openla-feature-representation 0.1.3

A Python module that adds features to OpenLA data to make it easier to use for ML

openla-feature-representation: generate features for EventStream data

Introduction

openla-feature-representation is an open-source Python module that generates features from OpenLA EventStream data, to make the data easier to use for ML.

Installation

This module module is available on PyPI and it can be installed using pip as follows:

pip install openla-feature-representation

Downloading the model

For the E2Vec class, you will need the openla-feature-representation-fastText_1min.bin model . Feel free to dowload it from the OpenLA models download site.

Usage of the E2Vec class

First, import the openla_feature_representation package with an arbitrary name, here lafr.

import openla_feature_representation as lafr

Initializing the class

This is the constructor:

e2Vec = lafr.E2Vec(ftmodel_path, input_csv_dir_path, course_id)

• ftmodel_path is the path to a fastText language model trained for this task
• input_csv_dir_path is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory (e.g. 'A-2023')

After getting your own e2Vec object, all methods the class provides can be used on it.

Generate sentences for the event log

The fastText model uses an artificial language to express event log entries as sentences. This is how you can generate them:

sentences = e2Vec.generate_sentences(
    sentences_dir_path=sentences_dir_path,
    eventstream_file_path=eventstream_file_path,
    input_csv_dir_path=input_csv_dir_path,
    course_id=course_id,
)

If you need to select or filter a time span:

sentences = e2Vec.generate_sentences(
    sentences_dir_path=sentence_path,
    use_timespan=True,
    start_minute=0,
    total_minutes=90,
    eventstream_file_path=eventstream_file_path,
    input_csv_dir_path=input_csv_dir_path,
    course_id=course_id,
)

• sentences_dir_path is the path to the directory where you want the sentence files to be written
• eventstream_file_path is the path to the event stream csv file
• input_csv_dir_path is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory
• use_timespan if True, the args below will be used to extract a timespan from the data (optional)
• start_minute is the minute in the data the sentence generation should start (optional)
• total_minutes is the number of minutes worth of sentences that should be generated (optional)

This function saves the sentences to a text file and returns a path to it.

Vectorize the sentences

This function returns a pandas DataFrame with the vectors generated from the sentences.

user_vectors = e2Vec.vectorize_sentences(sentences_file_path)

• sentences_file_path is the path to the sentence files generated in the previous step

Concatenation

The class has a function to concatenate vectors by time (minutes) or weeks.

This will concatenate the vectors in 10-minute spans.

vectors = e2Vec.concatenate_vectors(
    sentences_dir_path=sentences_dir_path,
    eventstream_file_path=eventstream_file_path,
    input_csv_dir_path=eduData,
    course_id=course_id,
    start_minute=0,
    total_minutes=10,
)

This will concatenate the vectors by the week or lesson.

vectors = e2Vec.concatenate_vectors(
    sentences_dir_path=sentences_dir_path,
    eventstream_file_path=eventstream_file_path,
    input_csv_dir_path=eduData,
    course_id=course_id,
    by_weeks=True,
    start_minute=0,
)

• sentences_dir_path is the path to the sentence files generated in the previous step
• eventstream_file_path is the path to the event stream csv file
• input_csv_dir_path is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory
• by_weeks concatenates vectors by week if True (by time by default)
• start_minute is the minute in the data the sentence generation should start (optional)
• total_minutes is the number of minutes worth of sentences that should be generated each time (optional)

Usage of the ALP class

ALP (Active Learner Point) is a set of metrics that take BookRoll (ebook) and Moodle activity per lecture into account: attendance, report submissions, course views, slide views, adding markers or memos, and other actions.

from openla_feature_representation import Alp
alp = Alp(course_id="114")

• course_id is a string to identify files for the course to analyze within the Dataset directory

The Alp class constructor above makes three DataFrames available as properties of the returned Alp object:

• features_df: aggregated totals of how many times each user took any of the relevant actions for each lecture
• alp_df: the features above replaced by a number from 0 to 5 following the criteria below
• alp_df_normalized: same as above, only the 0 to 5 numbers are normalized between 0 and 1

Criteria for the ALP 0-to-5 scale

value	description
5:	Top 10%, or attending the lecture, or submitting a report
4:	Top 20%
3:	Top 30%, or being late to the lecture, or submitting late
2:	Top 40%
1:	Top 50%
0:	Bottom 50%, or not attending, or not submitting

Examples for the class's methods and instance variables

alp.features_df       # These are the aggregated features
alp.alp_df            # These are the ALP 0-to-5 values
alp.alp_df_normalized # These are the normalized values

# The following will write CSV files for the relevant DataFrame
# Paths and filenames can be specified, but there are default
# filenames and the paths default to the present directory
alp.write_features_csv()
alp.write_alp_csv()
alp.write_alp_normalized_csv()

Datasets for OpenLA

This module uses data in the same or a similar format as OpenLA. Please refer to the OpenLA documentation for further information.

Datasets for the Alp class must be placed in the "Dataset/" directory relative to the Python script they are called from.