1, y=0, traceorder='normal')\n",
"fig['layout'].update(height=380, width=850, margin=go.Margin(b=150, l=20, r=20, t=25), barmode='stack')\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### By order of course items"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def rolling_count(df):\n",
" df['block'] = (df['category'] != df['category'].shift(1)).astype(int).cumsum()\n",
" df['count'] = df.groupby('block').num.cumsum()\n",
" return df\n",
"# count # of times an item (graded_problem, self_test, video) appears consecutively\n",
"df = course_structure.fillna(method='bfill')\n",
"df = df.groupby('chapter').apply(rolling_count)\n",
"idx = df.groupby(['chapter', 'block'])['count'].transform(max) == df['count']\n",
"df = df.ix[idx]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting \n",
"data = [go.Bar(x=df['count'], y=['UBCx/Climate101x/3T2015']*len(df), \n",
" orientation='h', hoverinfo='y',\n",
" marker=dict(color=df.category.apply(lambda x: colors[x]).values))]\n",
"layout = go.Layout(\n",
" xaxis=dict(tickfont=dict(size=8), showgrid=False),\n",
"# yaxis=dict(showticklabels=False),\n",
" barmode='stack', \n",
" width=850,\n",
" height=50,\n",
" margin=go.Margin(b=15, t=0, l=180)\n",
")\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df2 = course_structure2.fillna(method='bfill')\n",
"df2 = df2.groupby('chapter').apply(rolling_count)\n",
"idx2 = df2.groupby(['chapter', 'block'])['count'].transform(max) == df2['count']\n",
"df2 = df2.ix[idx2]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting \n",
"data = [go.Bar(x=df2['count'], y=['UBCx/China300.1x/1T2016']*len(df2), \n",
" orientation='h', hoverinfo='y',\n",
" marker=dict(color=df2.category.apply(lambda x: colors[x]).values))]\n",
"layout = go.Layout(\n",
" xaxis=dict(tickfont=dict(size=8), showgrid=False),\n",
"# yaxis=dict(showticklabels=False),\n",
" barmode='stack', \n",
" width=850,\n",
" height=50,\n",
" margin=go.Margin(b=15, t=0, l=180)\n",
")\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Course structure vs. students' activity"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def course_item(course_id):\n",
" \"\"\"\n",
" Given course_id, query students' event for video, graded_problem, \n",
" self_test, openassessment and chapter from the studentmodule table\n",
" => the numbers are slightly different from thosed queried from person_item and video_stats_day\n",
" \"\"\"\n",
" query = \"\"\"\n",
" SELECT sub.module_id As item_id, c.index As index, name, category, nstudents\n",
" FROM [ubcxdata:{0}.course_axis] c\n",
" Join \n",
" (Select Regexp_replace(module_id,'i4x://', '') As module_id, exact_count_distinct(student_id) As nstudents\n",
" From [ubcxdata:{0}.studentmodule]\n",
" Where module_type In ('openassessment', 'chapter')\n",
" Group By module_id) sub\n",
" On sub.module_id = c.module_id\n",
" Order By index\"\"\".format(course_id)\n",
" chapter_assign = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
"\n",
" query = \"\"\"\n",
" Select problem_url_name as item_id, index, name, \n",
" Case When graded='true' Then 'graded_problem' Else 'self_test' End As category,\n",
" exact_count_distinct(user_id) As nstudents\n",
" From [{0}.problem_analysis] p\n",
" Join [{0}.course_axis] c\n",
" On p.problem_url_name= c.url_name\n",
" Group By item_id, index, name, category\n",
" Order By index\"\"\".format(course_id)\n",
" nproblems = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False)\n",
"\n",
" query = \"\"\"\n",
" Select video_id as item_id, index_video as index, name, 'video' As category, videos_viewed As nstudents\n",
" From [{0}.video_stats]\n",
" Where videos_viewed > 20\"\"\".format(course_id)\n",
" nvideos = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False).dropna()\n",
"\n",
" courseItem = pd.concat([chapter_assign, nproblems, nvideos]).sort_values('index')\n",
" courseItem = courseItem[courseItem.nstudents > 20].reset_index(drop=True)\n",
" return courseItem\n",
"\n",
"courseItem = course_item('UBCx__Climate101x__3T2015')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# make it center in the middle\n",
"trace1 = go.Bar(x=courseItem.index+1, y=courseItem.nstudents, hoverinfo='text',\n",
" text=['{0}:
nstudents: {1}'.format(name.encode('utf-8'), value) \n",
" for name, value in zip(courseItem.name, courseItem.nstudents)],\n",
" marker=dict(color=courseItem.category.apply(lambda x: colors[x]).values))\n",
"trace2 = go.Bar(x=courseItem.index+1, y=-courseItem.nstudents, hoverinfo='none',\n",
" marker=dict(color=courseItem.category.apply(lambda x: colors[x]).values))\n",
"data = [trace1, trace2]\n",
"layout = go.Layout(barmode='relative', title='UBCx/Climate101x/3T2015', \n",
" xaxis=dict(showticklabels=False, title='course_structure'), \n",
" yaxis=dict(showticklabels=False, showgrid=False, title='nstudents', zeroline=False), \n",
" height=300, width=850, margin=go.Margin(t=25, b=15), showlegend=False)\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"courseItem2 = course_item('UBCx__China300_1x__1T2016')\n",
"# make it center in the middle\n",
"trace1 = go.Bar(x=courseItem2.index+1, y=courseItem2.nstudents, hoverinfo='text',\n",
" text=['{0}:
nstudents: {1}'.format(name.encode('utf-8'), value) \n",
" for name, value in zip(courseItem2.name, courseItem2.nstudents)],\n",
" marker=dict(color=courseItem2.category.apply(lambda x: colors[x]).values))\n",
"trace2 = go.Bar(x=courseItem2.index+1, y=-courseItem2.nstudents, hoverinfo='none',\n",
" marker=dict(color=courseItem2.category.apply(lambda x: colors[x]).values))\n",
"data = [trace1, trace2]\n",
"layout = go.Layout(barmode='relative', title='UBCx/China300.1x/1T2016', \n",
" xaxis=dict(showticklabels=False, title='course_structure'), \n",
" yaxis=dict(showticklabels=False, showgrid=False, title='nstudents', zeroline=False), \n",
" height=300, width=850, margin=go.Margin(t=25, b=15), showlegend=False)\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Density map illustrating pattern of video and problem activity for the involved"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def query_pc(course_id): \n",
" \"\"\"\n",
" Given course_id(e.g. 'UBCx__Marketing1x__3T2015'), query and calculate ndays_act, sum_dt, nforum_posts, \n",
" nvideos_watched, nproblems_attempted, pct_video_watched, pct_problem_attempted for sampled students,\n",
" also return total_videos and total_problems.\n",
" \"\"\"\n",
" query = \"\"\"\n",
" Select pc.user_id As user_id, pc.course_id As course_id, pc.mode As mode, pc.grade As grade, \n",
" pc.ndays_act As ndays_act, pc.sum_dt As sum_dt, pc.nforum_posts As nforum_posts,\n",
" v.videos_watched As nvideos_watched, p.problems_attempted As nproblems_attempted\n",
" From [{0}.person_course] pc\n",
" Left Join\n",
" (SELECT username, exact_count_distinct(video_id) As videos_watched \n",
" FROM [{0}.video_stats_day]\n",
" Group By username) v\n",
" on pc.username = v.username\n",
" Left Join \n",
" (Select user_id, exact_count_distinct(item.answer_id) As problems_attempted\n",
" From [{0}.problem_analysis]\n",
" Group By user_id) p\n",
" On pc.user_id = p.user_id\n",
" Where pc.sum_dt > 0\"\"\".format(course_id)\n",
" df = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False).fillna(0)\n",
"\n",
" # course_axis includes items not accessible to the students, \n",
" # => total_videos/total_problems are maximum number of videos/problems students accessed \n",
" # if smaller than the number from course_axis then use the latter one\n",
" total_videos = min(df.nvideos_watched.max(), cs_chapter.video.sum())\n",
" df['pct_video_watched'] = df.nvideos_watched / total_videos\n",
" \n",
" total_problems = min(df.nproblems_attempted.max(), \n",
" cs_chapter.graded_problem.sum() + cs_chapter.self_test.sum() if 'self_test' in cs_chapter.columns \n",
" else cs_chapter.graded_problem.sum())\n",
" df['pct_problem_attempted'] = df.nproblems_attempted / total_problems\n",
" \n",
" \n",
" return total_videos, total_problems, df"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"total_videos, total_problems, pc = query_pc('UBCx__Climate101x__3T2015')\n",
"pc[['pct_video_watched', 'pct_problem_attempted']] = \\\n",
"pc[['pct_video_watched', 'pct_problem_attempted']].applymap(lambda x: \"{0:.2f}\".format(x * 100))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# pc for the involved\n",
"pc_activity = pc[pc.sum_dt>900].copy()\n",
"# density map\n",
"trace1 = go.Histogram2d(x=pc_activity.pct_video_watched, y=pc_activity.pct_problem_attempted,\n",
" histnorm='probability',\n",
" autobinx=False,\n",
" xbins=dict(start=0, end=100, size=10),\n",
" autobiny=False,\n",
" ybins=dict(start=0, end=100, size=10),\n",
" colorscale=[[0, 'rgb(8,81,156)'], [1/1000, 'rgb(8,81,156)'], [1/100, 'rgb(242,211,56)'], \n",
" [1/10, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)']],\n",
" showscale=False)\n",
"trace2 = go.Histogram2d(x=pc_activity.pct_video_watched, y=pc_activity.pct_problem_attempted,\n",
" histnorm='probability',\n",
" autobinx=False,\n",
" xbins=dict(start=0, end=100, size=25),\n",
" autobiny=False,\n",
" ybins=dict(start=0, end=100, size=25),\n",
" colorscale=[[0, 'rgb(8,81,156)'], [1/1000, 'rgb(8,81,156)'], [1/100, 'rgb(242,211,56)'], \n",
" [1/10, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)']],\n",
" showscale=False)\n",
"trace3 = go.Histogram2d(x=pc_activity.pct_video_watched, y=pc_activity.pct_problem_attempted,\n",
" histnorm='probability',\n",
" autobinx=False,\n",
" xbins=dict(start=0, end=100, size=10),\n",
" autobiny=False,\n",
" ybins=dict(start=0, end=100, size=10),\n",
" colorscale=[[0, 'rgb(8,81,156)'], [1/1000, 'rgb(8,81,156)'], [1/100, 'rgb(242,211,56)'], \n",
" [1/10, 'rgb(242,143,56)'], [1, 'rgb(217,30,30)']],\n",
" colorbar=dict(thickness=20), zsmooth='fast')\n",
"\n",
"fig = tls.make_subplots(rows=1, cols=3, print_grid=False)\n",
"fig.append_trace(trace1, 1, 1)\n",
"fig.append_trace(trace2, 1, 2)\n",
"fig.append_trace(trace3, 1, 3)\n",
"\n",
"fig['layout']['xaxis1'].update(title='% videos (total:{0})'.format(int(total_videos)))\n",
"fig['layout']['xaxis2'].update(title='% videos (total:{0})'.format(int(total_videos)))\n",
"fig['layout']['xaxis3'].update(title='% videos (total:{0})'.format(int(total_videos)))\n",
"fig['layout']['yaxis1'].update(title='% problems (total:{0})'.format(int(total_problems)))\n",
"fig['layout']['yaxis2'].update(showticklabels=False)\n",
"fig['layout']['yaxis3'].update(showticklabels=False)\n",
"fig['layout'].update(\n",
" width=850, height=350, \n",
" title='UBCx/Climate101x/3T2015',\n",
" margin=go.Margin(l=40, t=40)\n",
")\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Students engagement: multi_courses\n",
"### Bar graph"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# only change course_list (for all courses on edX), mooc_list (Moocs) and pe_list (professional education) if needed\n",
"# list for all the courses\n",
"course_list = [\n",
" 'UBCx__Marketing1x__3T2015',\n",
" 'UBCx__Climate1x__2T2016', \n",
" 'UBCx__SPD1x__2T2016',\n",
" 'UBCx__SPD2x__2T2016',\n",
" 'UBCx__SPD3x__2T2016',\n",
" 'UBCx__UseGen_1x__1T2016',\n",
" 'UBCx__UseGen_2x__1T2016',\n",
" 'UBCx__China300_1x__1T2016',\n",
" 'UBCx__China300_2x__1T2016',\n",
" 'UBCx__Forest222x__1T2015',\n",
" 'UBCx__IndEdu200x__3T2015',\n",
" 'UBCx__Water201x_2__2T2015',\n",
" 'UBCx__CW1_1x__1T2016',\n",
" 'UBCx__CW1_2x__1T2016',\n",
" 'UBCx__Phot1x__1T2016',\n",
" 'UBCx__ITSx__2T2015'\n",
"]\n",
"indices = [course.replace('__', '/').replace('_', '.') for course in course_list]\n",
"indices[indices.index('UBCx/Water201x.2/2T2015')] = 'UBCx/Water201x_2/2T2015'"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false,
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"source": [
"# dictionaries to store total # of videos/problems for each course in the course_list\n",
"total_videos = {}\n",
"total_problems = {}\n",
"def query_pcs_learned(course_list = course_list):\n",
" \"\"\"\n",
" Iterate over each course in the course_list, query and calculate ndays_act, sum_dt, nforum_posts, \n",
" nvideos_watched, nproblems_attempted, pct_video_watched, pct_problem_attempted for involved students.\n",
" Update total_videos and total_problems\n",
" \"\"\"\n",
" dfs = []\n",
" for i in range(len(course_list)):\n",
" query = \"\"\"\n",
" Select pc.user_id As user_id, pc.course_id As course_id, pc.mode As mode, pc.grade As grade, \n",
" pc.ndays_act As ndays_act, pc.sum_dt / 3600 As sum_dt, pc.nforum_posts As nforum_posts,\n",
" v.videos_watched As nvideos_watched, p.problems_attempted As nproblems_attempted\n",
" From [{0}.person_course] pc\n",
" Left Join\n",
" (SELECT username, Count(Distinct video_id) As videos_watched \n",
" FROM [{0}.video_stats_day]\n",
" Group By username) v\n",
" on pc.username = v.username\n",
" Left Join \n",
" (Select user_id, Count(Distinct item.answer_id) As problems_attempted\n",
" From [{0}.problem_analysis]\n",
" Group By user_id) p\n",
" On pc.user_id = p.user_id\n",
" Where pc.sum_dt > 900\"\"\".format(course_list[i])\n",
" df = pd.io.gbq.read_gbq(query, project_id='ubcxdata', verbose=False).fillna(0)\n",
"\n",
" course_id = indices[i]\n",
" total_videos[course_id] = df.nvideos_watched.quantile(0.975)\n",
" df['pct_video_watched'] = df.nvideos_watched / total_videos[course_id]\n",
" total_problems[course_id] = df.nproblems_attempted.quantile(0.975)\n",
" df['pct_problem_attempted'] = df.nproblems_attempted / total_problems[course_id]\n",
" dfs.append(df)\n",
" pcs_learned = pd.concat(dfs)\n",
" return pcs_learned"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false,
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"source": [
"cols = ['ndays_act', 'sum_dt', 'pct_video_watched', 'pct_problem_attempted', 'nforum_posts']\n",
"# for the involved\n",
"pcs_learned = query_pcs_learned()\n",
"# aggregate nforum_posts => % students posted, others by median\n",
"pcs_learned_agg = pcs_learned.groupby('course_id').agg({'ndays_act': np.median, 'sum_dt': np.median, \n",
" 'pct_video_watched': np.median, 'pct_problem_attempted': np.median, \n",
" 'nforum_posts': lambda s: (s > 0).sum() / len(s)})\n",
"# order the courses as needed\n",
"pcs_learned_agg = pcs_learned_agg[cols].reindex(indices)\n",
"# for the passed\n",
"pcs_passed = pcs_learned[pcs_learned.grade >= 0.5]\n",
"pcs_passed_agg = pcs_passed.groupby('course_id').agg({'ndays_act': np.median, 'sum_dt': np.median, \n",
" 'pct_video_watched': np.median, 'pct_problem_attempted': np.median, \n",
" 'nforum_posts': lambda s: (s > 0).sum() / len(s)})\n",
"pcs_passed_agg = pcs_passed_agg[cols].reindex(indices)\n",
"\n",
"# normalize along the columns for plotting heatmap\n",
"pcs_passed_norm = pcs_passed_agg.divide(pcs_passed_agg.max(axis=0), axis=1)\n",
"pcs_learned_norm = pcs_learned_agg.divide(pcs_learned_agg.max(axis=0), axis=1)\n",
"# formatting\n",
"pcs_passed_agg.sum_dt = pcs_passed_agg.sum_dt.round(2)\n",
"pcs_passed_agg[['pct_video_watched', 'pct_problem_attempted', 'nforum_posts']] = \\\n",
"pcs_passed_agg[['pct_video_watched', 'pct_problem_attempted', 'nforum_posts']].applymap(lambda x: \"{0:.2f}\".format(x * 100))\n",
"pcs_learned_agg.sum_dt = pcs_learned_agg.sum_dt.round(2)\n",
"pcs_learned_agg[['pct_video_watched', 'pct_problem_attempted', 'nforum_posts']] = \\\n",
"pcs_learned_agg[['pct_video_watched', 'pct_problem_attempted', 'nforum_posts']].applymap(lambda x: \"{0:.2f}\".format(x * 100))"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"color_passed = ['rgb(166,206,227)', 'rgb(253,191,111)', 'rgb(178,223,138)', 'rgb(251,154,153)', 'rgb(202,178,214)']\n",
"color_learned = ['rgb(31,120,180)', 'rgb(255,127,0)', 'rgb(51,160,44)', 'rgb(227,26,28)', 'rgb(106,61,154)']\n",
"fig = tls.make_subplots(rows=1, cols=pcs_passed_agg.shape[1], horizontal_spacing=0.02, print_grid=False)\n",
"for i in range(0, pcs_passed_agg.shape[1]):\n",
" fig.append_trace(go.Bar(x=pcs_passed_agg.ix[:, i], y=pcs_passed_agg.index, marker=dict(color=color_passed[i]),\n",
" name='passed', orientation='h', showlegend=False), 1, i+1)\n",
" fig.append_trace(go.Bar(x=pcs_learned_agg.ix[:, i], y=pcs_learned_agg.index, marker=dict(color=color_learned[i]),\n",
" name='learned', orientation='h', showlegend=False), 1, i+1)\n",
"\n",
"showticklabels = True\n",
"# fig['layout']['yaxis1'].update(autorange='reversed')\n",
"for i in range(1, pcs_passed_agg.shape[1]+1):\n",
" if i >= 2:\n",
" showticklabels = False\n",
" fig['layout']['xaxis%s' % i].update(title=names[i-1], titlefont=dict(size=10), \n",
" tickfont=dict(size=8), showgrid=False)\n",
" fig['layout']['yaxis%s' % i].update(showticklabels=showticklabels, showgrid=False, autorange='reversed')\n",
"\n",
"fig['layout'].update(barmode='overlay', height=80+25*len(pcs_learned_agg),\n",
" width=800, margin=go.Margin(t=40, b=40, l=180, r=0), title=\"Engagement for the learned and the passed\")\n",
"py.iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Heatmap"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# normalized value for plotting heatmap, unnormalized for hoverinfo\n",
"names = ['median days active', 'median sum_dt (H)', \n",
" 'median % videos', 'median % problems', '% students posted']\n",
"trace1 = go.Heatmap(\n",
" z=pcs_learned_norm.values,\n",
" x=names,\n",
" y=pcs_learned_norm.index,\n",
" text = pcs_learned_agg.values,\n",
" hoverinfo='x+text',\n",
" showscale=False,\n",
" colorscale=[[0.0, 'rgb(224,243,248)'], [1.0, 'rgb(51,160,44)']])\n",
"\n",
"\n",
"trace2 = go.Heatmap(\n",
" z=pcs_passed_norm.values,\n",
" x=names,\n",
" y=pcs_passed_norm.index,\n",
" text = pcs_passed_agg.values,\n",
" hoverinfo='x+text',\n",
" showscale=False,\n",
" colorscale=[[0.0, 'rgb(224,243,248)'], [1.0, 'rgb(43,130,189)']])\n",
"\n",
"fig = tls.make_subplots(rows=1, cols=2, print_grid=False,\n",
" subplot_titles=('Engagement for the involved', \n",
" 'Engagement for the passed'))\n",
"fig.append_trace(trace1, 1, 1)\n",
"fig.append_trace(trace2, 1, 2)\n",
"fig['layout']['yaxis1'].update(autorange='reversed')\n",
"fig['layout']['yaxis2'].update(showticklabels=False, autorange='reversed')\n",
"\n",
"fig['layout'].update(\n",
" width=750, height=140+30*len(pcs_learned_agg),\n",
" margin=go.Margin(l=180, b=120, t=20)\n",
")\n",
"\n",
"py.iplot(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": true,
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
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"toc": {
"toc_cell": true,
"toc_number_sections": true,
"toc_section_display": "none",
"toc_threshold": 6,
"toc_window_display": true
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