ASSISTments2012-2013 Data Analysis

Data Description

Column Description

Field	Annotation
problem_log_id	Unique ID of the logged actions.
skill	Skill name associated with the problem (different skills are in different rows).
problem_id	The ID of the problem
user_id	The ID of the student doing the problem
assignment_id	Two different assignments can have the same sequence id
assistment_id	The ID of the ASSISTment
start_time	Timestamp when the problem starts
end_time	Timestamp when the problem ends
problem_type	choose_1，algebra，fill_in or open_response
original	Main problem or Scaffolding problem
correct	Correct on the fisrt attempt or Incorrect on the first attempt, or asked for help
bottom_hint	Whether or not the student asks for all hints
hint_count	Number of hints on this problem
actions	Every action on this problem
attempt_count	Number of student attempts on this problem
ms_first_response	The time in milliseconds for the student’s first response
tutor_mode	tutor, test mode, pre-test, or post-test
sequence_id	The content id of the problem set
student_class_id	The class ID
position	Assignment position on the class assignments page
type	This is the type of the head section of the problem set
base_sequence_id	This is to account for if a sequence has been copied
skill_id	ID of the skill associated with the problem (different skills are in different rows)
teacher_id	The ID of the teacher who assigned the problem
school_id	The ID of the school where the problem was assigned
overlap_time	The time in milliseconds for the student’s overlap time
template_id	The template ID of the ASSISTments
answer_id	The answer ID for multi-choice questions
answer_text	The answer text for fill-in questions
first_action	The type of first action: attempt or ask for a hint
problemlog_id	Unique ID of the logged actions
Average_confidence(FRUSTRATED)	Predicted Frustration of student for the problem
Average_confidence(CONFUSED)	Predicted Confusion of student for the problem
Average_confidence(CONCENTRATING)	Predicted Engaged Concentration of student for the problem
Average_confidence(BORED)	Predicted Boredom of student for the problem

import pandas as pd

import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objs as go

path = "2012-2013-data-with-predictions-4-final.csv"

data = pd.read_csv(path, encoding = "ISO-8859-15",low_memory=False)

Record Examples

pd.set_option('display.max_columns', 500)
data.head()

	problem_log_id	skill	problem_id	user_id	assignment_id	assistment_id	start_time	end_time	problem_type	original	correct	bottom_hint	hint_count	actions	attempt_count	ms_first_response	tutor_mode	sequence_id	student_class_id	position	type	base_sequence_id	skill_id	teacher_id	school_id	overlap_time	template_id	answer_id	answer_text	first_action	problemlogid	Average_confidence(FRUSTRATED)	Average_confidence(CONFUSED)	Average_confidence(CONCENTRATING)	Average_confidence(BORED)
0	137792159	NaN	557460	61394	565736	341511	2012-09-28 15:11:27	2012-09-28 15:11:36.856	choose_1	1	1.0	0.0	0	--- \n- - start\n - 1348859487561\n - "95952...	1	9852	tutor	55482	23643	4	LinearSection	55482	NaN	53472	5048.0	9852	341511	NaN	she	0	137792159	0.361323	0.0	0.336529	0.000000
1	138083797	Rounding	365981	61394	573819	204043	2012-10-09 11:01:52	2012-10-09 11:02:13.182	algebra	1	1.0	0.0	0	--- \n- - start\n - 1349794912269\n - "62459...	1	21175	tutor	34221	22967	5	LinearSection	34221	54.0	47424	5048.0	21175	204043	NaN	74.29	0	138083797	0.361323	0.0	0.766925	0.000000
2	142332619	Multiplication and Division Integers	426415	61394	734130	247525	2013-03-07 10:53:20	2013-03-07 10:53:28.661	algebra	1	0.0	0.0	0	--- \n- - start\n - 1362671600405\n - "74107...	1	8645	tutor	39601	22967	58	LinearSection	39601	279.0	47424	5048.0	8645	247525	NaN	00	0	142332619	0.361323	0.0	0.766925	0.442968
3	145939397	Proportion	86686	61394	821352	48081	2013-08-20 19:54:56	2013-08-20 19:55:21.753	algebra	1	1.0	0.0	0	--- \n- - start\n - 1377042896503\n - "73630...	1	25728	tutor	6912	26303	21	MasterySection	6912	79.0	47424	5048.0	25728	46362	NaN	3.8	0	145939397	0.775000	0.0	0.766925	0.912281
4	137111284	NaN	399669	76592	557216	227869	2012-09-10 17:20:10	2012-09-10 17:24:56.579	choose_1	1	1.0	0.0	0	--- \n- - start\n - 1347312010563\n - "69479...	1	286578	tutor	37143	21696	3	LinearSection	37143	NaN	152676	7561.0	286578	227869	NaN	C (wr - 1)(wr + 1)	0	137111284	0.361323	0.0	0.766925	0.000000

---

General features

data.describe()

	problem_log_id	problem_id	user_id	assignment_id	assistment_id	original	correct	bottom_hint	hint_count	attempt_count	ms_first_response	sequence_id	student_class_id	position	base_sequence_id	skill_id	teacher_id	school_id	overlap_time	template_id	answer_id	first_action	problemlogid	Average_confidence(FRUSTRATED)	Average_confidence(CONFUSED)	Average_confidence(CONCENTRATING)	Average_confidence(BORED)
count	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.062922e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	2.711813e+06	6.123270e+06	6.123113e+06	6.123270e+06	6.123270e+06	8.275000e+03	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06	6.123270e+06
mean	1.414932e+08	3.685675e+05	1.770492e+05	6.773074e+05	2.202825e+05	9.504296e-01	6.768206e-01	1.200497e-01	3.373479e-01	1.339212e+00	4.873469e+04	6.689567e+04	2.342511e+04	7.402669e+01	6.214174e+04	1.932575e+02	1.210437e+05	6.925225e+03	4.907237e+04	2.088952e+05	4.324879e+05	6.151860e-02	1.414932e+08	3.894586e-01	4.479487e-02	6.823843e-01	2.567723e-01
std	2.693733e+06	2.195421e+05	3.172431e+04	9.425983e+04	1.393519e+05	2.170557e-01	4.674909e-01	3.250197e-01	9.851956e-01	1.056276e+00	2.673557e+05	5.933111e+04	1.612341e+03	3.697118e+02	5.687449e+04	1.303155e+02	4.978645e+04	3.314489e+03	2.884992e+05	1.458227e+05	3.534885e+05	2.635170e-01	2.693733e+06	1.027662e-01	1.924793e-01	1.713734e-01	2.862460e-01
min	1.368431e+08	1.000000e+00	2.142100e+04	1.814560e+05	5.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-6.767000e+03	2.000000e+00	1.139300e+04	0.000000e+00	2.000000e+00	1.000000e+00	1.143600e+04	1.000000e+00	-6.767000e+03	5.000000e+00	1.000000e+00	0.000000e+00	1.368431e+08	3.613230e-01	0.000000e+00	1.707320e-01	0.000000e+00
25%	1.391705e+08	1.284030e+05	1.719780e+05	5.863570e+05	6.883725e+04	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	9.436000e+03	1.266200e+04	2.251800e+04	4.000000e+00	1.189800e+04	6.500000e+01	7.305500e+04	5.260000e+03	9.468000e+03	5.259000e+04	1.060495e+05	0.000000e+00	1.391705e+08	3.613230e-01	0.000000e+00	7.669250e-01	0.000000e+00
50%	1.414916e+08	4.168130e+05	1.791670e+05	6.785645e+05	2.399180e+05	1.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	2.233100e+04	4.614100e+04	2.314400e+04	1.200000e+01	4.493100e+04	2.770000e+02	1.285010e+05	5.978000e+03	2.241500e+04	2.395460e+05	3.442820e+05	0.000000e+00	1.414916e+08	3.613230e-01	0.000000e+00	7.669250e-01	2.214840e-01
75%	1.438272e+08	5.644030e+05	1.972510e+05	7.672320e+05	3.466830e+05	1.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	5.486500e+04	9.901300e+04	2.460300e+04	3.800000e+01	8.711200e+04	3.100000e+02	1.562940e+05	9.394000e+03	5.505400e+04	3.434800e+05	7.385615e+05	0.000000e+00	1.438272e+08	3.613230e-01	0.000000e+00	7.669250e-01	4.429680e-01
max	1.462357e+08	7.671430e+05	2.282130e+05	8.330540e+05	4.925890e+05	1.000000e+00	1.000000e+00	1.000000e+00	1.400000e+01	2.900000e+01	3.450552e+08	2.084530e+05	2.738600e+04	8.533000e+03	2.084530e+05	1.641000e+03	2.205230e+05	1.242800e+04	3.452775e+08	4.925890e+05	1.184706e+06	2.000000e+00	1.462357e+08	8.671330e-01	1.000000e+00	7.669250e-01	1.000000e+00

print("The number of records: "+ str(len(data['problem_log_id'].unique())))

The number of records: 6123270

print('Part of missing values for every column')
print(data.isnull().sum() / len(data))

Part of missing values for every column
problem_log_id                       0.000000
skill                                0.570478
problem_id                           0.000000
user_id                              0.000000
assignment_id                        0.000000
assistment_id                        0.000000
start_time                           0.000000
end_time                             0.000000
problem_type                         0.000000
original                             0.000000
correct                              0.000000
bottom_hint                          0.009856
hint_count                           0.000000
actions                              0.000000
attempt_count                        0.000000
ms_first_response                    0.000000
tutor_mode                           0.000000
sequence_id                          0.000000
student_class_id                     0.000000
position                             0.000000
type                                 0.000000
base_sequence_id                     0.000000
skill_id                             0.557130
teacher_id                           0.000000
school_id                            0.000026
overlap_time                         0.000000
template_id                          0.000000
answer_id                            0.998649
answer_text                          0.056561
first_action                         0.000000
problemlogid                         0.000000
Average_confidence(FRUSTRATED)       0.000000
Average_confidence(CONFUSED)         0.000000
Average_confidence(CONCENTRATING)    0.000000
Average_confidence(BORED)            0.000000
dtype: float64

len(data.user_id.unique())

46674

ds = data['user_id'].value_counts().reset_index()

ds.columns = [
    'user_id',
    'count'
]

ds['user_id'] = ds['user_id'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(40)

fig = px.bar(
    ds,
    x = 'count',
    y = 'user_id',
    orientation='h',
    title='Top 40 students by number of actions'
)

fig.show('svg')

ds = data['user_id'].value_counts().reset_index()

ds.columns = [
    'user_id',
    'count'
]

ds = ds.sort_values('user_id')

fig = px.histogram(
    ds,
    x = 'user_id',
    y = 'count',
    title = 'User action distribution'
)

fig.show('svg')

ds = data['problem_id'].value_counts().reset_index()

ds.columns = [
    'problem_id',
    'count'
]

ds['problem_id'] = ds['problem_id'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(40)

fig = px.bar(
    ds,
    x = 'count',
    y = 'problem_id',
    orientation = 'h',
    title = 'Top 40 useful problem_ids'
)

fig.show('svg')

ds = data['problem_id'].value_counts().reset_index()

ds.columns = [
    'problem_id',
    'count'
]

ds = ds.sort_values('problem_id')

fig = px.histogram(
    ds,
    x='problem_id',
    y='count',
    title='problem_id action distribution'
)

fig.show('svg')

ds = data['correct'].value_counts().reset_index()

ds.columns = [
    'correct',
    'percent'
]

ds['percent'] /= len(data)
ds = ds.sort_values(['correct'])

fig = px.pie(
    ds,
    names = ['0', '0.25','0.375','0.5','0,6','0.625','0.65','0.75','0.85','0.875','0.95','0.975','1'],
    values = 'percent',
    title = 'Percent of correct answers'
)

fig.show('svg')
"Minor note:we also have Essay questions that teachers can grade. If this value is say 0.25, that means the teacher gave it a 1 out of 4."

'Minor note:we also have Essay questions that teachers can grade. If this value is say 0.25, that means the teacher gave it a 1 out of 4.'

---

Sort by sequence id

ds = data['sequence_id'].value_counts().reset_index()

ds.columns = [
    'sequence_id',
    'count'
]

ds['sequence_id'] = ds['sequence_id'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(40)

fig = px.bar(
    ds,
    x = 'count',
    y = 'sequence_id',
    orientation = 'h',
    title = 'Top 40 useful sequence_ids'
)

fig.show('svg')

sort by problem types

ds = data['problem_type'].value_counts().reset_index()

ds.columns = [
    'problem_type',
    'percent'
]

ds['percent'] /= len(data)
ds = ds.sort_values(['percent'])

fig = px.pie(
    ds,
    names = 'problem_type',
    values = 'percent',
    title = 'Percent of Problem Types',
)

fig.show('svg')

ds = ds.sort_values(['percent']).tail(6)

fig = make_subplots(rows=3, cols=2)

traces = [
    go.Bar(
        x = ['wrong', 'right'],
        y = [
            len(data[(data['problem_type'] == item) & (data['correct'] == 0)]),
            len(data[(data['problem_type'] == item) & (data['correct'] == 1)])
        ],
        name = 'Type: ' + str(item),
        text = [
            str(round(100*len(data[(data['problem_type'] == item)&(data['correct'] == 0)])/len(data[data['problem_type'] == item]),2)) + '%',
            str(round(100*len(data[(data['problem_type'] == item)&(data['correct'] == 1)])/len(data[data['problem_type'] == item]),2)) + '%'
        ],
        textposition = 'auto'
    ) for item in ds['problem_type'].unique().tolist()
]

for i in range(len(traces)):
    fig.append_trace(
        traces[i],
        (i //2) + 1,
        (i % 2) + 1
    )

fig.update_layout(
    title_text = 'Percent of correct answers for every problem type',
)

fig.show('svg')

---

Sort by schools

len(data['school_id'].unique())

662

ds = data['school_id'].value_counts().reset_index()

ds.columns = [
    'school_id',
    'percent'
]

ds['percent'] /= len(data)
ds = ds.sort_values(['percent'])

fig = px.pie(
    ds,
    names = 'school_id',
    values = 'percent',
    title = 'Percent of schools',
)

fig.show('svg')

ds = data['school_id'].value_counts().reset_index()

ds.columns = [
    'school_id',
    'count'
]

ds['school_id'] = ds['school_id'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(20)

fig = px.bar(
    ds,
    x = 'count',
    y = 'school_id',
    orientation = 'h',
    title = 'Top 20 useful school_ids'
)

fig.show('svg')

---

Sort by attemp counts

ds = data['attempt_count'].value_counts().reset_index()

ds.columns = [
    'attempt_count',
    'count'
]

ds['attempt_count'] = ds['attempt_count'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(40)

fig = px.bar(
    ds,
    x = 'count',
    y = 'attempt_count',
    orientation = 'h',
    title = 'Top 20 often attempt count'
)

fig.show('svg')

---

Sort by skills

ds = data['skill_id'].dropna() # There are less NaNs in 'skill_id' column than 'skill_name' column.
ds = ds.value_counts().reset_index()

ds.columns = [
    'skill_id',
    'count'
]

ds['skill_id'] = ds['skill_id'].astype(str) + '-'
ds = ds.sort_values(['count']).tail(40)

fig = px.bar(
    ds,
    x = 'count',
    y = 'skill_id',
    orientation = 'h',
    title = 'Top 40 useful skill_id'
)

fig.show('svg')