Example of Operation Count

[1]:

# import openLA as la
import OpenLA as la

[2]:

course_info, event_stream = la.start_analysis(files_dir="dataset_sample", course_id="A")

Before conversion

[3]:

event_stream.df

[3]:
userid contentsid operationname pageno marker memo_length devicecode eventtime
0 A_U1 C1 PREV 10 NaN 0 tablet 2018-04-09 10:57:15
1 A_U1 C1 PREV 9 NaN 0 tablet 2018-04-09 11:00:59
2 A_U1 C1 PREV 8 NaN 0 tablet 2018-04-09 11:03:31
3 A_U1 C1 PREV 30 NaN 0 tablet 2018-04-10 10:14:12
4 A_U1 C1 PREV 29 NaN 0 tablet 2018-04-10 10:27:24
... ... ... ... ... ... ... ... ...
263279 A_U99 C8 NEXT 3 NaN 0 pc 2018-06-05 16:16:18
263280 A_U99 C8 ADD MARKER 4 difficult 0 pc 2018-06-05 16:18:34
263281 A_U99 C8 NEXT 4 NaN 0 pc 2018-06-05 16:19:24
263282 A_U99 C8 NEXT 5 NaN 0 pc 2018-06-05 16:20:45
263283 A_U99 C8 PREV 6 NaN 0 pc 2018-06-05 16:21:03

263284 rows × 8 columns

After conversion: the operation count in each contents

[4]:

operation_count = la.convert_into_operation_count(event_stream=event_stream,
                                                  user_id=course_info.user_id()[:2],
                                                  contents_id=course_info.contents_id()[:2],
                                                  operation_name=["OPEN", "CLOSE", "ADD MARKER", "ADD MEMO", "ADD BOOKMARK"],
                                                  separate_marker_type=False)

[5]:

operation_count.df # related to users "A_U1" and "A_U10", and contents "C1" and "C2"

[5]:
userid contentsid OPEN CLOSE ADD MARKER ADD MEMO ADD BOOKMARK
0 A_U1 C1 9 8.0 2.0 0.0 3.0
1 A_U1 C2 2 0.0 0.0 0.0 2.0
2 A_U10 C1 2 2.0 8.0 1.0 1.0
3 A_U10 C2 1 1.0 0.0 0.0 0.0

Save the data to CSV file

[6]:

operation_count.to_csv(save_file="data.csv")

Aggregate information

[7]:

# Number of users in the data
operation_count.num_users()

[7]:

2

[8]:

# User ids in the data
operation_count.user_id()

[8]:

['A_U1', 'A_U10']

[9]:

# Contents ids in the data
operation_count.contents_id()

[9]:

['C1', 'C2']

[10]:

# Operation names in the data
operation_count.operation_name()

[10]:

['OPEN', 'CLOSE', 'ADD MARKER', 'ADD MEMO', 'ADD BOOKMARK']

[11]:

# Number of selected operation logs
operation_count.operation_count(operation_name="ADD MARKER",
                                user_id="A_U1",
                                contents_id="C1")

[11]:

2.0

[12]:

operation_count.operation_count(operation_name=["ADD MARKER", "ADD BOOKMARK"],
                                user_id="A_U1",
                                contents_id="C1")

[12]:

{'ADD MARKER': 2.0, 'ADD BOOKMARK': 3.0}

Visualize information

The average operation count of all users in all contents.

[16]:

la.visualize_operation_count_bar(operation_count,
                                 user_id=None,
                                 contents_id=None,
                                 operation_name=None,
                                 calculate_type="average",
                                 xlabel=None,
                                 ylabel=None,
                                 ax=None,
                                 figsize=(20, 10),
                                 fontsize=20,
                                 save_file=None)

[16]:

<matplotlib.axes._subplots.AxesSubplot at 0x168c961b860>
../_images/jupyter_examples_example_operation_count_18_1.png